diff --git a/KR_Project.ipynb b/KR_Project.ipynb
new file mode 100644
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+++ b/KR_Project.ipynb
@@ -0,0 +1,998 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e760bf9f-4601-4521-9f4c-c2be10101c18",
+   "metadata": {},
+   "source": [
+    "# The impact of large-scale forcing scales on clouds.\n",
+    "\n",
+    "# Feel free to add your insights to my approach!\n",
+    "# It is fun to learn new things!\n",
+    "# May 22, 2024"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "24f314f4-f123-48bb-a970-f099f67bdb5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import packages\n",
+    "\n",
+    "from datetime import datetime\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import xwrf\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ad7270ee-57fb-4ddd-b1d7-45448e3df3a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# The Large Scale Forcing Scale in Simulation 6 is 75 km\n",
+    "# The Large Scale Forcing Scale in Simulation 7 is 150 km\n",
+    "# The Large Scale Forcing Scale in Simulation 8 is 300 km"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "019e1abf-b3fe-4c3a-9f71-7356fac5ed2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR 1: PROJ: proj_create_from_database: Open of /opt/conda/share/proj failed\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "  min-width: 300px;\n",
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+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
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+       "  margin-left: 2px;\n",
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+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
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+       ".xr-section-summary {\n",
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+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
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+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
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+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
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+       "  grid-column: 1;\n",
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+       "  color: var(--xr-font-color3);\n",
+       "}\n",
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+       ".xr-array-data {\n",
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+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
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+       ".xr-var-list,\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 72GB\n",
+       "Dimensions:         (Time: 91, z: 226, z_stag: 227, y: 250, x: 250,\n",
+       "                     x_stag: 251, y_stag: 251)\n",
+       "Coordinates:\n",
+       "    XTIME           (Time) datetime64[ns] 728B ...\n",
+       "  * Time            (Time) datetime64[ns] 728B 2019-04-04T12:00:00 ... 2019-0...\n",
+       "  * y_stag          (y_stag) float64 2kB -1.25e+04 -1.24e+04 ... 1.25e+04\n",
+       "  * y               (y) float64 2kB -1.245e+04 -1.235e+04 ... 1.245e+04\n",
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+       "  * x               (x) float64 2kB -1.245e+04 -1.235e+04 ... 1.245e+04\n",
+       "Dimensions without coordinates: z, z_stag\n",
+       "Data variables: (12/179)\n",
+       "    Times           (Time) |S19 2kB b&#x27;2019-04-04_12:00:00&#x27; ... b&#x27;2019-04-05_0...\n",
+       "    CST_CLDLOW      (Time) float32 364B ...\n",
+       "    CST_CLDTOT      (Time) float32 364B ...\n",
+       "    CST_LWP         (Time) float32 364B ...\n",
+       "    CST_IWP         (Time) float32 364B ...\n",
+       "    CST_PRECW       (Time) float32 364B ...\n",
+       "    ...              ...\n",
+       "    CSV_IWC         (Time, z, y, x) float32 5GB ...\n",
+       "    CSV_CLDFRAC     (Time, z, y, x) float32 5GB ...\n",
+       "    CSS_LWP         (Time, y, x) float32 23MB ...\n",
+       "    CSS_IWP         (Time, y, x) float32 23MB ...\n",
+       "    CSS_CLDTOT      (Time, y, x) float32 23MB ...\n",
+       "    CSS_CLDLOW      (Time, y, x) float32 23MB ...\n",
+       "Attributes: (12/96)\n",
+       "    TITLE:                                  OUTPUT FROM WRF V3.8.1 MODEL\n",
+       "    START_DATE:                            2019-04-04_12:00:00\n",
+       "    WEST-EAST_GRID_DIMENSION:              251\n",
+       "    SOUTH-NORTH_GRID_DIMENSION:            251\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:             227\n",
+       "    DX:                                    100.0\n",
+       "    ...                                    ...\n",
+       "    config_aerosol:                        NA\n",
+       "    config_forecast_time:                  15.0 h\n",
+       "    config_boundary_method:                Periodic\n",
+       "    config_microphysics:                   Thompson (mp_physics=8)\n",
+       "    config_nickname:                       runlas20190404v1msda2d150km\n",
+       "    simulation_origin_host:                cumulus-login2.ccs.ornl.gov</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-db7bf5ab-c5f5-4fa8-a41b-3e27df38449e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-db7bf5ab-c5f5-4fa8-a41b-3e27df38449e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Time</span>: 91</li><li><span>z</span>: 226</li><li><span>z_stag</span>: 227</li><li><span class='xr-has-index'>y</span>: 250</li><li><span class='xr-has-index'>x</span>: 250</li><li><span class='xr-has-index'>x_stag</span>: 251</li><li><span class='xr-has-index'>y_stag</span>: 251</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ae07cd0f-55e9-468e-a5fc-4d84af3811a2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae07cd0f-55e9-468e-a5fc-4d84af3811a2' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>XTIME</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5d4e7729-d045-49ab-9573-e9b891e0e80e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d4e7729-d045-49ab-9573-e9b891e0e80e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a983e8f3-d0df-4c90-861b-f8906f98412e' class='xr-var-data-in' type='checkbox'><label for='data-a983e8f3-d0df-4c90-861b-f8906f98412e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>minutes since 2019-04-04 12:00:00</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Time</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-04-04T12:00:00 ... 2019-04-...</div><input id='attrs-57fc153d-6671-4c23-9b5a-8d132ad73f80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57fc153d-6671-4c23-9b5a-8d132ad73f80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16fad84e-9378-4e41-97d2-0cc268afb053' class='xr-var-data-in' type='checkbox'><label for='data-16fad84e-9378-4e41-97d2-0cc268afb053' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-04-04T12:00:00.000000000&#x27;, &#x27;2019-04-04T12:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T12:20:00.000000000&#x27;, &#x27;2019-04-04T12:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T12:40:00.000000000&#x27;, &#x27;2019-04-04T12:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T13:00:00.000000000&#x27;, &#x27;2019-04-04T13:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T13:20:00.000000000&#x27;, &#x27;2019-04-04T13:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T13:40:00.000000000&#x27;, &#x27;2019-04-04T13:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T14:00:00.000000000&#x27;, &#x27;2019-04-04T14:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T14:20:00.000000000&#x27;, &#x27;2019-04-04T14:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T14:40:00.000000000&#x27;, &#x27;2019-04-04T14:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T15:00:00.000000000&#x27;, &#x27;2019-04-04T15:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T15:20:00.000000000&#x27;, &#x27;2019-04-04T15:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T15:40:00.000000000&#x27;, &#x27;2019-04-04T15:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T16:00:00.000000000&#x27;, &#x27;2019-04-04T16:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T16:20:00.000000000&#x27;, &#x27;2019-04-04T16:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T16:40:00.000000000&#x27;, &#x27;2019-04-04T16:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T17:00:00.000000000&#x27;, &#x27;2019-04-04T17:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T17:20:00.000000000&#x27;, &#x27;2019-04-04T17:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T17:40:00.000000000&#x27;, &#x27;2019-04-04T17:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T18:00:00.000000000&#x27;, &#x27;2019-04-04T18:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T18:20:00.000000000&#x27;, &#x27;2019-04-04T18:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T18:40:00.000000000&#x27;, &#x27;2019-04-04T18:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T19:00:00.000000000&#x27;, &#x27;2019-04-04T19:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T19:20:00.000000000&#x27;, &#x27;2019-04-04T19:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T19:40:00.000000000&#x27;, &#x27;2019-04-04T19:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T20:00:00.000000000&#x27;, &#x27;2019-04-04T20:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T20:20:00.000000000&#x27;, &#x27;2019-04-04T20:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T20:40:00.000000000&#x27;, &#x27;2019-04-04T20:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T21:00:00.000000000&#x27;, &#x27;2019-04-04T21:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T21:20:00.000000000&#x27;, &#x27;2019-04-04T21:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T21:40:00.000000000&#x27;, &#x27;2019-04-04T21:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T22:00:00.000000000&#x27;, &#x27;2019-04-04T22:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T22:20:00.000000000&#x27;, &#x27;2019-04-04T22:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T22:40:00.000000000&#x27;, &#x27;2019-04-04T22:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T23:00:00.000000000&#x27;, &#x27;2019-04-04T23:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T23:20:00.000000000&#x27;, &#x27;2019-04-04T23:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T23:40:00.000000000&#x27;, &#x27;2019-04-04T23:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T00:00:00.000000000&#x27;, &#x27;2019-04-05T00:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T00:20:00.000000000&#x27;, &#x27;2019-04-05T00:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T00:40:00.000000000&#x27;, &#x27;2019-04-05T00:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T01:00:00.000000000&#x27;, &#x27;2019-04-05T01:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T01:20:00.000000000&#x27;, &#x27;2019-04-05T01:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T01:40:00.000000000&#x27;, &#x27;2019-04-05T01:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T02:00:00.000000000&#x27;, &#x27;2019-04-05T02:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T02:20:00.000000000&#x27;, &#x27;2019-04-05T02:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T02:40:00.000000000&#x27;, &#x27;2019-04-05T02:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T03:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_stag</span></div><div class='xr-var-dims'>(y_stag)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.25e+04 -1.24e+04 ... 1.25e+04</div><input id='attrs-bb8b6bbe-2587-4b87-9ffe-1c7eb60e3faa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb8b6bbe-2587-4b87-9ffe-1c7eb60e3faa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e747a4d-793d-4e07-a237-701a4c24bbeb' class='xr-var-data-in' type='checkbox'><label for='data-1e747a4d-793d-4e07-a237-701a4c24bbeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([-12500., -12400., -12300., ...,  12300.,  12400.,  12500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.245e+04 -1.235e+04 ... 1.245e+04</div><input id='attrs-954a66e0-a9b2-4d36-9edc-695621d77be5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-954a66e0-a9b2-4d36-9edc-695621d77be5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e73a53ba-e2e9-4bf2-a7f6-89c3c83b965b' class='xr-var-data-in' type='checkbox'><label for='data-e73a53ba-e2e9-4bf2-a7f6-89c3c83b965b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-12450., -12350., -12250., ...,  12250.,  12350.,  12450.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x_stag</span></div><div class='xr-var-dims'>(x_stag)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.25e+04 -1.24e+04 ... 1.25e+04</div><input id='attrs-b123b6cb-a6e1-4d26-9085-6dee45dd0008' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b123b6cb-a6e1-4d26-9085-6dee45dd0008' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62f59f2b-947d-407b-bb2b-43f29d8ee4b2' class='xr-var-data-in' type='checkbox'><label for='data-62f59f2b-947d-407b-bb2b-43f29d8ee4b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([-12500., -12400., -12300., ...,  12300.,  12400.,  12500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.245e+04 -1.235e+04 ... 1.245e+04</div><input id='attrs-64f0f10c-b883-46a0-b9a3-6f7de452a21c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64f0f10c-b883-46a0-b9a3-6f7de452a21c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12a54297-9c72-45a4-b682-371af2190810' class='xr-var-data-in' type='checkbox'><label for='data-12a54297-9c72-45a4-b682-371af2190810' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-12450., -12350., -12250., ...,  12250.,  12350.,  12450.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd5668ca-6afa-4f06-93a2-b27e583ac21e' class='xr-section-summary-in' type='checkbox'  ><label for='section-fd5668ca-6afa-4f06-93a2-b27e583ac21e' class='xr-section-summary' >Data variables: <span>(179)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Times</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>|S19</div><div class='xr-var-preview xr-preview'>b&#x27;2019-04-04_12:00:00&#x27; ... b&#x27;201...</div><input id='attrs-dcdc39ae-b89d-45c6-b672-b31e985887ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcdc39ae-b89d-45c6-b672-b31e985887ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-796d35a4-ff86-426b-936d-78a49e44a763' class='xr-var-data-in' type='checkbox'><label for='data-796d35a4-ff86-426b-936d-78a49e44a763' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([b&#x27;2019-04-04_12:00:00&#x27;, b&#x27;2019-04-04_12:10:00&#x27;, b&#x27;2019-04-04_12:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_12:30:00&#x27;, b&#x27;2019-04-04_12:40:00&#x27;, b&#x27;2019-04-04_12:50:00&#x27;,\n",
+       "       b&#x27;2019-04-04_13:00:00&#x27;, b&#x27;2019-04-04_13:10:00&#x27;, b&#x27;2019-04-04_13:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_13:30:00&#x27;, b&#x27;2019-04-04_13:40:00&#x27;, b&#x27;2019-04-04_13:50:00&#x27;,\n",
+       "       b&#x27;2019-04-04_14:00:00&#x27;, b&#x27;2019-04-04_14:10:00&#x27;, b&#x27;2019-04-04_14:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_14:30:00&#x27;, b&#x27;2019-04-04_14:40:00&#x27;, b&#x27;2019-04-04_14:50:00&#x27;,\n",
+       "       b&#x27;2019-04-04_15:00:00&#x27;, b&#x27;2019-04-04_15:10:00&#x27;, b&#x27;2019-04-04_15:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_15:30:00&#x27;, b&#x27;2019-04-04_15:40:00&#x27;, b&#x27;2019-04-04_15:50:00&#x27;,\n",
+       "       b&#x27;2019-04-04_16:00:00&#x27;, b&#x27;2019-04-04_16:10:00&#x27;, b&#x27;2019-04-04_16:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_16:30:00&#x27;, b&#x27;2019-04-04_16:40:00&#x27;, b&#x27;2019-04-04_16:50:00&#x27;,\n",
+       "       b&#x27;2019-04-04_17:00:00&#x27;, b&#x27;2019-04-04_17:10:00&#x27;, b&#x27;2019-04-04_17:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_17:30:00&#x27;, b&#x27;2019-04-04_17:40:00&#x27;, b&#x27;2019-04-04_17:50:00&#x27;,\n",
+       "       b&#x27;2019-04-04_18:00:00&#x27;, b&#x27;2019-04-04_18:10:00&#x27;, b&#x27;2019-04-04_18:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_18:30:00&#x27;, b&#x27;2019-04-04_18:40:00&#x27;, b&#x27;2019-04-04_18:50:00&#x27;,\n",
+       "       b&#x27;2019-04-04_19:00:00&#x27;, b&#x27;2019-04-04_19:10:00&#x27;, b&#x27;2019-04-04_19:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_19:30:00&#x27;, b&#x27;2019-04-04_19:40:00&#x27;, b&#x27;2019-04-04_19:50:00&#x27;,\n",
+       "       b&#x27;2019-04-04_20:00:00&#x27;, b&#x27;2019-04-04_20:10:00&#x27;, b&#x27;2019-04-04_20:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_20:30:00&#x27;, b&#x27;2019-04-04_20:40:00&#x27;, b&#x27;2019-04-04_20:50:00&#x27;,\n",
+       "       b&#x27;2019-04-04_21:00:00&#x27;, b&#x27;2019-04-04_21:10:00&#x27;, b&#x27;2019-04-04_21:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_21:30:00&#x27;, b&#x27;2019-04-04_21:40:00&#x27;, b&#x27;2019-04-04_21:50:00&#x27;,\n",
+       "       b&#x27;2019-04-04_22:00:00&#x27;, b&#x27;2019-04-04_22:10:00&#x27;, b&#x27;2019-04-04_22:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_22:30:00&#x27;, b&#x27;2019-04-04_22:40:00&#x27;, b&#x27;2019-04-04_22:50:00&#x27;,\n",
+       "       b&#x27;2019-04-04_23:00:00&#x27;, b&#x27;2019-04-04_23:10:00&#x27;, b&#x27;2019-04-04_23:20:00&#x27;,\n",
+       "       b&#x27;2019-04-04_23:30:00&#x27;, b&#x27;2019-04-04_23:40:00&#x27;, b&#x27;2019-04-04_23:50:00&#x27;,\n",
+       "       b&#x27;2019-04-05_00:00:00&#x27;, b&#x27;2019-04-05_00:10:00&#x27;, b&#x27;2019-04-05_00:20:00&#x27;,\n",
+       "       b&#x27;2019-04-05_00:30:00&#x27;, b&#x27;2019-04-05_00:40:00&#x27;, b&#x27;2019-04-05_00:50:00&#x27;,\n",
+       "       b&#x27;2019-04-05_01:00:00&#x27;, b&#x27;2019-04-05_01:10:00&#x27;, b&#x27;2019-04-05_01:20:00&#x27;,\n",
+       "       b&#x27;2019-04-05_01:30:00&#x27;, b&#x27;2019-04-05_01:40:00&#x27;, b&#x27;2019-04-05_01:50:00&#x27;,\n",
+       "       b&#x27;2019-04-05_02:00:00&#x27;, b&#x27;2019-04-05_02:10:00&#x27;, b&#x27;2019-04-05_02:20:00&#x27;,\n",
+       "       b&#x27;2019-04-05_02:30:00&#x27;, b&#x27;2019-04-05_02:40:00&#x27;, b&#x27;2019-04-05_02:50:00&#x27;,\n",
+       "       b&#x27;2019-04-05_03:00:00&#x27;], dtype=&#x27;|S19&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_CLDLOW</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9f74a507-4007-41e3-bcdc-ed0d862fd198' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f74a507-4007-41e3-bcdc-ed0d862fd198' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ced8ad5-8bdf-4186-8fa7-83b7830e4c2a' class='xr-var-data-in' type='checkbox'><label for='data-0ced8ad5-8bdf-4186-8fa7-83b7830e4c2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Fractional low-cloud cover (&lt;5 km)</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_CLDTOT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-042e5ae8-faa5-4416-b387-6220139517ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-042e5ae8-faa5-4416-b387-6220139517ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3887f167-4392-4c86-be84-e90512ee9015' class='xr-var-data-in' type='checkbox'><label for='data-3887f167-4392-4c86-be84-e90512ee9015' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Fractional cloud cover</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_LWP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9474f105-4fa8-497b-b632-c45dfcca8a7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9474f105-4fa8-497b-b632-c45dfcca8a7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a963880-301e-4f80-9b32-7c56e8eab882' class='xr-var-data-in' type='checkbox'><label for='data-3a963880-301e-4f80-9b32-7c56e8eab882' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Vertical integrated liquid water path (based on ql)</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_IWP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7b5c05ef-5e86-4f9c-9a80-9bf0bdafc38d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b5c05ef-5e86-4f9c-9a80-9bf0bdafc38d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24bb5296-01c3-46b9-bf7e-b87a61a16d52' class='xr-var-data-in' type='checkbox'><label for='data-24bb5296-01c3-46b9-bf7e-b87a61a16d52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Vertical integrated ice water path (based on qf)</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_PRECW</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6ddb1b91-0c19-48f0-9f19-729ee733ac85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ddb1b91-0c19-48f0-9f19-729ee733ac85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68d8a240-2ef2-4c86-9282-55318f454ee8' class='xr-var-data-in' type='checkbox'><label for='data-68d8a240-2ef2-4c86-9282-55318f454ee8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Vertical integrated water vapor</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_TKE</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32f05100-cbb3-411e-b94d-d383d084c604' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32f05100-cbb3-411e-b94d-d383d084c604' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5579ddc0-9c3d-49d2-81e4-31472943a557' class='xr-var-data-in' type='checkbox'><label for='data-5579ddc0-9c3d-49d2-81e4-31472943a557' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Vertical integrated TKE</dd><dt><span>units :</span></dt><dd>kg/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_TSAIR</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ef9ae1e6-f9b7-48e5-8206-438fbf1c3d7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef9ae1e6-f9b7-48e5-8206-438fbf1c3d7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12dcce8f-4335-44d0-92dc-76d32b9bd04c' class='xr-var-data-in' type='checkbox'><label for='data-12dcce8f-4335-44d0-92dc-76d32b9bd04c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_PS</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-68c6d5db-42da-4ab9-a15a-6fc45ba89367' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68c6d5db-42da-4ab9-a15a-6fc45ba89367' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af6bf863-6bc9-4abd-a569-8d197bf290a6' class='xr-var-data-in' type='checkbox'><label for='data-af6bf863-6bc9-4abd-a569-8d197bf290a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_PRECT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-929c5cea-fd0e-431e-a17d-16dab41300b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-929c5cea-fd0e-431e-a17d-16dab41300b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a2b4134-e866-447b-ab6b-dacccde3635a' class='xr-var-data-in' type='checkbox'><label for='data-9a2b4134-e866-447b-ab6b-dacccde3635a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Total precipitation at surface</dd><dt><span>units :</span></dt><dd>mm/sec</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_SH</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7fbc4ca5-df07-4ab1-8979-ab6c0c847199' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fbc4ca5-df07-4ab1-8979-ab6c0c847199' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01493ae5-266f-44f7-b04d-f2c3b46a613e' class='xr-var-data-in' type='checkbox'><label for='data-01493ae5-266f-44f7-b04d-f2c3b46a613e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface sensible heat flux</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_LH</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-431107a3-a5e6-477b-97c8-6a870a2e11e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-431107a3-a5e6-477b-97c8-6a870a2e11e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1d45683-9d2b-40bb-9c43-a048c557be81' class='xr-var-data-in' type='checkbox'><label for='data-e1d45683-9d2b-40bb-9c43-a048c557be81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface latent heat flux</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FSNTC</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4e8558bb-66f2-445a-b3eb-eecba1073e87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e8558bb-66f2-445a-b3eb-eecba1073e87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be2497b4-8f84-466b-8171-c433f8370a26' class='xr-var-data-in' type='checkbox'><label for='data-be2497b4-8f84-466b-8171-c433f8370a26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TOA SW net upward clear-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FSNT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ce279aa1-028d-42c6-b4df-fb4fc03099d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce279aa1-028d-42c6-b4df-fb4fc03099d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1849a9b-c363-4352-bea2-ac7794ce19a1' class='xr-var-data-in' type='checkbox'><label for='data-f1849a9b-c363-4352-bea2-ac7794ce19a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TOA SW net upward total-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FLNTC</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f2516f7-e3f8-407a-ba90-ac0b43e83481' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f2516f7-e3f8-407a-ba90-ac0b43e83481' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a614461-89d0-498b-a967-2e9c24d059e0' class='xr-var-data-in' type='checkbox'><label for='data-5a614461-89d0-498b-a967-2e9c24d059e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TOA LW (net) upward clear-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FLNT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-68742307-e0a6-470f-b0e2-45012606fd73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68742307-e0a6-470f-b0e2-45012606fd73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96695f44-67c9-4f3c-ac3a-44bc47c7c3ef' class='xr-var-data-in' type='checkbox'><label for='data-96695f44-67c9-4f3c-ac3a-44bc47c7c3ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TOA LW (net) upward total-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FSNSC</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7eca4f02-4dfa-4b6f-b869-8835b80ea274' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7eca4f02-4dfa-4b6f-b869-8835b80ea274' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f00a3bb1-32ac-4282-96eb-8e1f541c3a42' class='xr-var-data-in' type='checkbox'><label for='data-f00a3bb1-32ac-4282-96eb-8e1f541c3a42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface SW net upward clear-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FSNS</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f91009f3-ef5a-4bdd-a1b7-0da2372a1c39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f91009f3-ef5a-4bdd-a1b7-0da2372a1c39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3e526f4-d3b6-412c-a76b-b06795e73204' class='xr-var-data-in' type='checkbox'><label for='data-c3e526f4-d3b6-412c-a76b-b06795e73204' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface SW net upward total-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FLNSC</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d8784c72-c303-44e7-9e7f-9c023f3b3705' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8784c72-c303-44e7-9e7f-9c023f3b3705' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ded8988-f49f-423d-8a89-d89b215bd55e' class='xr-var-data-in' type='checkbox'><label for='data-6ded8988-f49f-423d-8a89-d89b215bd55e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface LW net upward clear-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FLNS</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a8091992-ca10-4746-b032-63f081801b05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8091992-ca10-4746-b032-63f081801b05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4ea27e5-e48a-4c3f-bb2e-f1019e2544a4' class='xr-var-data-in' type='checkbox'><label for='data-c4ea27e5-e48a-4c3f-bb2e-f1019e2544a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface LW net upward total-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_SWINC</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0a2693b-712f-4bba-b147-cd02a72d7c31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0a2693b-712f-4bba-b147-cd02a72d7c31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65af23f4-af8c-4849-b789-58b2205104cf' class='xr-var-data-in' type='checkbox'><label for='data-65af23f4-af8c-4849-b789-58b2205104cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TOA solar insolation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_TSK</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6a074785-4e84-4f4c-93bd-ba54c6af23da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a074785-4e84-4f4c-93bd-ba54c6af23da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-168529ff-f72a-40fd-89a7-d8276ac26ace' class='xr-var-data-in' type='checkbox'><label for='data-168529ff-f72a-40fd-89a7-d8276ac26ace' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface skin temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_UST</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01bcf2eb-c6ec-488e-9809-630da7fdb421' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01bcf2eb-c6ec-488e-9809-630da7fdb421' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2768c0a-3f0d-4e51-8038-1980609f03b7' class='xr-var-data-in' type='checkbox'><label for='data-b2768c0a-3f0d-4e51-8038-1980609f03b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface friction velocity</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_Z</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cad07477-8e7e-4086-9644-6be109de451a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cad07477-8e7e-4086-9644-6be109de451a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0eaafd71-5713-40be-957e-8344f3ab18b6' class='xr-var-data-in' type='checkbox'><label for='data-0eaafd71-5713-40be-957e-8344f3ab18b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Half level height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_Z8W</span></div><div class='xr-var-dims'>(Time, z_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-98d1efb2-0fe2-4fc9-9196-3a34d1fecbc4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98d1efb2-0fe2-4fc9-9196-3a34d1fecbc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-191ea23b-9f27-4ead-89d4-e7248eb3ee12' class='xr-var-data-in' type='checkbox'><label for='data-191ea23b-9f27-4ead-89d4-e7248eb3ee12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Full level height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[20657 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_DZ8W</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-744b913d-cd8f-4ab1-927e-d62bef375e13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-744b913d-cd8f-4ab1-927e-d62bef375e13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac7d2543-f78b-48ee-8a31-44b355976a39' class='xr-var-data-in' type='checkbox'><label for='data-ac7d2543-f78b-48ee-8a31-44b355976a39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dz at full level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_U</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-354d1455-1ef9-46ae-9a7a-3bf84b4e76e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-354d1455-1ef9-46ae-9a7a-3bf84b4e76e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a19f745-9228-47c8-b2c0-93b95f0e7815' class='xr-var-data-in' type='checkbox'><label for='data-4a19f745-9228-47c8-b2c0-93b95f0e7815' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Zonal wind</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_V</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3041e5f1-be2f-4489-a966-b2d5f3322d59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3041e5f1-be2f-4489-a966-b2d5f3322d59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ed6fdd0-6a70-4b9b-a343-a0b9ffb87c69' class='xr-var-data-in' type='checkbox'><label for='data-1ed6fdd0-6a70-4b9b-a343-a0b9ffb87c69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Meridional wind</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W</span></div><div class='xr-var-dims'>(Time, z_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-900e1524-6ae0-4e9b-a75c-e8c6cd9d6c56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-900e1524-6ae0-4e9b-a75c-e8c6cd9d6c56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68e17e38-9103-40ea-b91d-64b37579ebe6' class='xr-var-data-in' type='checkbox'><label for='data-68e17e38-9103-40ea-b91d-64b37579ebe6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Vertical motion</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[20657 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_P</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e964da9b-7036-465b-bca0-475c9def966d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e964da9b-7036-465b-bca0-475c9def966d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62518fde-a880-4f84-9b9c-7bbe7d20988b' class='xr-var-data-in' type='checkbox'><label for='data-62518fde-a880-4f84-9b9c-7bbe7d20988b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TH</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-97203ac6-24b0-4c94-9b04-ec84d24c4daf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97203ac6-24b0-4c94-9b04-ec84d24c4daf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f91c6ad8-694b-418e-8416-d0a3ea4dbba8' class='xr-var-data-in' type='checkbox'><label for='data-f91c6ad8-694b-418e-8416-d0a3ea4dbba8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Potential temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THV</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-562e13ea-87ea-4342-b73c-118566abb020' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-562e13ea-87ea-4342-b73c-118566abb020' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7dfd1fc-df10-4ba5-8636-fb10625252d8' class='xr-var-data-in' type='checkbox'><label for='data-e7dfd1fc-df10-4ba5-8636-fb10625252d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Virtual potential temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THL</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-237e676f-d08e-4a0e-a199-0c6e783a6e44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-237e676f-d08e-4a0e-a199-0c6e783a6e44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4aa99e7-4e96-4f18-bdfc-f53649f24411' class='xr-var-data-in' type='checkbox'><label for='data-a4aa99e7-4e96-4f18-bdfc-f53649f24411' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water potential temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QV</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3915e716-beaf-482e-b133-05acf527b817' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3915e716-beaf-482e-b133-05acf527b817' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-def3a4af-4da3-46a8-a5d1-8e7fac9caeb0' class='xr-var-data-in' type='checkbox'><label for='data-def3a4af-4da3-46a8-a5d1-8e7fac9caeb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Water vapor mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5656c2f8-6847-4b74-a5ea-8794f7d43e56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5656c2f8-6847-4b74-a5ea-8794f7d43e56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3805ed3-99f9-428c-a4b4-934a4dc131ea' class='xr-var-data-in' type='checkbox'><label for='data-b3805ed3-99f9-428c-a4b4-934a4dc131ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud water mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QI</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1aa55f0f-09b0-4fcf-946e-da38aa72fb85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1aa55f0f-09b0-4fcf-946e-da38aa72fb85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fa0aef4-2b35-4348-88d7-a833be158a82' class='xr-var-data-in' type='checkbox'><label for='data-9fa0aef4-2b35-4348-88d7-a833be158a82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Ice crystal (cloud ice) mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QL</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-297bf0cd-717c-4317-84cc-a415711dfd8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-297bf0cd-717c-4317-84cc-a415711dfd8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0966a5a-4d9d-4d05-967e-c28efb833705' class='xr-var-data-in' type='checkbox'><label for='data-e0966a5a-4d9d-4d05-967e-c28efb833705' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QF</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-75f90a80-53a6-484a-94cb-0d6681105472' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75f90a80-53a6-484a-94cb-0d6681105472' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71367636-f5e5-4cc7-b8dd-cb9ddc168ea3' class='xr-var-data-in' type='checkbox'><label for='data-71367636-f5e5-4cc7-b8dd-cb9ddc168ea3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Frozen water mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QT</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3858a413-525f-483f-a888-c35d836268b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3858a413-525f-483f-a888-c35d836268b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3f8a015-64e6-4ac8-8b84-e98ec17346ec' class='xr-var-data-in' type='checkbox'><label for='data-d3f8a015-64e6-4ac8-8b84-e98ec17346ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Total (vapor+liquid+frozen) water mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_LWC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d62c0404-43d8-45f0-940a-fcbf29154eef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d62c0404-43d8-45f0-940a-fcbf29154eef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acf0967a-eafb-48bc-8d2d-93b27fb05c7e' class='xr-var-data-in' type='checkbox'><label for='data-acf0967a-eafb-48bc-8d2d-93b27fb05c7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water content (based on ql)</dd><dt><span>units :</span></dt><dd>kg/m^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_IWC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-19209827-4ed7-4c76-90bf-b2a5dfe92d98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19209827-4ed7-4c76-90bf-b2a5dfe92d98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89f8ec10-be1e-4b62-94cc-e5cdd61fdd04' class='xr-var-data-in' type='checkbox'><label for='data-89f8ec10-be1e-4b62-94cc-e5cdd61fdd04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Ice water content (based on qf)</dd><dt><span>units :</span></dt><dd>kg/m^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SPEQV</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-453635e8-80c4-4980-ab2f-8259be534538' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-453635e8-80c4-4980-ab2f-8259be534538' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2720b9f-54e8-4a80-b4b7-6121a54c0049' class='xr-var-data-in' type='checkbox'><label for='data-b2720b9f-54e8-4a80-b4b7-6121a54c0049' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Specific humidity</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_A_CL</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c6f48fc3-4ede-4059-a00a-2872310e21d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6f48fc3-4ede-4059-a00a-2872310e21d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f40fa594-e07f-4389-8a35-43bbe74e8f89' class='xr-var-data-in' type='checkbox'><label for='data-f40fa594-e07f-4389-8a35-43bbe74e8f89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Fraction of cloudy grid points</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_RHO</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ac963b47-1fd2-4435-a822-35127cd7be7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac963b47-1fd2-4435-a822-35127cd7be7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9009b913-2e3a-4b75-b7bc-d17454384185' class='xr-var-data-in' type='checkbox'><label for='data-9009b913-2e3a-4b75-b7bc-d17454384185' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Density</dd><dt><span>units :</span></dt><dd>kg/m^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_U2</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0337c8a5-b479-4a4f-b986-7a9ea13652bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0337c8a5-b479-4a4f-b986-7a9ea13652bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f06d08be-257f-4fed-b784-c0a60fe851a2' class='xr-var-data-in' type='checkbox'><label for='data-f06d08be-257f-4fed-b784-c0a60fe851a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u_p^2</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_V2</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7a5a6727-e645-4f01-b19b-528de5167db2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a5a6727-e645-4f01-b19b-528de5167db2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4395c207-0451-4a37-bf0e-0800e4a36cd6' class='xr-var-data-in' type='checkbox'><label for='data-4395c207-0451-4a37-bf0e-0800e4a36cd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>v_p^2</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_U2V2</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6a26fd9f-a437-4c9a-8309-cbf93be811fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a26fd9f-a437-4c9a-8309-cbf93be811fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dae69a00-0e80-4a41-905d-b60318876c45' class='xr-var-data-in' type='checkbox'><label for='data-dae69a00-0e80-4a41-905d-b60318876c45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u_p^2+v_p^2</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W2</span></div><div class='xr-var-dims'>(Time, z_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2614c622-15d6-4530-836e-974814bf5e28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2614c622-15d6-4530-836e-974814bf5e28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1c0435a-348c-461f-a221-1d961c028983' class='xr-var-data-in' type='checkbox'><label for='data-a1c0435a-348c-461f-a221-1d961c028983' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>w_p^2</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[20657 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W3</span></div><div class='xr-var-dims'>(Time, z_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c20fbb03-d01a-4aa4-b0f6-815cd3b5d2c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c20fbb03-d01a-4aa4-b0f6-815cd3b5d2c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85721db3-e2c0-4b0a-be16-44b3afefde65' class='xr-var-data-in' type='checkbox'><label for='data-85721db3-e2c0-4b0a-be16-44b3afefde65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>w_p^3</dd><dt><span>units :</span></dt><dd>m^3/s^3</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[20657 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WSKEW</span></div><div class='xr-var-dims'>(Time, z_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-494ec1f9-ebfa-428e-94e9-7371975bb17d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-494ec1f9-ebfa-428e-94e9-7371975bb17d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f91fba4b-43b2-4af3-8229-aa0e971d22d0' class='xr-var-data-in' type='checkbox'><label for='data-f91fba4b-43b2-4af3-8229-aa0e971d22d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Skewness &lt;w3&gt;/&lt;w2&gt;^(3/2)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[20657 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_UW</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c1b9a765-f56f-4f12-a6b5-8da9081c7e95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1b9a765-f56f-4f12-a6b5-8da9081c7e95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6b5762a-fbab-4f5e-83a8-0ef5e10808d6' class='xr-var-data-in' type='checkbox'><label for='data-c6b5762a-fbab-4f5e-83a8-0ef5e10808d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>x-momentum flux uw (rs+sgs)</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_VW</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cbfa0f65-d652-4fe3-b9f4-3d8215728509' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbfa0f65-d652-4fe3-b9f4-3d8215728509' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f317082-f0fd-4279-8ec6-0dc97575c957' class='xr-var-data-in' type='checkbox'><label for='data-5f317082-f0fd-4279-8ec6-0dc97575c957' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>y-momentum flux vw (rs+sgs)</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTH</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c94ab769-2aba-4b24-8098-659aedb8e53c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c94ab769-2aba-4b24-8098-659aedb8e53c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a1fd309-0158-4864-ba56-1cd57d5f31e3' class='xr-var-data-in' type='checkbox'><label for='data-6a1fd309-0158-4864-ba56-1cd57d5f31e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Potential temperature flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTHV</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a2fbdcf6-75ad-4250-a36c-d338d6dcf01b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2fbdcf6-75ad-4250-a36c-d338d6dcf01b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-176a367a-f5bb-4d91-9ebb-21ad5f81857f' class='xr-var-data-in' type='checkbox'><label for='data-176a367a-f5bb-4d91-9ebb-21ad5f81857f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Virtual potential temperature flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTHL</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cb7aa448-3535-4eec-9dfd-7066d80667d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb7aa448-3535-4eec-9dfd-7066d80667d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ace0a626-b05d-452d-9af4-680f03dec5ab' class='xr-var-data-in' type='checkbox'><label for='data-ace0a626-b05d-452d-9af4-680f03dec5ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water potential temperature flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQV</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9085bea2-deb5-4e57-8ef8-b67836bce6fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9085bea2-deb5-4e57-8ef8-b67836bce6fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74cc64d3-fa03-4c8a-9247-5d6b448bed1b' class='xr-var-data-in' type='checkbox'><label for='data-74cc64d3-fa03-4c8a-9247-5d6b448bed1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Water vapor flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dfeca10f-1b32-46dd-a4d4-479ef3a269ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfeca10f-1b32-46dd-a4d4-479ef3a269ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-285ea131-0cd9-4f0e-966d-92f3a5fd6454' class='xr-var-data-in' type='checkbox'><label for='data-285ea131-0cd9-4f0e-966d-92f3a5fd6454' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud water flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQI</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2e6946bf-5d04-44a9-a631-9080204e22ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e6946bf-5d04-44a9-a631-9080204e22ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73989f16-26c0-4c1c-9c8b-0d81fd8c221c' class='xr-var-data-in' type='checkbox'><label for='data-73989f16-26c0-4c1c-9c8b-0d81fd8c221c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Ice crystal (cloud ice) flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQL</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-57e52a73-4fe6-4e13-9b39-dd54cea04326' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57e52a73-4fe6-4e13-9b39-dd54cea04326' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-941eb368-6003-4709-8328-4c59ccd417e8' class='xr-var-data-in' type='checkbox'><label for='data-941eb368-6003-4709-8328-4c59ccd417e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQF</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-27379e6d-642b-4db1-aad6-4ad8633fabf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27379e6d-642b-4db1-aad6-4ad8633fabf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31e95d5b-2067-4621-8c70-014c86f30b64' class='xr-var-data-in' type='checkbox'><label for='data-31e95d5b-2067-4621-8c70-014c86f30b64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Frozen water flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQT</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c01be57-f1bc-43d4-bee1-3941b70dca84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c01be57-f1bc-43d4-bee1-3941b70dca84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a26636a-36a5-456b-b695-2ab332f444a3' class='xr-var-data-in' type='checkbox'><label for='data-4a26636a-36a5-456b-b695-2ab332f444a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Total water flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_UW_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-129dfaf2-4e99-44d8-82c1-0093e6cd46c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-129dfaf2-4e99-44d8-82c1-0093e6cd46c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56384433-8b22-455d-ba72-45cc6182b930' class='xr-var-data-in' type='checkbox'><label for='data-56384433-8b22-455d-ba72-45cc6182b930' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>x-momentum flux uw (sgs)</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_VW_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5385804a-50b2-4bc6-9f29-ed244329f5bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5385804a-50b2-4bc6-9f29-ed244329f5bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a13c640-9201-4d25-a229-880e3551c686' class='xr-var-data-in' type='checkbox'><label for='data-8a13c640-9201-4d25-a229-880e3551c686' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>y-momentum flux vw (sgs)</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTH_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ad424d3-2e74-456a-8ae4-98a3d8fd6021' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ad424d3-2e74-456a-8ae4-98a3d8fd6021' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c236c285-45fc-4051-ba81-af9da51bfa3a' class='xr-var-data-in' type='checkbox'><label for='data-c236c285-45fc-4051-ba81-af9da51bfa3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Potential temperature flux (sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTHV_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e340588d-810b-4162-afc9-701969e6b725' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e340588d-810b-4162-afc9-701969e6b725' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e54cf0f-fe41-4708-965a-eff253986b05' class='xr-var-data-in' type='checkbox'><label for='data-9e54cf0f-fe41-4708-965a-eff253986b05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Virtual potential temperature flux (sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTHL_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a41c4306-dacf-43c6-95af-aca441fa1c5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a41c4306-dacf-43c6-95af-aca441fa1c5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07880030-33b2-497b-aba7-0ccb08e375b8' class='xr-var-data-in' type='checkbox'><label for='data-07880030-33b2-497b-aba7-0ccb08e375b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water potential temperature flux (sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQV_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e3015dea-0f27-40e4-8a75-2174960d14f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3015dea-0f27-40e4-8a75-2174960d14f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc06c9c4-c54e-44f2-a862-91b98b63647d' class='xr-var-data-in' type='checkbox'><label for='data-dc06c9c4-c54e-44f2-a862-91b98b63647d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Water vapor flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQC_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5e8dba2-98fe-41c3-9a51-67128a856b46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5e8dba2-98fe-41c3-9a51-67128a856b46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-085ad55e-b43e-4b5d-8db8-afa5288030e9' class='xr-var-data-in' type='checkbox'><label for='data-085ad55e-b43e-4b5d-8db8-afa5288030e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud water flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQI_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-afceaba6-8f33-43c2-980f-112348564799' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afceaba6-8f33-43c2-980f-112348564799' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-221799dc-a2d4-4bc9-af37-16a127a37e0f' class='xr-var-data-in' type='checkbox'><label for='data-221799dc-a2d4-4bc9-af37-16a127a37e0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Ice crystal (cloud ice) flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQL_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42eccc0f-b040-4b49-9692-b96c4fe0835d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42eccc0f-b040-4b49-9692-b96c4fe0835d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-693e2e5c-9841-47a9-8e65-3bc8deb34b94' class='xr-var-data-in' type='checkbox'><label for='data-693e2e5c-9841-47a9-8e65-3bc8deb34b94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQF_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1d120fa8-659a-4348-a03d-f3fc111d32e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d120fa8-659a-4348-a03d-f3fc111d32e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ff0552f-39c8-42ac-883b-9307939e1abb' class='xr-var-data-in' type='checkbox'><label for='data-2ff0552f-39c8-42ac-883b-9307939e1abb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Frozen water flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQT_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e751ec78-cde3-47e9-8f3b-825fca6571b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e751ec78-cde3-47e9-8f3b-825fca6571b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-150485d1-8acf-48c8-8bc1-3257f033b25c' class='xr-var-data-in' type='checkbox'><label for='data-150485d1-8acf-48c8-8bc1-3257f033b25c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Total water flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SEDFQC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b6bfacd-b2d8-41c6-9961-ae6bb7ec3d00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b6bfacd-b2d8-41c6-9961-ae6bb7ec3d00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-956c22ed-d702-4d34-a8e1-298f046d606d' class='xr-var-data-in' type='checkbox'><label for='data-956c22ed-d702-4d34-a8e1-298f046d606d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Sedimentation flux of qc</dd><dt><span>units :</span></dt><dd>kg /m^2/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SEDFQR</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a22b0572-3a4d-47b8-82f8-3381f7bcb209' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a22b0572-3a4d-47b8-82f8-3381f7bcb209' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8723cc2-f7ed-493a-9507-cb85a60f81f4' class='xr-var-data-in' type='checkbox'><label for='data-b8723cc2-f7ed-493a-9507-cb85a60f81f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Sedimentation (Precipitation) flux of qr</dd><dt><span>units :</span></dt><dd>kg /m^2/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_COND</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9d99fe5-1cef-492d-af57-7467504ef006' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9d99fe5-1cef-492d-af57-7467504ef006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a4a790b-f861-4bd5-96ba-952ba8036904' class='xr-var-data-in' type='checkbox'><label for='data-0a4a790b-f861-4bd5-96ba-952ba8036904' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dth/dt due to net condensation</dd><dt><span>units :</span></dt><dd>K/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_LW</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-144bc72e-71ca-441b-a6b3-5a386c30f75f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-144bc72e-71ca-441b-a6b3-5a386c30f75f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14023427-f26d-4473-881a-f1cc33fd1c90' class='xr-var-data-in' type='checkbox'><label for='data-14023427-f26d-4473-881a-f1cc33fd1c90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dth/dt due to LW radiation</dd><dt><span>units :</span></dt><dd>K/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_SW</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8f415383-39e6-4206-9327-dc2215567f85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f415383-39e6-4206-9327-dc2215567f85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4abd4aff-b758-4d52-87ad-48434b23e0ea' class='xr-var-data-in' type='checkbox'><label for='data-4abd4aff-b758-4d52-87ad-48434b23e0ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dth/dt due to SW radiation</dd><dt><span>units :</span></dt><dd>K/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_LS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-94bd12b9-b819-4a1f-a0e6-df5aa6b0976f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94bd12b9-b819-4a1f-a0e6-df5aa6b0976f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c1123e3-28a9-4ece-8654-65267b1e8558' class='xr-var-data-in' type='checkbox'><label for='data-1c1123e3-28a9-4ece-8654-65267b1e8558' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dth/dt due to large-scale forcing</dd><dt><span>units :</span></dt><dd>K/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_PR</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9789e62a-46dc-4777-99be-002fafce6ed7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9789e62a-46dc-4777-99be-002fafce6ed7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8cba3ae-c515-4ac8-b6cb-01dddf168db8' class='xr-var-data-in' type='checkbox'><label for='data-c8cba3ae-c515-4ac8-b6cb-01dddf168db8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqv/dt due to conversion to precipitation</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_COND</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bdf70ee8-85f6-4341-bea0-0066266275f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdf70ee8-85f6-4341-bea0-0066266275f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00c14bae-bc9d-4d28-8951-8ac1501b2a89' class='xr-var-data-in' type='checkbox'><label for='data-00c14bae-bc9d-4d28-8951-8ac1501b2a89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqv/dt due to net condensation</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_LS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8ac24001-a028-4d9a-9032-8e1d707871e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ac24001-a028-4d9a-9032-8e1d707871e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51e6943f-8d8b-47bb-9aa7-451866792dd1' class='xr-var-data-in' type='checkbox'><label for='data-51e6943f-8d8b-47bb-9aa7-451866792dd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqv/dt due to large-scale forcing</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QCDT_PR</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-43dafae1-e44c-41e5-a4cc-1330bbe8176a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43dafae1-e44c-41e5-a4cc-1330bbe8176a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe5b34c-82a9-4350-ba76-9c1a69b9ab9e' class='xr-var-data-in' type='checkbox'><label for='data-0fe5b34c-82a9-4350-ba76-9c1a69b9ab9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqc/dt due to conversion to precipitation</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QCDT_SED</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e18ffe81-525d-497f-9d93-9a2148e93550' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e18ffe81-525d-497f-9d93-9a2148e93550' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9faecc2b-d136-4578-acc9-f73b149beee6' class='xr-var-data-in' type='checkbox'><label for='data-9faecc2b-d136-4578-acc9-f73b149beee6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqc/dt due to sedimentation</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QRDT_SED</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-53f40aa3-ee52-4aa1-995a-fa478da281bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53f40aa3-ee52-4aa1-995a-fa478da281bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-040a488e-9ac3-4a14-991d-4072eadbfa4c' class='xr-var-data-in' type='checkbox'><label for='data-040a488e-9ac3-4a14-991d-4072eadbfa4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqr/dt due to sedimentation</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_LSHOR</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-80346911-8e34-4df5-8eaf-953dd5104b33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80346911-8e34-4df5-8eaf-953dd5104b33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e48b904-29d0-46c1-92c5-4ba6f1164da1' class='xr-var-data-in' type='checkbox'><label for='data-3e48b904-29d0-46c1-92c5-4ba6f1164da1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>th tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_LSHOR</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-14a86cb2-7d7b-4420-97b0-b6dad5dc2044' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14a86cb2-7d7b-4420-97b0-b6dad5dc2044' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bc1fedd-a34c-4803-bc30-d3c90cee872b' class='xr-var-data-in' type='checkbox'><label for='data-7bc1fedd-a34c-4803-bc30-d3c90cee872b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_LSVER</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad4457fb-cb4a-46c7-b01f-6f093441bf8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad4457fb-cb4a-46c7-b01f-6f093441bf8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c66e8f05-bcc1-4708-8faa-610e69728932' class='xr-var-data-in' type='checkbox'><label for='data-c66e8f05-bcc1-4708-8faa-610e69728932' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>th tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_LSVER</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cfb6c007-a1d0-4265-8a38-2fd1efc4ccb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfb6c007-a1d0-4265-8a38-2fd1efc4ccb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d060fcb3-9b3a-40c1-928c-ff5df4c361a5' class='xr-var-data-in' type='checkbox'><label for='data-d060fcb3-9b3a-40c1-928c-ff5df4c361a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-35be28a4-f882-4d74-b63e-c56cc868c6e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35be28a4-f882-4d74-b63e-c56cc868c6e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b5de720-c385-472a-ac32-6a9bdc9b8e1b' class='xr-var-data-in' type='checkbox'><label for='data-7b5de720-c385-472a-ac32-6a9bdc9b8e1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>th tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-159336e2-06d0-441f-b2e8-22789209c6f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-159336e2-06d0-441f-b2e8-22789209c6f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b140000-78fc-4fc4-8d8a-3b9970478acd' class='xr-var-data-in' type='checkbox'><label for='data-7b140000-78fc-4fc4-8d8a-3b9970478acd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_UDT_LS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ff3eefd-cb35-4b21-a70c-2e04391ed370' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ff3eefd-cb35-4b21-a70c-2e04391ed370' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e069c883-1af0-46d1-9c91-125100b14fed' class='xr-var-data-in' type='checkbox'><label for='data-e069c883-1af0-46d1-9c91-125100b14fed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_VDT_LS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b75fb9fe-94fb-43aa-bdae-5b14475d9923' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b75fb9fe-94fb-43aa-bdae-5b14475d9923' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d2cb8c5-1cf9-4260-840f-f56ac3618b7a' class='xr-var-data-in' type='checkbox'><label for='data-3d2cb8c5-1cf9-4260-840f-f56ac3618b7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>v tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_UDT_LSVER</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-142dc7b0-46eb-408b-ba1d-d9fc7025f92b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-142dc7b0-46eb-408b-ba1d-d9fc7025f92b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-025993e8-b03e-4485-a029-670ecd4fd35e' class='xr-var-data-in' type='checkbox'><label for='data-025993e8-b03e-4485-a029-670ecd4fd35e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_VDT_LSVER</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0f0a48a9-7da5-4461-a7c2-9e902e37225e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f0a48a9-7da5-4461-a7c2-9e902e37225e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f63bff06-40a3-4b1e-a7dc-1b154f1f4627' class='xr-var-data-in' type='checkbox'><label for='data-f63bff06-40a3-4b1e-a7dc-1b154f1f4627' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>v tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_UDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d812b6d-ae6e-4fa4-936a-ff698c7a16fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d812b6d-ae6e-4fa4-936a-ff698c7a16fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b4bf60d-baf7-45e8-abbf-f7ce669fc54f' class='xr-var-data-in' type='checkbox'><label for='data-0b4bf60d-baf7-45e8-abbf-f7ce669fc54f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_VDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9bc90674-00d0-4cdf-932c-6df4fd7e7a19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bc90674-00d0-4cdf-932c-6df4fd7e7a19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b370b18-3977-4738-baff-ec38b8337447' class='xr-var-data-in' type='checkbox'><label for='data-1b370b18-3977-4738-baff-ec38b8337447' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>v tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SWUPF</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-801fde6b-6764-46b8-a81b-2b016085c22d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-801fde6b-6764-46b8-a81b-2b016085c22d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92a08c2b-69e5-40ac-b409-57f7b0e67db0' class='xr-var-data-in' type='checkbox'><label for='data-92a08c2b-69e5-40ac-b409-57f7b0e67db0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>SW flux upward</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SWDNF</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-be0f5914-5684-4821-9207-795d41e5ce8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be0f5914-5684-4821-9207-795d41e5ce8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77b01aa5-cefa-4596-be1a-0f0c77d0ee06' class='xr-var-data-in' type='checkbox'><label for='data-77b01aa5-cefa-4596-be1a-0f0c77d0ee06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>SW flux downward</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_LWUPF</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1918ec22-06e5-4afe-ba37-f6f9473b23f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1918ec22-06e5-4afe-ba37-f6f9473b23f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1aceb6a5-150f-4dd1-9423-97690e478937' class='xr-var-data-in' type='checkbox'><label for='data-1aceb6a5-150f-4dd1-9423-97690e478937' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>LW flux upward</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_LWDNF</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03ad49be-29e6-4cd2-9170-22c39f4b8342' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03ad49be-29e6-4cd2-9170-22c39f4b8342' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc79db9e-7a0d-418a-94d6-1f0e8296040c' class='xr-var-data-in' type='checkbox'><label for='data-dc79db9e-7a0d-418a-94d6-1f0e8296040c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>LW flux downward</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_RS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-013f686e-b633-420d-a129-6671ba006021' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-013f686e-b633-420d-a129-6671ba006021' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e696e37a-dad5-4fa4-9117-98fe888734c6' class='xr-var-data-in' type='checkbox'><label for='data-e696e37a-dad5-4fa4-9117-98fe888734c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>RS TKE</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_SH</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f26a93b-da75-46ba-9cf8-a36f60bb7246' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f26a93b-da75-46ba-9cf8-a36f60bb7246' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-455c1642-a6dc-42be-98d4-5976acae02bb' class='xr-var-data-in' type='checkbox'><label for='data-455c1642-a6dc-42be-98d4-5976acae02bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>RS TKE shear production</dd><dt><span>units :</span></dt><dd>m^2/s^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_BU</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-219df5e5-0024-4e89-9620-235bd398f5c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-219df5e5-0024-4e89-9620-235bd398f5c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43334702-9605-44af-8fc9-1391c5fa8731' class='xr-var-data-in' type='checkbox'><label for='data-43334702-9605-44af-8fc9-1391c5fa8731' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>RS TKE buoyancy production</dd><dt><span>units :</span></dt><dd>m^2/s^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_TR</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2cf63401-da2a-4ca6-ab46-b4fb6851187d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cf63401-da2a-4ca6-ab46-b4fb6851187d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a4b8f18-bae8-46c1-8729-700a8af2b841' class='xr-var-data-in' type='checkbox'><label for='data-1a4b8f18-bae8-46c1-8729-700a8af2b841' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>RS TKE turbulent + pressure transport</dd><dt><span>units :</span></dt><dd>m^2/s^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_DI</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-583609cd-d65b-415c-b5f7-987e0b733207' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-583609cd-d65b-415c-b5f7-987e0b733207' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed60b76a-fb20-48a3-976e-3aaa33a35143' class='xr-var-data-in' type='checkbox'><label for='data-ed60b76a-fb20-48a3-976e-3aaa33a35143' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>TKE dissipation</dd><dt><span>units :</span></dt><dd>m^2/s^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_SGS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0cd7c029-1098-49f0-b171-2a68a9aa386a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cd7c029-1098-49f0-b171-2a68a9aa386a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb47d560-c313-42ce-85a3-cc83ebd32f80' class='xr-var-data-in' type='checkbox'><label for='data-bb47d560-c313-42ce-85a3-cc83ebd32f80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>SGS TKE</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8a27f4b8-c4e0-40d1-a74f-5e2bff814016' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a27f4b8-c4e0-40d1-a74f-5e2bff814016' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15a19ace-0c63-4cb7-b689-118cfbc65912' class='xr-var-data-in' type='checkbox'><label for='data-15a19ace-0c63-4cb7-b689-118cfbc65912' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of w</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THL_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ef4687e2-37ad-4aa6-8fdd-47cb5bbc9597' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef4687e2-37ad-4aa6-8fdd-47cb5bbc9597' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7348409-b9da-49c8-b648-94651caf347c' class='xr-var-data-in' type='checkbox'><label for='data-a7348409-b9da-49c8-b648-94651caf347c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of thl</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QT_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d7c4d86-7e7e-4d24-a73a-d612831463e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d7c4d86-7e7e-4d24-a73a-d612831463e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb204757-08ad-45e9-a791-ccf741416a5d' class='xr-var-data-in' type='checkbox'><label for='data-bb204757-08ad-45e9-a791-ccf741416a5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qt</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QV_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5ee3ca6-bf53-4625-a545-a7d8145ec2d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5ee3ca6-bf53-4625-a545-a7d8145ec2d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51f9c943-484f-4eee-84bd-85cc98b73ea1' class='xr-var-data-in' type='checkbox'><label for='data-51f9c943-484f-4eee-84bd-85cc98b73ea1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qv</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QL_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5504ed24-1b01-46d3-9b5e-12a6c619e87d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5504ed24-1b01-46d3-9b5e-12a6c619e87d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-447fa7fc-207a-4347-a5dd-d9d2f6b42244' class='xr-var-data-in' type='checkbox'><label for='data-447fa7fc-207a-4347-a5dd-d9d2f6b42244' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of ql</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QF_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd9fea7c-0fda-41d3-a2e9-1a8c00b889d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd9fea7c-0fda-41d3-a2e9-1a8c00b889d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-691869a1-6bda-4bc5-b3b0-b76cfb724af7' class='xr-var-data-in' type='checkbox'><label for='data-691869a1-6bda-4bc5-b3b0-b76cfb724af7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qf</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QC_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5063088d-1777-49a2-ab32-b63e6a4a49b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5063088d-1777-49a2-ab32-b63e6a4a49b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1345e967-321e-42a5-8d16-b51c7ea88915' class='xr-var-data-in' type='checkbox'><label for='data-1345e967-321e-42a5-8d16-b51c7ea88915' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qc</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QI_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45e7b6e4-ccb3-4973-a7b6-c1c47114d7ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45e7b6e4-ccb3-4973-a7b6-c1c47114d7ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bce1ac4c-11a2-46fd-8235-1fd804c2715b' class='xr-var-data-in' type='checkbox'><label for='data-bce1ac4c-11a2-46fd-8235-1fd804c2715b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qi</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QNC_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a60e7078-78a4-4913-ad76-046cb4346345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a60e7078-78a4-4913-ad76-046cb4346345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82906277-626a-4343-a2ed-6527cbf39ef8' class='xr-var-data-in' type='checkbox'><label for='data-82906277-626a-4343-a2ed-6527cbf39ef8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qnc</dd><dt><span>units :</span></dt><dd>cm-3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THV_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8e7867f2-2d7e-4dc7-9a28-90b17e80cdff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e7867f2-2d7e-4dc7-9a28-90b17e80cdff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-003ae884-519f-4e55-8232-d65306754234' class='xr-var-data-in' type='checkbox'><label for='data-003ae884-519f-4e55-8232-d65306754234' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of thv</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W2_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7a2fe0b5-af84-4284-b7c9-6872b2f6dacc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a2fe0b5-af84-4284-b7c9-6872b2f6dacc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dba0ce7-04fa-46b5-a752-f5aaa6cebec2' class='xr-var-data-in' type='checkbox'><label for='data-3dba0ce7-04fa-46b5-a752-f5aaa6cebec2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of w variance</dd><dt><span>units :</span></dt><dd>(m/s)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AW_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-726180e2-1708-4ed0-93ed-daf975aa8d4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-726180e2-1708-4ed0-93ed-daf975aa8d4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36b76af6-7168-423d-acf5-e67fd63ffaa2' class='xr-var-data-in' type='checkbox'><label for='data-36b76af6-7168-423d-acf5-e67fd63ffaa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of w</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWTHL_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ddb71771-0d30-43aa-b8fb-8543311ba0be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddb71771-0d30-43aa-b8fb-8543311ba0be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b90d953a-9f8a-4782-9851-23f05c3868e8' class='xr-var-data-in' type='checkbox'><label for='data-b90d953a-9f8a-4782-9851-23f05c3868e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wthl</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQT_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a556dfa5-ecd9-4266-8aac-b2572e19d421' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a556dfa5-ecd9-4266-8aac-b2572e19d421' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-772a93ef-5e2a-4116-8bc7-47e01edf5ec8' class='xr-var-data-in' type='checkbox'><label for='data-772a93ef-5e2a-4116-8bc7-47e01edf5ec8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wqt</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQV_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-86e12d49-ff4b-4030-b258-b99b5a73b067' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86e12d49-ff4b-4030-b258-b99b5a73b067' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f861b2d5-a0ab-48b5-b671-ea6b9c6d11ee' class='xr-var-data-in' type='checkbox'><label for='data-f861b2d5-a0ab-48b5-b671-ea6b9c6d11ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wqv</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQL_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-470795ae-0429-4ee7-9d99-bfcc78c6ec43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-470795ae-0429-4ee7-9d99-bfcc78c6ec43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7183aa66-80e4-4638-b772-348688e159eb' class='xr-var-data-in' type='checkbox'><label for='data-7183aa66-80e4-4638-b772-348688e159eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wql</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQF_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-14ed72cd-0e4b-4dcd-a742-cd4e006dca91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14ed72cd-0e4b-4dcd-a742-cd4e006dca91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c011965-cf35-400c-bc42-7709047813f8' class='xr-var-data-in' type='checkbox'><label for='data-9c011965-cf35-400c-bc42-7709047813f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wqf</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQC_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6f3af64-1cb0-4cdf-819e-90fede514848' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6f3af64-1cb0-4cdf-819e-90fede514848' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6568fc1-fbea-446c-b533-f3fde3b65287' class='xr-var-data-in' type='checkbox'><label for='data-c6568fc1-fbea-446c-b533-f3fde3b65287' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wqc</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQI_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-55c16239-98cf-43f4-b242-5301f47e2728' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55c16239-98cf-43f4-b242-5301f47e2728' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66f6a4c2-b136-4a81-9815-a24903ffcb38' class='xr-var-data-in' type='checkbox'><label for='data-66f6a4c2-b136-4a81-9815-a24903ffcb38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wqi</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWTHV_C</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c86f95e-af16-4949-baac-2f2edc42e84c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c86f95e-af16-4949-baac-2f2edc42e84c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f20288c6-c412-45ca-9664-3b511b33ea89' class='xr-var-data-in' type='checkbox'><label for='data-f20288c6-c412-45ca-9664-3b511b33ea89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wthv</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_A_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-78a09385-0a48-47c9-80d1-b52604219177' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78a09385-0a48-47c9-80d1-b52604219177' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b9e5338-5d37-4f32-84db-ba4a9a0d851b' class='xr-var-data-in' type='checkbox'><label for='data-9b9e5338-5d37-4f32-84db-ba4a9a0d851b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Fraction of cloudcore grid points</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-465f98d2-2948-4fbb-b619-4b82e71c128e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-465f98d2-2948-4fbb-b619-4b82e71c128e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f0f1953-2a2d-4cfd-84b8-094a34af9ed1' class='xr-var-data-in' type='checkbox'><label for='data-9f0f1953-2a2d-4cfd-84b8-094a34af9ed1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of w</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THL_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a4c2e7fe-32a5-4488-ba7a-07a5e71920b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4c2e7fe-32a5-4488-ba7a-07a5e71920b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2c482b9-6a04-4082-9e8d-779842cf9502' class='xr-var-data-in' type='checkbox'><label for='data-d2c482b9-6a04-4082-9e8d-779842cf9502' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of thl</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QT_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-26fadda8-eddf-4fd0-be32-ce35be7a2bdb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26fadda8-eddf-4fd0-be32-ce35be7a2bdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4cd04b9-b9b4-4162-b9ba-0633c6bda2ea' class='xr-var-data-in' type='checkbox'><label for='data-e4cd04b9-b9b4-4162-b9ba-0633c6bda2ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of qt</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QV_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0cc6bc81-5ad1-495e-8a8a-6c68fb753eff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cc6bc81-5ad1-495e-8a8a-6c68fb753eff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d93c233c-fb61-4477-a3fe-6462fb120e8b' class='xr-var-data-in' type='checkbox'><label for='data-d93c233c-fb61-4477-a3fe-6462fb120e8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of qv</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QL_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-25d7f75f-117a-4e65-9eed-519a2819b95b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25d7f75f-117a-4e65-9eed-519a2819b95b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00a7ac3c-9b4e-45c4-bc64-ac5a018dba37' class='xr-var-data-in' type='checkbox'><label for='data-00a7ac3c-9b4e-45c4-bc64-ac5a018dba37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of ql</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QF_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd66c4c0-892f-45d2-8d25-9bb65f2b8a93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd66c4c0-892f-45d2-8d25-9bb65f2b8a93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbde7aef-d6b6-4469-9a01-a43b9fd20759' class='xr-var-data-in' type='checkbox'><label for='data-bbde7aef-d6b6-4469-9a01-a43b9fd20759' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of qf</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QC_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c2d8047e-1e40-421e-bfb6-c60f2f875ed3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2d8047e-1e40-421e-bfb6-c60f2f875ed3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41707ea7-8671-40a3-9875-48eef969d83c' class='xr-var-data-in' type='checkbox'><label for='data-41707ea7-8671-40a3-9875-48eef969d83c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of qc</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QI_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c5b0811-da42-4992-9457-724c76cf3b3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c5b0811-da42-4992-9457-724c76cf3b3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ba240b5-fbe6-481d-b102-8c6234c680c2' class='xr-var-data-in' type='checkbox'><label for='data-6ba240b5-fbe6-481d-b102-8c6234c680c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of qi</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THV_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-94600f49-18eb-406b-9213-4541da7ae047' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94600f49-18eb-406b-9213-4541da7ae047' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59869ea3-db47-4f0e-8867-7580d22b2c23' class='xr-var-data-in' type='checkbox'><label for='data-59869ea3-db47-4f0e-8867-7580d22b2c23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of thv</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W2_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-43dbe175-df8e-4fd5-8e5a-11a58fbb1404' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43dbe175-df8e-4fd5-8e5a-11a58fbb1404' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64445b80-2add-4654-aacc-c674c79fa87a' class='xr-var-data-in' type='checkbox'><label for='data-64445b80-2add-4654-aacc-c674c79fa87a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of w variance</dd><dt><span>units :</span></dt><dd>(m/s)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AW_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c97a769-0b27-4a83-9896-fe13edc630cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c97a769-0b27-4a83-9896-fe13edc630cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1c3c644-24ca-443a-a5ab-81fbaf456b85' class='xr-var-data-in' type='checkbox'><label for='data-c1c3c644-24ca-443a-a5ab-81fbaf456b85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of w</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWTHL_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9eae4bef-d361-4933-9604-f1a32cb81269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9eae4bef-d361-4933-9604-f1a32cb81269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a761ab32-93c6-43df-8687-ccb6befc7be2' class='xr-var-data-in' type='checkbox'><label for='data-a761ab32-93c6-43df-8687-ccb6befc7be2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wthl</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQT_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a69b2e24-e045-474e-86ad-39250846cbf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a69b2e24-e045-474e-86ad-39250846cbf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51207546-4054-4f78-9d98-af24794a8a6d' class='xr-var-data-in' type='checkbox'><label for='data-51207546-4054-4f78-9d98-af24794a8a6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wqt</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQV_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-baa63db3-0ad7-48b7-8265-fc0c067b4b45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-baa63db3-0ad7-48b7-8265-fc0c067b4b45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65dfdffc-9eeb-4c5c-9f6c-34b3403b54a4' class='xr-var-data-in' type='checkbox'><label for='data-65dfdffc-9eeb-4c5c-9f6c-34b3403b54a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wqv</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQL_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4b494146-b041-41d7-98c6-d287ba1997a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b494146-b041-41d7-98c6-d287ba1997a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8a06f11-4399-4983-a74c-a0f40df9ba70' class='xr-var-data-in' type='checkbox'><label for='data-e8a06f11-4399-4983-a74c-a0f40df9ba70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wql</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQF_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d0383b3-e47b-42e2-bd6b-63b923c398dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d0383b3-e47b-42e2-bd6b-63b923c398dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c7cf8f2-cbd0-404b-a6e5-7bcc264bf0c5' class='xr-var-data-in' type='checkbox'><label for='data-8c7cf8f2-cbd0-404b-a6e5-7bcc264bf0c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wqf</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQC_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-97599d4d-c67e-4b0e-b480-61a630c7b948' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97599d4d-c67e-4b0e-b480-61a630c7b948' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bae075e-5b29-41c7-9712-69ad38626c03' class='xr-var-data-in' type='checkbox'><label for='data-1bae075e-5b29-41c7-9712-69ad38626c03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wqc</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQI_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5d974df7-cd51-481e-96d8-43d02a190929' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d974df7-cd51-481e-96d8-43d02a190929' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-083f3802-1d3e-4da5-b497-88d8b953d9f4' class='xr-var-data-in' type='checkbox'><label for='data-083f3802-1d3e-4da5-b497-88d8b953d9f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wqi</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWTHV_CC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f9befa93-728f-43bf-95f2-911d88b5187f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9befa93-728f-43bf-95f2-911d88b5187f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10e12129-1a20-4af2-892e-bf0aa5cfd209' class='xr-var-data-in' type='checkbox'><label for='data-10e12129-1a20-4af2-892e-bf0aa5cfd209' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wthv</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_THL</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b754a385-2d42-4005-b5b0-feee2b235dca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b754a385-2d42-4005-b5b0-feee2b235dca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-831c6fd8-cd50-44f4-bad1-60ea6e195fab' class='xr-var-data-in' type='checkbox'><label for='data-831c6fd8-cd50-44f4-bad1-60ea6e195fab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of thl</dd><dt><span>units :</span></dt><dd>K^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_QT</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-914b8b75-c5ff-468a-bee8-0cb6a7d5b826' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-914b8b75-c5ff-468a-bee8-0cb6a7d5b826' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8b3baa4-fc80-460d-bd29-bf66c9812a2b' class='xr-var-data-in' type='checkbox'><label for='data-c8b3baa4-fc80-460d-bd29-bf66c9812a2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of qt</dd><dt><span>units :</span></dt><dd>(kg/kg)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_QL</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-17942854-5532-4d76-95c6-437cebbf38b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17942854-5532-4d76-95c6-437cebbf38b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d50d56d6-07c8-474e-bb88-a58d9b4057ae' class='xr-var-data-in' type='checkbox'><label for='data-d50d56d6-07c8-474e-bb88-a58d9b4057ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of ql</dd><dt><span>units :</span></dt><dd>(kg/kg)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_QF</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9e879454-2d42-40a9-9d63-2140d2336c5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e879454-2d42-40a9-9d63-2140d2336c5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34f3f516-8d05-4944-b1a6-21f65617871d' class='xr-var-data-in' type='checkbox'><label for='data-34f3f516-8d05-4944-b1a6-21f65617871d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of qf</dd><dt><span>units :</span></dt><dd>(kg/kg)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_QC</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f17fb6be-1b79-4685-aaa7-638a5bef5ae1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f17fb6be-1b79-4685-aaa7-638a5bef5ae1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfae3239-1251-4dc7-bf6c-893a5c464cf8' class='xr-var-data-in' type='checkbox'><label for='data-cfae3239-1251-4dc7-bf6c-893a5c464cf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of qc</dd><dt><span>units :</span></dt><dd>(kg/kg)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_QI</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5bf983aa-9012-40e8-8810-835107191eb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bf983aa-9012-40e8-8810-835107191eb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e709214b-b50d-47bf-b954-1db5df2ef628' class='xr-var-data-in' type='checkbox'><label for='data-e709214b-b50d-47bf-b954-1db5df2ef628' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of qi</dd><dt><span>units :</span></dt><dd>(kg/kg)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_THV</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-283c24ff-52be-44ea-a020-c5a882bdc337' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-283c24ff-52be-44ea-a020-c5a882bdc337' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29f6b482-654e-4c10-a609-7512811cafc0' class='xr-var-data-in' type='checkbox'><label for='data-29f6b482-654e-4c10-a609-7512811cafc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of thv</dd><dt><span>units :</span></dt><dd>K^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TH2</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5361460b-3b41-4be8-9922-4e0a38d1a812' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5361460b-3b41-4be8-9922-4e0a38d1a812' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4435ef2-6e11-41fc-8b14-39b9db08e0fa' class='xr-var-data-in' type='checkbox'><label for='data-c4435ef2-6e11-41fc-8b14-39b9db08e0fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Variance of th</dd><dt><span>units :</span></dt><dd>K^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THV2</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eaa828af-3231-49f2-831b-7093481e9be7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eaa828af-3231-49f2-831b-7093481e9be7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9786037a-0561-469d-bd36-8d3537956ed0' class='xr-var-data-in' type='checkbox'><label for='data-9786037a-0561-469d-bd36-8d3537956ed0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Variance of thv</dd><dt><span>units :</span></dt><dd>K^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THL2</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c7a7d4af-1f45-4ff8-9654-a8bb3be8760a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7a7d4af-1f45-4ff8-9654-a8bb3be8760a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37f7be66-82f0-475c-baa5-354ea1464819' class='xr-var-data-in' type='checkbox'><label for='data-37f7be66-82f0-475c-baa5-354ea1464819' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Variance of thl</dd><dt><span>units :</span></dt><dd>K^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QV2</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-399faed5-bb3f-4e9b-8621-d00b84e912c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-399faed5-bb3f-4e9b-8621-d00b84e912c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-961ba616-22de-426d-80ff-0c8decde2111' class='xr-var-data-in' type='checkbox'><label for='data-961ba616-22de-426d-80ff-0c8decde2111' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Variance of qv</dd><dt><span>units :</span></dt><dd>kg^2/kg^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SMAXACTMAX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a6f1b208-9419-4adb-a5f4-7e1ca54f88d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6f1b208-9419-4adb-a5f4-7e1ca54f88d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c40bc6d8-06c2-4a27-93f2-472b76a9aec0' class='xr-var-data-in' type='checkbox'><label for='data-c40bc6d8-06c2-4a27-93f2-472b76a9aec0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Max of max supersat in Morrison microphysics</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_RMINACTMIN</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aa6a7bb4-c5ba-48f5-83b4-9a28aa055f47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa6a7bb4-c5ba-48f5-83b4-9a28aa055f47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d38d6678-8b7d-44e0-b48a-f7a8b602f1ab' class='xr-var-data-in' type='checkbox'><label for='data-d38d6678-8b7d-44e0-b48a-f7a8b602f1ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Min of min activated radius in Morrison microphysics</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WMAX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-511779c7-2cff-4a9d-8265-5ada5c37613f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-511779c7-2cff-4a9d-8265-5ada5c37613f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c903acb3-43e0-4503-acb2-82bc3033bb20' class='xr-var-data-in' type='checkbox'><label for='data-c903acb3-43e0-4503-acb2-82bc3033bb20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Max value of vertical motion</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WMIN</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7c493aee-dc22-4106-993f-8b612ed8e137' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c493aee-dc22-4106-993f-8b612ed8e137' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28b60931-88df-47e7-8f50-f240fad316a6' class='xr-var-data-in' type='checkbox'><label for='data-28b60931-88df-47e7-8f50-f240fad316a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Min value of vertical motion</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_TH</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6ef89d3b-d18f-403d-80f2-4da72837e609' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ef89d3b-d18f-403d-80f2-4da72837e609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1727870-4cbc-4e0d-8972-c9ba93fae58d' class='xr-var-data-in' type='checkbox'><label for='data-e1727870-4cbc-4e0d-8972-c9ba93fae58d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged potential temperature</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_U</span></div><div class='xr-var-dims'>(Time, z, y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-56f2d41b-4d38-4e93-b6cd-c854983e178b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56f2d41b-4d38-4e93-b6cd-c854983e178b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dddbe74-2b86-4b1c-a8ad-244bfb56177d' class='xr-var-data-in' type='checkbox'><label for='data-1dddbe74-2b86-4b1c-a8ad-244bfb56177d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged meridional wind speed</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>[1290516500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_V</span></div><div class='xr-var-dims'>(Time, z, y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f439a7b5-2201-43c5-aa1e-875314f6a6f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f439a7b5-2201-43c5-aa1e-875314f6a6f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a676bb4-2e23-4c6f-9178-9228318d2f48' class='xr-var-data-in' type='checkbox'><label for='data-5a676bb4-2e23-4c6f-9178-9228318d2f48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged zonal wind speed</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>[1290516500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_W</span></div><div class='xr-var-dims'>(Time, z_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-00903b17-afe4-43b5-8cb9-02fd5164905f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00903b17-afe4-43b5-8cb9-02fd5164905f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea61d28c-113f-49be-b231-9c729965c7b8' class='xr-var-data-in' type='checkbox'><label for='data-ea61d28c-113f-49be-b231-9c729965c7b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged vertical wind speed</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[1291062500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_W2</span></div><div class='xr-var-dims'>(Time, z_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ba3e90a1-3c4b-4633-9255-b1a6e8025829' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba3e90a1-3c4b-4633-9255-b1a6e8025829' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19eec929-9da2-420d-9b61-63dbaa6ac349' class='xr-var-data-in' type='checkbox'><label for='data-19eec929-9da2-420d-9b61-63dbaa6ac349' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged vertical wind speed variance</dd><dt><span>units :</span></dt><dd>m^2 s^-2</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[1291062500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QV</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7c8b0dd1-b8b1-419b-a561-65d14def3cb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c8b0dd1-b8b1-419b-a561-65d14def3cb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53564d54-1fd9-4adf-834e-5e9f1b92d363' class='xr-var-data-in' type='checkbox'><label for='data-53564d54-1fd9-4adf-834e-5e9f1b92d363' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged water vapor mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QC</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-22aaec80-1db0-4ba2-a707-f2a04dd5546d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22aaec80-1db0-4ba2-a707-f2a04dd5546d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54512f80-b0fe-4168-8d42-77333842b2d3' class='xr-var-data-in' type='checkbox'><label for='data-54512f80-b0fe-4168-8d42-77333842b2d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged cloud droplet mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QR</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-69aaf9eb-c94b-484b-aced-b68b2a3b4a5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69aaf9eb-c94b-484b-aced-b68b2a3b4a5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3ce5c90-f4fa-4c2f-9f40-a2b57fd55fbc' class='xr-var-data-in' type='checkbox'><label for='data-c3ce5c90-f4fa-4c2f-9f40-a2b57fd55fbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged rain droplet mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QI</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d0c29a4-308e-435b-aecb-37befd78efe8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d0c29a4-308e-435b-aecb-37befd78efe8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6b97dbd-cb0f-409b-8a44-62173195b74b' class='xr-var-data-in' type='checkbox'><label for='data-f6b97dbd-cb0f-409b-8a44-62173195b74b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged cloud ice mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QS</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-44eb86c5-bfe0-45d0-91f8-78109ce96e97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44eb86c5-bfe0-45d0-91f8-78109ce96e97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f50e78e8-1c41-4be9-8161-6650cdaefc00' class='xr-var-data-in' type='checkbox'><label for='data-f50e78e8-1c41-4be9-8161-6650cdaefc00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged snow mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QG</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-88877182-570c-46d9-a161-21c3d5f56181' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88877182-570c-46d9-a161-21c3d5f56181' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a736f2b-05d5-43aa-a3b2-c715abed06a3' class='xr-var-data-in' type='checkbox'><label for='data-4a736f2b-05d5-43aa-a3b2-c715abed06a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged graupel mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_LWC</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7af6bd09-6408-46e3-b51f-5ca192fec3b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7af6bd09-6408-46e3-b51f-5ca192fec3b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5482858c-5499-45d6-961b-b08239ce68ee' class='xr-var-data-in' type='checkbox'><label for='data-5482858c-5499-45d6-961b-b08239ce68ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged liquid water content (based on ql)</dd><dt><span>units :</span></dt><dd>kg/m^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_IWC</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5a32a2d-6855-4248-9c4d-fa41bd032938' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5a32a2d-6855-4248-9c4d-fa41bd032938' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f36db91-3848-444c-9066-76cf1eaa015b' class='xr-var-data-in' type='checkbox'><label for='data-3f36db91-3848-444c-9066-76cf1eaa015b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged ice water content (based on qf)</dd><dt><span>units :</span></dt><dd>kg/m^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_CLDFRAC</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d582b17-db19-45d0-9ee7-910e3133c985' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d582b17-db19-45d0-9ee7-910e3133c985' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aabca80-1640-438d-95da-bc9c7bec6bbc' class='xr-var-data-in' type='checkbox'><label for='data-9aabca80-1640-438d-95da-bc9c7bec6bbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged cloud fraction</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSS_LWP</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5cbd38f1-3bb5-4b8d-a9b9-4022d1d63c1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cbd38f1-3bb5-4b8d-a9b9-4022d1d63c1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a7bbc59-1d51-47f3-a4be-4a81874c766d' class='xr-var-data-in' type='checkbox'><label for='data-5a7bbc59-1d51-47f3-a4be-4a81874c766d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Time-averaged liquid water path (based on ql)</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[5687500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSS_IWP</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-809a7fad-ccdf-4aef-8cb0-51d52a27d8ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-809a7fad-ccdf-4aef-8cb0-51d52a27d8ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3deb2f8c-46bb-459f-ad10-d19fdc8d9d3d' class='xr-var-data-in' type='checkbox'><label for='data-3deb2f8c-46bb-459f-ad10-d19fdc8d9d3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Time-averaged ice water path (based on qf)</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[5687500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSS_CLDTOT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-50217b90-bd70-4ddf-8a5d-4e0a3071f7e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50217b90-bd70-4ddf-8a5d-4e0a3071f7e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3af1d9a-cb34-4ce6-992f-63214bc9f792' class='xr-var-data-in' type='checkbox'><label for='data-a3af1d9a-cb34-4ce6-992f-63214bc9f792' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Time-averaged fractional cloud cover</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[5687500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSS_CLDLOW</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c5b95e6e-49e6-4669-8025-d3dcc6bd3c8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5b95e6e-49e6-4669-8025-d3dcc6bd3c8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc459558-4a1c-4bc3-8646-15805e7d2cc2' class='xr-var-data-in' type='checkbox'><label for='data-dc459558-4a1c-4bc3-8646-15805e7d2cc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Time-averaged fractional low-cloud cover (&lt;5 km)</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[5687500 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b388fb98-1fe8-48b8-a20a-a681be88db5e' class='xr-section-summary-in' type='checkbox'  ><label for='section-b388fb98-1fe8-48b8-a20a-a681be88db5e' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b474f286-ea09-420d-95bf-e386d42e22ac' class='xr-index-data-in' type='checkbox'/><label for='index-b474f286-ea09-420d-95bf-e386d42e22ac' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-04-04 12:00:00&#x27;, &#x27;2019-04-04 12:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 12:20:00&#x27;, &#x27;2019-04-04 12:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 12:40:00&#x27;, &#x27;2019-04-04 12:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 13:00:00&#x27;, &#x27;2019-04-04 13:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 13:20:00&#x27;, &#x27;2019-04-04 13:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 13:40:00&#x27;, &#x27;2019-04-04 13:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 14:00:00&#x27;, &#x27;2019-04-04 14:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 14:20:00&#x27;, &#x27;2019-04-04 14:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 14:40:00&#x27;, &#x27;2019-04-04 14:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 15:00:00&#x27;, &#x27;2019-04-04 15:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 15:20:00&#x27;, &#x27;2019-04-04 15:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 15:40:00&#x27;, &#x27;2019-04-04 15:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 16:00:00&#x27;, &#x27;2019-04-04 16:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 16:20:00&#x27;, &#x27;2019-04-04 16:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 16:40:00&#x27;, &#x27;2019-04-04 16:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 17:00:00&#x27;, &#x27;2019-04-04 17:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 17:20:00&#x27;, &#x27;2019-04-04 17:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 17:40:00&#x27;, &#x27;2019-04-04 17:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 18:00:00&#x27;, &#x27;2019-04-04 18:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 18:20:00&#x27;, &#x27;2019-04-04 18:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 18:40:00&#x27;, &#x27;2019-04-04 18:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 19:00:00&#x27;, &#x27;2019-04-04 19:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 19:20:00&#x27;, &#x27;2019-04-04 19:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 19:40:00&#x27;, &#x27;2019-04-04 19:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 20:00:00&#x27;, &#x27;2019-04-04 20:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 20:20:00&#x27;, &#x27;2019-04-04 20:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 20:40:00&#x27;, &#x27;2019-04-04 20:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 21:00:00&#x27;, &#x27;2019-04-04 21:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 21:20:00&#x27;, &#x27;2019-04-04 21:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 21:40:00&#x27;, &#x27;2019-04-04 21:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 22:00:00&#x27;, &#x27;2019-04-04 22:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 22:20:00&#x27;, &#x27;2019-04-04 22:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 22:40:00&#x27;, &#x27;2019-04-04 22:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 23:00:00&#x27;, &#x27;2019-04-04 23:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 23:20:00&#x27;, &#x27;2019-04-04 23:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 23:40:00&#x27;, &#x27;2019-04-04 23:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 00:00:00&#x27;, &#x27;2019-04-05 00:10:00&#x27;,\n",
+       "               &#x27;2019-04-05 00:20:00&#x27;, &#x27;2019-04-05 00:30:00&#x27;,\n",
+       "               &#x27;2019-04-05 00:40:00&#x27;, &#x27;2019-04-05 00:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 01:00:00&#x27;, &#x27;2019-04-05 01:10:00&#x27;,\n",
+       "               &#x27;2019-04-05 01:20:00&#x27;, &#x27;2019-04-05 01:30:00&#x27;,\n",
+       "               &#x27;2019-04-05 01:40:00&#x27;, &#x27;2019-04-05 01:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 02:00:00&#x27;, &#x27;2019-04-05 02:10:00&#x27;,\n",
+       "               &#x27;2019-04-05 02:20:00&#x27;, &#x27;2019-04-05 02:30:00&#x27;,\n",
+       "               &#x27;2019-04-05 02:40:00&#x27;, &#x27;2019-04-05 02:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 03:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_stag</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-63f19327-2d1b-4e70-ad78-b6d882220b4a' class='xr-index-data-in' type='checkbox'/><label for='index-63f19327-2d1b-4e70-ad78-b6d882220b4a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12500.0, -12400.0, -12300.0, -12200.0, -12100.0, -12000.0, -11900.0,\n",
+       "       -11800.0, -11700.0, -11600.0,\n",
+       "       ...\n",
+       "        11600.0,  11700.0,  11800.0,  11900.0,  12000.0,  12100.0,  12200.0,\n",
+       "        12300.0,  12400.0,  12500.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y_stag&#x27;, length=251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-95c38c4d-e15a-4a71-bdb3-7ff1fb8e872c' class='xr-index-data-in' type='checkbox'/><label for='index-95c38c4d-e15a-4a71-bdb3-7ff1fb8e872c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12450.0, -12350.0, -12250.0, -12150.0, -12050.0, -11950.0, -11850.0,\n",
+       "       -11750.0, -11650.0, -11550.0,\n",
+       "       ...\n",
+       "        11550.0,  11650.0,  11750.0,  11850.0,  11950.0,  12050.0,  12150.0,\n",
+       "        12250.0,  12350.0,  12450.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=250))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x_stag</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-932722b8-9251-4e10-80d5-85c4ce374ea2' class='xr-index-data-in' type='checkbox'/><label for='index-932722b8-9251-4e10-80d5-85c4ce374ea2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12500.0, -12400.0, -12300.0, -12200.0, -12100.0, -12000.0, -11900.0,\n",
+       "       -11800.0, -11700.0, -11600.0,\n",
+       "       ...\n",
+       "        11600.0,  11700.0,  11800.0,  11900.0,  12000.0,  12100.0,  12200.0,\n",
+       "        12300.0,  12400.0,  12500.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x_stag&#x27;, length=251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d48b3d5b-e3c0-4f55-ace3-a092ef25a173' class='xr-index-data-in' type='checkbox'/><label for='index-d48b3d5b-e3c0-4f55-ace3-a092ef25a173' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12450.0, -12350.0, -12250.0, -12150.0, -12050.0, -11950.0, -11850.0,\n",
+       "       -11750.0, -11650.0, -11550.0,\n",
+       "       ...\n",
+       "        11550.0,  11650.0,  11750.0,  11850.0,  11950.0,  12050.0,  12150.0,\n",
+       "        12250.0,  12350.0,  12450.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=250))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49d46497-1a40-4ca6-a5b9-788e842493d1' class='xr-section-summary-in' type='checkbox'  ><label for='section-49d46497-1a40-4ca6-a5b9-788e842493d1' class='xr-section-summary' >Attributes: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TITLE :</span></dt><dd> OUTPUT FROM WRF V3.8.1 MODEL</dd><dt><span>START_DATE :</span></dt><dd>2019-04-04_12:00:00</dd><dt><span>WEST-EAST_GRID_DIMENSION :</span></dt><dd>251</dd><dt><span>SOUTH-NORTH_GRID_DIMENSION :</span></dt><dd>251</dd><dt><span>BOTTOM-TOP_GRID_DIMENSION :</span></dt><dd>227</dd><dt><span>DX :</span></dt><dd>100.0</dd><dt><span>DY :</span></dt><dd>100.0</dd><dt><span>GRIDTYPE :</span></dt><dd>C</dd><dt><span>DIFF_OPT :</span></dt><dd>2</dd><dt><span>KM_OPT :</span></dt><dd>2</dd><dt><span>DAMP_OPT :</span></dt><dd>3</dd><dt><span>DAMPCOEF :</span></dt><dd>0.2</dd><dt><span>KHDIF :</span></dt><dd>1.0</dd><dt><span>KVDIF :</span></dt><dd>1.0</dd><dt><span>MP_PHYSICS :</span></dt><dd>8</dd><dt><span>RA_LW_PHYSICS :</span></dt><dd>4</dd><dt><span>RA_SW_PHYSICS :</span></dt><dd>4</dd><dt><span>SF_SFCLAY_PHYSICS :</span></dt><dd>1</dd><dt><span>SF_SURFACE_PHYSICS :</span></dt><dd>1</dd><dt><span>BL_PBL_PHYSICS :</span></dt><dd>0</dd><dt><span>CU_PHYSICS :</span></dt><dd>0</dd><dt><span>SF_LAKE_PHYSICS :</span></dt><dd>0</dd><dt><span>SURFACE_INPUT_SOURCE :</span></dt><dd>3</dd><dt><span>SST_UPDATE :</span></dt><dd>0</dd><dt><span>GRID_FDDA :</span></dt><dd>0</dd><dt><span>GFDDA_INTERVAL_M :</span></dt><dd>0</dd><dt><span>GFDDA_END_H :</span></dt><dd>0</dd><dt><span>GRID_SFDDA :</span></dt><dd>0</dd><dt><span>SGFDDA_INTERVAL_M :</span></dt><dd>0</dd><dt><span>SGFDDA_END_H :</span></dt><dd>0</dd><dt><span>HYPSOMETRIC_OPT :</span></dt><dd>1</dd><dt><span>USE_THETA_M :</span></dt><dd>1</dd><dt><span>WEST-EAST_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>WEST-EAST_PATCH_END_UNSTAG :</span></dt><dd>250</dd><dt><span>WEST-EAST_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>WEST-EAST_PATCH_END_STAG :</span></dt><dd>251</dd><dt><span>SOUTH-NORTH_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>SOUTH-NORTH_PATCH_END_UNSTAG :</span></dt><dd>250</dd><dt><span>SOUTH-NORTH_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>SOUTH-NORTH_PATCH_END_STAG :</span></dt><dd>251</dd><dt><span>BOTTOM-TOP_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>BOTTOM-TOP_PATCH_END_UNSTAG :</span></dt><dd>226</dd><dt><span>BOTTOM-TOP_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>BOTTOM-TOP_PATCH_END_STAG :</span></dt><dd>227</dd><dt><span>GRID_ID :</span></dt><dd>1</dd><dt><span>PARENT_ID :</span></dt><dd>0</dd><dt><span>I_PARENT_START :</span></dt><dd>0</dd><dt><span>J_PARENT_START :</span></dt><dd>0</dd><dt><span>PARENT_GRID_RATIO :</span></dt><dd>1</dd><dt><span>DT :</span></dt><dd>0.5</dd><dt><span>CEN_LAT :</span></dt><dd>0.0</dd><dt><span>CEN_LON :</span></dt><dd>0.0</dd><dt><span>TRUELAT1 :</span></dt><dd>0.0</dd><dt><span>TRUELAT2 :</span></dt><dd>0.0</dd><dt><span>MOAD_CEN_LAT :</span></dt><dd>0.0</dd><dt><span>STAND_LON :</span></dt><dd>0.0</dd><dt><span>POLE_LAT :</span></dt><dd>0.0</dd><dt><span>POLE_LON :</span></dt><dd>0.0</dd><dt><span>GMT :</span></dt><dd>0.0</dd><dt><span>JULYR :</span></dt><dd>0</dd><dt><span>JULDAY :</span></dt><dd>1</dd><dt><span>MAP_PROJ :</span></dt><dd>0</dd><dt><span>MAP_PROJ_CHAR :</span></dt><dd>Cartesian</dd><dt><span>MMINLU :</span></dt><dd></dd><dt><span>NUM_LAND_CAT :</span></dt><dd>21</dd><dt><span>ISWATER :</span></dt><dd>16</dd><dt><span>ISLAKE :</span></dt><dd>0</dd><dt><span>ISICE :</span></dt><dd>0</dd><dt><span>ISURBAN :</span></dt><dd>0</dd><dt><span>ISOILWATER :</span></dt><dd>0</dd><dt><span>doi :</span></dt><dd>10.5439/1342961</dd><dt><span>contacts :</span></dt><dd>lasso@arm.gov, LASSO PI: William Gustafson (William.Gustafson@pnnl.gov), LASSO Co-PI: Andrew Vogelmann (vogelmann@bnl.gov)</dd><dt><span>site_id :</span></dt><dd>sgp</dd><dt><span>facility_id :</span></dt><dd>C1</dd><dt><span>location_description :</span></dt><dd>Southern Great Plains (SGP), Lamont, Oklahoma</dd><dt><span>date :</span></dt><dd>20190404</dd><dt><span>simulation_id_number :</span></dt><dd>7</dd><dt><span>model_type :</span></dt><dd>WRF</dd><dt><span>model_version :</span></dt><dd>3.8.1</dd><dt><span>model_github_hash :</span></dt><dd>b6b6a5cc4229eec1ea9b005746b5ebef2205fb07</dd><dt><span>output_domain_size :</span></dt><dd>25.0 km</dd><dt><span>output_number_of_levels :</span></dt><dd>226</dd><dt><span>output_horizontal_grid_spacing :</span></dt><dd>100 m</dd><dt><span>config_large_scale_forcing :</span></dt><dd>MSDA</dd><dt><span>config_large_scale_forcing_scale :</span></dt><dd>150 km</dd><dt><span>config_large_scale_forcing_specifics :</span></dt><dd>msda_sgp (v20191227)</dd><dt><span>config_surface_treatment :</span></dt><dd>VARANAL</dd><dt><span>config_surface_treatment_specifics :</span></dt><dd>sgp60varanarapC1.c1 (v20191126)</dd><dt><span>config_initial_condition :</span></dt><dd>Sounding</dd><dt><span>config_initial_condition_specifics :</span></dt><dd>sgpsondewnpnC1</dd><dt><span>config_aerosol :</span></dt><dd>NA</dd><dt><span>config_forecast_time :</span></dt><dd>15.0 h</dd><dt><span>config_boundary_method :</span></dt><dd>Periodic</dd><dt><span>config_microphysics :</span></dt><dd>Thompson (mp_physics=8)</dd><dt><span>config_nickname :</span></dt><dd>runlas20190404v1msda2d150km</dd><dt><span>simulation_origin_host :</span></dt><dd>cumulus-login2.ccs.ornl.gov</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 72GB\n",
+       "Dimensions:         (Time: 91, z: 226, z_stag: 227, y: 250, x: 250,\n",
+       "                     x_stag: 251, y_stag: 251)\n",
+       "Coordinates:\n",
+       "    XTIME           (Time) datetime64[ns] 728B ...\n",
+       "  * Time            (Time) datetime64[ns] 728B 2019-04-04T12:00:00 ... 2019-0...\n",
+       "  * y_stag          (y_stag) float64 2kB -1.25e+04 -1.24e+04 ... 1.25e+04\n",
+       "  * y               (y) float64 2kB -1.245e+04 -1.235e+04 ... 1.245e+04\n",
+       "  * x_stag          (x_stag) float64 2kB -1.25e+04 -1.24e+04 ... 1.25e+04\n",
+       "  * x               (x) float64 2kB -1.245e+04 -1.235e+04 ... 1.245e+04\n",
+       "Dimensions without coordinates: z, z_stag\n",
+       "Data variables: (12/179)\n",
+       "    Times           (Time) |S19 2kB b'2019-04-04_12:00:00' ... b'2019-04-05_0...\n",
+       "    CST_CLDLOW      (Time) float32 364B ...\n",
+       "    CST_CLDTOT      (Time) float32 364B ...\n",
+       "    CST_LWP         (Time) float32 364B ...\n",
+       "    CST_IWP         (Time) float32 364B ...\n",
+       "    CST_PRECW       (Time) float32 364B ...\n",
+       "    ...              ...\n",
+       "    CSV_IWC         (Time, z, y, x) float32 5GB ...\n",
+       "    CSV_CLDFRAC     (Time, z, y, x) float32 5GB ...\n",
+       "    CSS_LWP         (Time, y, x) float32 23MB ...\n",
+       "    CSS_IWP         (Time, y, x) float32 23MB ...\n",
+       "    CSS_CLDTOT      (Time, y, x) float32 23MB ...\n",
+       "    CSS_CLDLOW      (Time, y, x) float32 23MB ...\n",
+       "Attributes: (12/96)\n",
+       "    TITLE:                                  OUTPUT FROM WRF V3.8.1 MODEL\n",
+       "    START_DATE:                            2019-04-04_12:00:00\n",
+       "    WEST-EAST_GRID_DIMENSION:              251\n",
+       "    SOUTH-NORTH_GRID_DIMENSION:            251\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:             227\n",
+       "    DX:                                    100.0\n",
+       "    ...                                    ...\n",
+       "    config_aerosol:                        NA\n",
+       "    config_forecast_time:                  15.0 h\n",
+       "    config_boundary_method:                Periodic\n",
+       "    config_microphysics:                   Thompson (mp_physics=8)\n",
+       "    config_nickname:                       runlas20190404v1msda2d150km\n",
+       "    simulation_origin_host:                cumulus-login2.ccs.ornl.gov"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load Data\n",
+    "# To recap, wrfstat is 10 minutes-average values, wrfout is a snapshot at a given time.\n",
+    "\n",
+    "path_shcu_root = \"/data/project/ARM_Summer_School_2024_Data/lasso_tutorial/ShCu/untar\"  # on Jupyter\n",
+    "\n",
+    "case_date = datetime(2019, 4, 4)\n",
+    "\n",
+    "sim_id2 = 6\n",
+    "sim_id3 = 7\n",
+    "sim_id4 = 8\n",
+    "\n",
+    "ds_stat6 =  xr.open_dataset(f\"{path_shcu_root}/{case_date:%Y%m%d}/sim{sim_id2:04d}/raw_model/wrfstat_d01_{case_date:%Y-%m-%d_12:00:00}.nc\")\n",
+    "ds_stat7 =  xr.open_dataset(f\"{path_shcu_root}/{case_date:%Y%m%d}/sim{sim_id3:04d}/raw_model/wrfstat_d01_{case_date:%Y-%m-%d_12:00:00}.nc\")\n",
+    "ds_stat8 =  xr.open_dataset(f\"{path_shcu_root}/{case_date:%Y%m%d}/sim{sim_id4:04d}/raw_model/wrfstat_d01_{case_date:%Y-%m-%d_12:00:00}.nc\")\n",
+    "\n",
+    "ds_stat6 = ds_stat6.xwrf.postprocess()\n",
+    "ds_stat7 = ds_stat7.xwrf.postprocess()\n",
+    "ds_stat8 = ds_stat8.xwrf.postprocess()\n",
+    "\n",
+    "ds_stat7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3929a911-5ff5-4a8b-a275-5fdab4855905",
+   "metadata": {},
+   "source": [
+    "# Compare cloud core fraction with the observation (COG)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4f095b16-8449-4f60-b477-728509685074",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "sim_id2 = 6\n",
+    "ds_cogs6 = xr.open_dataset(f\"{path_shcu_root}/{case_date:%Y%m%d}/sim{sim_id2:04d}/obs_model/sgplassocogsdiagobsmod{sim_id2}C1.m1.{case_date:%Y%m%d}.120000.nc\")\n",
+    "\n",
+    "sim_id3 = 7\n",
+    "ds_cogs7 = xr.open_dataset(f\"{path_shcu_root}/{case_date:%Y%m%d}/sim{sim_id3:04d}/obs_model/sgplassocogsdiagobsmod{sim_id3}C1.m1.{case_date:%Y%m%d}.120000.nc\")\n",
+    "\n",
+    "sim_id4 = 8\n",
+    "ds_cogs8 = xr.open_dataset(f\"{path_shcu_root}/{case_date:%Y%m%d}/sim{sim_id4:04d}/obs_model/sgplassocogsdiagobsmod{sim_id4}C1.m1.{case_date:%Y%m%d}.120000.nc\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4b0012be-69b3-4a25-94eb-25ee481d952b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lUuHr68v48ePp2rVruWUzo5tvvpkDBw6YvU2fPt3iMVeHtV9nS6/ZoUMHOnbsaPb43XffbdV1vvnmm3Kfj9IJ3pV9PT/77DO6dOmCi4sLSqUSlUrFli1bzL4X/vjjD1xcXMotvVdXdX4ulU6BAMP3S35+PklJSTYZU20SwY0D+fv74+bmZpoWvp6UlBTAELSUFRoaanreqFGjRuWOU6vV5R5Xq9UA5Ofnlzs+ODjY7H2lUomfn5/pXtnZ2fTp04d9+/bx+uuvs337dg4cOMCvv/4KUG4pxc3NzbTDpyp9+/Zl9erVFBUVcd9999G4cWMiIiL44YcfTMekpKRU+rkwPl+an5+f2fvGHCfjGGUyGVu2bGHIkCEsWLCALl26EBAQwBNPPFHl2vz1vi56vb5GO0PatWvHk08+yXfffcelS5f44IMPSElJ4eWXX77uuZV9ra15DVTHU089xcsvv8zYsWP5/fff2bdvHwcOHKBjx47VWl4D61//Zb/eYPial77/nDlzeO+999i7dy/Dhg3Dz8+PgQMHcvDgwWqP0ZrXZEXHVsYYrGzbto0jR46Qnp5u+iOiffv2BAQEsH37dvbu3Vtlvs1ff/3FgQMH+PPPPxk3bhw7duzg8ccfr/BYb29vunXrZvZ2vd1BTZs2JTk5uVzOnaWs/Tpbes2yP8ug/M+362nXrl25z0dpFY35gw8+4NFHH6VHjx788ssv7N27lwMHDjB06FCz12JycjKhoaFmgUhNVOfn0vV+RtYnIrhxIIVCwcCBAzl06FC5RLmKGF948fHx5Z6Li4vD39/f5mMsW/+iqKiIlJQU01i2bt1KXFwcy5YtY+rUqfTt25du3brh6elZ4fWsqWcxZswYtmzZQkZGBtu3b6dx48bcfffdpr9+/fz8Kv1cANX6fISHh7N06VISEhI4c+YMs2bNYsmSJTzzzDOVnnO9r4tcLsfX19fqsVREJpMxa9YsfHx8OHnypE2uWRkXFxcKCgrKPW5JzsN3333Hfffdx5tvvsmQIUPo3r073bp1q1a+hJE9Xv9KpZKnnnqKw4cPk5qayg8//MDly5cZMmRItWbbrH1NWvP9EBERYQpgtm/fTlBQkNm25L59+7Jt2zZTHkdlwU3Hjh3p1q0bgwcP5ueff2bQoEF88cUXHDhwwOKxVGXIkCHodDp+//33ap1vzdfZ0teon59fhbV8KnqsJir6en733Xf079+fTz/9lBEjRtCjRw+6detW7g+mgIAA4uLiLJottkRt/lyqi0Rw42Bz5sxBkiSmTZuGVqst93xhYaHph8Qtt9wCYJYQCXDgwAFOnz5t2nlkSytWrDB7/6effqKoqMg0HW78Zi670+vzzz+32Rg0Gg39+vXjnXfeAQz1XwAGDhzIqVOnOHz4sNnx33zzDTKZzKJkyqq0bt2al156icjIyHL3KK1NmzaEhYXx/fffmyXk5uTk8Msvv5h2Kliroh9KYPjBlJmZaff6Gs2aNTMVgDM6e/asRcthMpms3Gti/fr15ZYXrfnL0N6vfx8fH+644w5mzJhBamoqsbGxVl9j4MCBpoC/tG+++QY3Nzduuummao9PJpPRr18/du/ezebNm02zNkb9+vXj77//Ztu2bYSGhlZaP6nsNT/55BMUCgUvvfRStcdW2pQpUwgODubZZ5+tcDkZMM3sVsSar7Olr9EBAwbw77//cuzYMbPHv//+++t/QDVU0ffC8ePHyy1RDhs2jPz8/OsWwyw7+1gZe/1cqi9EET8H69mzJ59++inTp0+na9euPProo3To0IHCwkKOHDnCF198QUREBKNGjaJNmzY89NBDfPTRR8jlcoYNG0ZsbCwvv/wyTZo0sUtW+6+//opSqWTQoEH8+++/vPzyy3Ts2JHx48cD0KtXL3x9fXnkkUeYN28eKpWKFStWlPshYq25c+dy5coVBg4cSOPGjUlPT2fx4sVm+TyzZs3im2++YcSIEcyfP5/w8HDWr1/PkiVLePTRRy364V7a8ePHeeyxx7jzzjtp1aoVarWarVu3cvz4cZ5//vlKz5PL5SxYsIB77rmHkSNH8vDDD1NQUMC7775Leno6b7/9drU+Bw899BDp6emMGzeOiIgIFAoF0dHRLFy4ELlcznPPPVet61pq0qRJ3HvvvUyfPp1x48Zx8eJFFixYQEBAwHXPHTlyJMuXL6dt27ZERUVx6NAh3n333XJbg2+44QZcXV1ZsWIF7dq1w8PDg9DQ0AoDN3u8/keNGkVERATdunUjICCAixcvsmjRIsLDw2nVqpXV15s3bx7r1q1jwIABzJ07l0aNGrFixQrWr1/PggUL8Pb2tvqapQ0YMIBVq1axadMmPv74Y7Pn+vXrR0pKCjt27LAql6RVq1Y89NBDLFmyhF27dpnKFVSXt7c3a9asYeTIkXTu3NmsiN+5c+f47rvvOHbsGLfffnuF51vzdbb0Nfrkk0+ybNkyRowYweuvv05QUBArVqwgOjq6Rh+rJUaOHMlrr73GvHnz6NevH2fOnGH+/Pk0b97cbLfVxIkT+eqrr3jkkUc4c+YMAwYMQK/Xs2/fPtq1a8eECRMAiIyMZPv27fz++++EhITg6elJmzZtyt3XXj+X6g3H5jMLRkePHpXuv/9+qWnTppJarZbc3d2lzp07S3PnzjXbGaPT6aR33nlHat26taRSqSR/f3/p3nvvLVcsq1+/flKHDh3K3aeywlyU2flhzJ4/dOiQNGrUKMnDw0Py9PSUJk6cKCUmJpqdu3v3bqlnz56Sm5ubFBAQIE2dOlU6fPhwuV0w999/v+Tu7l7hx192t8+6deukYcOGSWFhYZJarZYCAwOl4cOHSzt37jQ77+LFi9Ldd98t+fn5SSqVSmrTpo307rvvmu0OMO7meffddyv8uOfNmydJkiQlJiZKkydPltq2bSu5u7tLHh4eUlRUlLRw4UKzXVaVWb16tdSjRw/JxcVFcnd3lwYOHCj9888/ZsdYs1vqzz//lB588EGpffv2kre3t6RUKqWQkBDp9ttvNyvmJkmV75Yqu5unss9FRePS6/XSggULpBYtWkguLi5St27dpK1bt1q0WyotLU2aMmWKFBgYKLm5uUk333yztHPnznLnSpIk/fDDD1Lbtm0llUpl9vWoqLhbTV//ZT9P77//vtSrVy/J399fUqvVUtOmTaUpU6ZIsbGx5c4tq6LPryRJ0okTJ6RRo0ZJ3t7eklqtljp27FhuN5g1r4PSTp06Zdqlc/LkSbPn9Hq91KhRo0p33FRWxE+SDK99Dw8PacCAAabHqlvEzyghIUF67rnnpA4dOkhubm6SRqORWrZsKT388MPSiRMnyo2rNEu/zpa+RiXJ8LkbNGiQ5OLiIjVq1EiaMmWKtGbNGpsU8avq61lQUCDNnj1bCgsLk1xcXKQuXbpIq1evrvB7Ni8vT5o7d67UqlUrSa1WS35+ftItt9wi7d6923TM0aNHpd69e0tubm4SYPo4KytIaMnPpcpeG8aPOyYmpsrPT10kkyQbFbYQnMorr7zCq6++SnJysl1yeQRBEATBXkTOjSAIgiAITkUEN4IgCIIgOBWxLCUIgiAIglMRMzeCIAiCIDgVEdwIgiAIguBURHAjCIIgCIJTaXBF/PR6PXFxcXh6elpV+lwQBEEQBMeRJImsrCyLenA1uOAmLi6uXMdeQRAEQRDqh8uXL5erdl5WgwtujA0dL1++bFF3akEQBEEQHC8zM5MmTZpU2pi5tAYX3BiXory8vERwIwiCIAj1jCUpJSKhWBAEQRAEpyKCG0EQBEEQnIoIbgRBEARBcCoNLudGEARBcE46nY7CwkJHD0OoAbVafd1t3pYQwY0gCIJQr0mSREJCAunp6Y4eilBDcrmc5s2bo1ara3QdEdwIgiAI9ZoxsAkMDMTNzU0UaK2njEV24+Pjadq0aY2+jiK4EQRBEOotnU5nCmz8/PwcPRyhhgICAoiLi6OoqAiVSlXt64iEYkEQBKHeMubYuLm5OXgkgi0Yl6N0Ol2NriOCG0EQBKHeE0tRzsFWX0cR3AiCIAiC4FREcCMIgiAIdZBMJmP16tWOHka9JIIbwWp6vYQkSWaPSZKEXi9VcoYgXJ94XQkNTVJSEg8//DBNmzZFo9EQHBzMkCFD2LNnDwDx8fEMGzasxvdJT09nxowZhISE4OLiQrt27diwYUOV58hksgrf3n33XdMx/fv3L/f8hAkTajxeWxC7pQSrLdpyjnOJWbw2NgJ/Dw3Xsgt4efVJWgd5MmtQa0cPT6injK+r+WM60MhdQ1quVryuBKc2btw4CgsL+frrr2nRogWJiYls2bKF1NRUAIKDg2t8D61Wy6BBgwgMDGTVqlU0btyYy5cvX7ezdnx8vNn7f/zxB1OmTGHcuHFmj0+bNo358+eb3nd1da3xmG1BBDeC1doEefLd3ov0XbCNJr5uJGXlI5PJGBkV6uihCfVYyetqO/mFOjxclKgUcvG6EuxOr5eQycyTWSVJQpJALrdPonJ6ejq7du1i+/bt9OvXD4Dw8HC6d+9uOkYmk/Hbb78xduxYYmNjad68OStXruSjjz7i4MGDREREsGLFCjIyMnj00UeJjo7m5ptv5ttvvyUgIACAZcuWkZqayu7du01bq8PDw687vrKB1Zo1axgwYAAtWrQwe9zNzc0mQZitiWUpwWojokL444mbydPqOJOYRbC3K5tm9WVEVIijhybUYyOiQtg0qy8yGUiAq0ohXldCtUiSRK62yOK3dzed4eFvD3E5NYdcbRGXU3N4+NtDvLfpjFXXKbusWhUPDw88PDxYvXo1BQUFFp83b948XnrpJQ4fPoxSqWTixIk8++yzLF68mJ07d3L+/Hnmzp1rOn7t2rX07NmTGTNmEBQUREREBG+++aZVW60TExNZv349U6ZMKffcihUr8Pf3p0OHDsyePZusrCyLr2tPYuZGqBY9hl9AAH1b+ePvoXHkcAQn4eOqolCnB8DfQy1eV0K15BXqaD/3T6vP23Qqsdz7S7aft/j8U/OH4Ka27NeqUqlk+fLlTJs2jc8++4wuXbrQr18/JkyYQFRUVKXnzZ49myFDhgAwc+ZMJk6cyJYtW+jduzcAU6ZMYfny5abjL1y4wNatW7nnnnvYsGED586dY8aMGRQVFZkFQVX5+uuv8fT05Pbbbzd7/J577qF58+YEBwdz8uRJ5syZw7Fjx9i8ebNF17UnMXMjVEt8Rr7p///GZTpwJIIziU3JpVBnCJvPJ+dQVBzoCIIzGjduHHFxcaxdu5YhQ4awfft2unTpYhaclFU68AkKCgIgMjLS7LGkpCTT+3q9nsDAQL744gu6du3KhAkTePHFF/n0008Bw8yLcRbJw8ODnTt3lrvnsmXLuOeee3BxcTF7fNq0adx6661EREQwYcIEVq1axV9//cXhw4er9fmwJTFzI1RLQqng5mRcBpIkiSJaQo2dji8JlAuK9JxPzqFNcNWJj4JQlqtKwan5Q6w+71RcJnd8todVj/SkfahXte5rLRcXFwYNGsSgQYOYO3cuU6dOZd68eUyePLnC40u3JDD+zC37mF5f8kdBSEgIKpUKhaJkbO3atSMhIQGtVsvo0aPp0aOH6bmwsDCz++3cuZMzZ86wcuXK634sXbp0QaVSce7cObp06XLd4+1JBDdCtcSl55n+n55byMWUXJr5uztwRIIziE4wnwU8diVdBDeC1WQymcXLQ6W5FAcnLipFtc63hfbt29u0tk3v3r35/vvv0ev1yOWGxZqzZ88SEhKCWq1GrVZXuXNq6dKldO3alY4dO173Xv/++y+FhYWEhDg+T04sSwnVUnrmBmB/bKqDRiI4k9PxhmRELxfDL5bjV9IdOBpBsJ+UlBRuueUWvvvuO44fP05MTAw///wzCxYsYMyYMTa7z6OPPkpKSgozZ87k7NmzrF+/njfffJMZM2Zc99zMzEx+/vlnpk6dWu658+fPM3/+fA4ePEhsbCwbNmzgzjvvpHPnzqb8H0cSMzdCtcRnGoIbTxclWflFHIhJZXy3Jg4elVDfRRcvS93WOYyv91zkxJUMB49IaEgCPTXMHNiKQE/7J7J7eHjQo0cPFi5cyPnz5yksLKRJkyZMmzaNF154wWb3adKkCZs2bWLWrFlERUURFhbGzJkzee6556577o8//ogkSUycOLHcc2q1mi1btrB48WKys7Np0qQJI0aMYN68eWZLYI4ik6zZu+YEMjMz8fb2JiMjAy8v69dUBYNxn+7m0MU0xndrzE8HrxDu58bfzwxw9LCEeiw9V0un+YZdFusev5mRH+1CrZBz8tUhqJViklmoWH5+PjExMTRv3rxcwqtQ/1T19bTm97f4iSFUi3FZamRUKDIZXEzJJSkz/zpnCULlohMMS1JhPq50CPXCx02FVqcvl4cjCIJwPSK4Eaym00skFgcyrYM8aRdsiKBF3o1QE8adUu1CvJDJZESGeQNwTCxNCYJgJRHcCFZLyS6gSC+hkMsI8NTQvXkjAA7EiOBGqL7o4mTidiGGnRsdG/sAcEIkFQuCYCUR3AhWiytekgr01KCQy7ixmSG42SeCG6EGTieUzNwARDU2zNwcFzM3giBYSQQ3gtUSMgw1bkK8DcleNzb3BeBMYhYZeYUOG5dQf+n0EmeKc27aFte1iSqeuTmbmEWutshRQxMEoR4SwY1gNWPrhRBvQ2v7QE8Xmvu7I0lw6KKYvRGsF3Mth4IiPa4qBeF+hmKQwd4uBHpq0EuGyrGCIAiWEsGNYDXjTqlg75Jtejc2M8zeiKUpoTqMO6JaB3uikJe08TDO3oikYkEQrOHw4GbJkiWm/exdu3atsGlXaStWrKBjx464ubkREhLCAw88QEpKSi2NVoDSMzelgxuRVCxUn3GnVPsQ8zLwJXk36bU9JEEQ6jGHBjcrV67kySef5MUXX+TIkSP06dOHYcOGcenSpQqP37VrF/fddx9Tpkzh33//5eeff+bAgQMVloYW7KeimZsezf0AOHE1g/xCnUPGJdRfxp1SbYPNC3MZgxtRqVgQBGs4NLj54IMPmDJlClOnTqVdu3YsWrSIJk2amFqxl7V3716aNWvGE088QfPmzbn55pt5+OGHOXjwYC2PvGGLK5NQDNCkkStBXhoKdRJHLqU7aGRCfVW6xk1pxmWpC9dyRLK60ODIZDKbNtFsSBwW3Gi1Wg4dOsTgwYPNHh88eDC7d++u8JxevXpx5coVNmzYgCRJJCYmsmrVKkaMGFHpfQoKCsjMzDR7E6pPX6qAX3BxQjEYvgmNS1P7xdKUYIWM3EJTeYGyHcAbuatp7Gt4nZ28KmZvBOeSlJTEww8/TNOmTdFoNAQHBzNkyBD27NkDQHx8PMOGDavRPfr3749MJiv3VtXvTYBff/2VIUOG4O/vj0wm4+jRoxZde8KECWbHpKWlMWnSJLy9vfH29mbSpEmkp6fX6GOyhMOCm2vXrqHT6QgKCjJ7PCgoiISEhArP6dWrFytWrOCuu+5CrVYTHByMj48PH330UaX3eeutt0yfVG9vb5o0Ec0dayIlR0uhTkIuo1xzOVMxP1GpWLCCsb5NmI8r3q6qcs8bi/mJejeCsxk3bhzHjh3j66+/5uzZs6xdu5b+/fuTmmr4GRocHIxGU7Mmnr/++ivx8fGmt5MnT6JQKLjzzjurPC8nJ4fevXvz9ttvV3nctGnTzK7/+eefmz1/9913c/ToUTZu3MjGjRs5evQokyZNqtHHZAmHJxTLZDKz9yVJKveY0alTp3jiiSeYO3cuhw4dYuPGjcTExPDII49Uev05c+aQkZFhert8+bJNx9/QGPNtAjw1qBTmLx9jcHP4UhpFOn2tj02on6JNS1KeFT4vkooFu9ProWwPaUkyPG4n6enp7Nq1i3feeYcBAwYQHh5O9+7dmTNnjmlWpfSyVGxsLDKZjJ9++ok+ffrg6urKjTfeyNmzZzlw4ADdunXDw8ODoUOHkpycbLpPo0aNCA4ONr1t3rwZNze36wY3kyZNYu7cudx6661VHufm5mZ2fW9vb9Nzp0+fZuPGjXz55Zf07NmTnj178r///Y9169Zx5syZan7mLOOw4Mbf3x+FQlFuliYpKancbI7RW2+9Re/evXnmmWeIiopiyJAhLFmyhGXLlhEfH1/hORqNBi8vL7M3ofrii/NtSi9JGbUO9MTbVUWuVse/oi6JYKHTprYLFX9vRopKxYK1JAm0OZa/bZ0PP94DaRcN76ddNLy/9TXrrlM2QKqCh4cHHh4erF69moKCAovPmzdvHi+99BKHDx9GqVQyceJEnn32WRYvXszOnTs5f/48c+fOrfT8pUuXMmHCBNzd3S2+Z1VWrFiBv78/HTp0YPbs2WRlZZme27NnD97e3vTo0cP02E033YS3t3el6Se2orTr1augVqvp2rUrmzdv5rbbbjM9vnnzZsaMGVPhObm5uSiV5kNWKBSAYcZHsL+E4nybEC+Xcs/J5TK6hfuyJTqJ/TGpdGziU8ujE+ojY42bsjuljCLDvJHJ4Gp6HteyC/D3qNk0vdAAFObCm6HWn3dmffn3d31g+fkvxIHasqBBqVSyfPlypk2bxmeffUaXLl3o168fEyZMICoqqtLzZs+ezZAhQwCYOXMmEydOZMuWLfTu3RuAKVOmsHz58grP3b9/PydPnmTp0qWWf0xVuOeee2jevDnBwcGcPHmSOXPmcOzYMTZv3gxAQkICgYGB5c4LDAysNP3EVhy6LPXUU0/x5ZdfsmzZMk6fPs2sWbO4dOmSaZlpzpw53HfffabjR40axa+//sqnn37KhQsX+Oeff3jiiSfo3r07oaHVeCELVouvYBt4aTcWL02JDuGCJXR6iTOJ5g0zy/J0UdHC3/ALQ2wJF5zJuHHjiIuLY+3atQwZMoTt27fTpUuXSoMTwCzwMa5yREZGmj2WlJRU4blLly4lIiKC7t27mx5bsWKFaRbJw8PjurXmSps2bRq33norERERTJgwgVWrVvHXX39x+PBh0zEVpZlUlX5iKw6buQG46667SElJYf78+cTHxxMREcGGDRsIDw8HDJnipWveTJ48maysLD7++GOefvppfHx8uOWWW3jnnXcc9SE0OPHp5beBl2bMuzkYm4peLyGX2/cFLNRvsSk55BfqcVHJTW0XKtKxsQ/nk3M4diWdAW3L/yUoCGZUboZZFGslHIdlQ+HBjRBc+exJlfe1kouLC4MGDWLQoEHMnTuXqVOnMm/ePCZPnlzxLVQlSffGAKHsY/oKcoVyc3P58ccfmT9/vtnjo0ePNls2CgsLs/pjMOrSpQsqlYpz587RpUsXgoODSUxMLHdccnJypekntuLQ4AZg+vTpTJ8+vcLnKopeH3/8cR5//HE7j0qozPVmbiJCvXFRyUnLLeR8cjatgir+a1wQoKS+TZtgL7O2C2VFNvbm1yNXxcyNYBmZzOLlITNK15J/q3O+DbRv394utW1++uknCgoKuPfee80e9/T0xNPTNj+n//33XwoLCwkJCQGgZ8+eZGRksH//ftNs0b59+8jIyKBXr142uWdlHB7cCPWLMecm1Kd8QjGAWimncxNf9lxIYV9MqghuhCoZKxO3C676dVK6x1RtTGkLgr2lpKRw55138uCDDxIVFYWnpycHDx5kwYIFlead1sTSpUsZO3Ysfn5+Fh2fmprKpUuXiIszzIAZdzcZd0WdP3+eFStWMHz4cPz9/Tl16hRPP/00nTt3NuX/tGvXjqFDhzJt2jTTFvGHHnqIkSNH0qZNG5t/jKU5fCu4UH9IklQyc1NBQrHRjaLejWChyioTl9Uh1DCzcy27wPQaFASb8wyGfs8b/rUzDw8PevTowcKFC+nbty8RERG8/PLLTJs2jY8//tim9zp79iy7du1iypQpFp+zdu1aOnfubNqWPmHCBDp37sxnn30GGDYFbdmyhSFDhtCmTRueeOIJBg8ezF9//WXa6AOGnJ7IyEgGDx7M4MGDiYqK4ttvv7Xpx1cRmdTAthllZmbi7e1NRkaG2BZupdQcLV1eM2TBn319GGplxbHxP/9d454v9xHq7cLuOQNrc4hCPdP77a1cTc9j5UM30aNF1X9RDlu8k9PxmXx2b1eGRtj/l49QP+Tn5xMTE2NqwCzUb1V9Pa35/S1mbgSLGWvc+HtoKg1sADo39UEplxGXkc+VtNzaGp5Qz2TkFnK1OEG97XVmbgA6imJ+giBYSAQ3gsXi04tr3FSSTGzkplbSIczwi0j0mRIqE32dtgtlRYk2DIIgWEgEN4LF4jOr3ilVWvdmvoDIuxEqd/o6bRfKKt2GoYGtpguCYCUR3AgWSyhelgq1JLhpbsifEDM3QmWiEww7pSqrTFxWm2BP1Eo5mflFXEwRy52CIFROBDeCxUpq3FS8Dby0buGGmZvzyTlcy7a8b4rQcFi6U8pIpZDTvvjYYyLvRhCEKojgRrCYsSP49XJuAHzd1bQO8gAM1YoFobTSbRfaWrgsBSVLU6KYnyAIVRHBjWCxhOtUJy7rxmbFfaZi0uw2JqF+Kt12oVkVbRfKEknFgiBYQgQ3gkVKF/CzZOYGSvpMiaRioSxjZeI2QZ5Vtl0oy7gd/GRcBjq9SCoWBKFiIrgRLJKRV0heoQ6AoCqqE5dmDG7+jcsgu6DIbmMT6h9r822MWgR44KZWkKvVcT452x5DEwTBCYjgRrCIcdamkbsaF5XiOkcbhHi70tjXFb0Ehy6KpSmhhLHGTdvr9JQqSyGXEVFcQ+nY5XRbD0sQ6hSZTGaXJpoNgQhuBItYk0xcWvfivJsDYku4UMppY8NMK2duoHSlYpF3I9RvSUlJPPzwwzRt2hSNRkNwcDBDhgxhz549AMTHxzNs2LAa32fRokW0adMGV1dXmjRpwqxZs8jPr7pH2yuvvELbtm1xd3fH19eXW2+9lX379pkdU1BQwOOPP46/vz/u7u6MHj2aK1eumB2TlpbGpEmT8Pb2xtvbm0mTJpGenl7jj+l6RHAjWMTafBsj49LUfpF3IxTLyCvVdsHCGjelRZqSitNtOCpBqH3jxo3j2LFjfP3115w9e5a1a9fSv39/UlMNPy+Dg4PRaDQ1useKFSt4/vnnmTdvHqdPn2bp0qWsXLmSOXPmVHle69at+fjjjzlx4gS7du2iWbNmDB48mOTkZNMxTz75JL/99hs//vgju3btIjs7m5EjR6LT6UzH3H333Rw9epSNGzeyceNGjh49yqRJk2r0MVlEamAyMjIkQMrIyHD0UOqV9/+MlsKfWye9+Ntxq877LylLCn9undTqxQ1SfmGRnUYn1Cf7LqRI4c+tk3q9taVa58deyza8pl7YIBUU6mw8OqG+ycvLk06dOiXl5eVV+xo6vU7S6/Vmj+n1ekmnt9/rKy0tTQKk7du3V3oMIP3222+SJElSTEyMBEgrV66Ubr75ZsnFxUXq1q2bdObMGWn//v1S165dJXd3d2nIkCFSUlKS6RozZsyQbrnlFrPrPvXUU9LNN99s1XiNvzv/+usvSZIkKT09XVKpVNKPP/5oOubq1auSXC6XNm7cKEmSJJ06dUoCpL1795qO2bNnjwRI0dHRFd6nqq+nNb+/xcyNYJGSmZvrF/ArrYW/O/4earRFerGMIAAlycTW5tsYNW3kho+bCq1Oz5niKseCUJokSeQW5lr89uHhD5m5bSZXsq6QW5jLlawrzNw2k48Of2TVdSQr2oJ4eHjg4eHB6tWrKSiwvNDpvHnzeOmllzh8+DBKpZKJEyfy7LPPsnjxYnbu3Mn58+eZO3eu6fibb76ZQ4cOsX//fgAuXLjAhg0bGDFihMX31Gq1fPHFF3h7e9OxY0cADh06RGFhIYMHDzYdFxoaSkREBLt37wZgz549eHt706NHD9MxN910E97e3qZj7EVp16sLTsNUndjCnVJGMpmMbuGN2PhvAvtjUk21b4SGy5hMXJ18GzC8piLDvNl57hrHrqQTWZyDIwhGeUV59Pi+x/UPLGPb5W3l3v/y5JcWn7/v7n24qdwsOlapVLJ8+XKmTZvGZ599RpcuXejXrx8TJkwgKiqq0vNmz57NkCFDAJg5cyYTJ05ky5Yt9O7dG4ApU6awfPly0/ETJkwgOTmZm2++GUmSKCoq4tFHH+X555+/7hjXrVvHhAkTyM3NJSQkhM2bN+Pv7w9AQkICarUaX19fs3OCgoJISEgwHRMYGFjuuoGBgaZj7EXM3AgWiS/uK2Vtzg2IejeCuVPx1lcmLktUKhacwbhx44iLi2Pt2rUMGTKE7du306VLF7PgpKzSgU9QUBAAkZGRZo8lJSWZ3t++fTtvvPEGS5Ys4fDhw/z666+sW7eO1157DTDk5BhnkTw8PNi5c6fp3AEDBnD06FF2797N0KFDGT9+vNm1KyJJEjJZSe2q0v+v7Bh7EDM3wnVJpQv4+Vi3LAUlwc2h2DR0esmqom2Cc9HpJc4mVH+nlJGxUrHoMSVUxFXpyr67913/wDKiU6O5f+P9fD30a9o2alut+1rLxcWFQYMGMWjQIObOncvUqVOZN28ekydPrvB4lUpl+r8xQCj7mF6vN73/8ssvM2nSJKZOnQoYAqGcnBweeughXnzxRUaPHm22bBQWFmb6v7u7Oy1btqRly5bcdNNNtGrViqVLlzJnzhyCg4PRarWkpaWZzd4kJSXRq1cvwJAQnZiYWO5jSE5ONgVm9iKCG+G6sgqKyNUast+tXZYCwy8xD42SrIIiTsdnmuqUCA3PxZQc8gp1VrddKKtjcXBzLimbPK0OV7VltZeEhkEmk1m8PFSai9LF9G91zreF9u3b27S2TW5uLnK5+SKNQqFAkiQkScLT0xNPT8tmUSVJMuUHde3aFZVKxebNmxk/fjxg2Lp+8uRJFixYAEDPnj3JyMhg//79dO/eHYB9+/aRkZFhCoDsRQQ3wnUZa9z4uKmq9UtEIZfRJdyXHWeTORCbKoKbBiw6oXptF8oK8tIQ4KkhOauAU/EZdA0XuVxC/ZKSksKdd97Jgw8+SFRUFJ6enhw8eJAFCxYwZswYm91n1KhRfPDBB3Tu3JkePXrw33//8fLLLzN69GgUiop/nufk5PDGG28wevRoQkJCSElJYcmSJVy5coU777wTAG9vb6ZMmcLTTz+Nn58fjRo1Yvbs2URGRnLrrbcC0K5dO4YOHcq0adP4/PPPAXjooYcYOXIkbdq0sdnHWBER3AjXVd1k4tJ6NG9kCm4e6N3cVkMT6pmSnVLVX5ICw1/mHRt789fpJI5dFsGNYBsBrgE82vFRAlwD7H4vDw8PevTowcKFCzl//jyFhYU0adKEadOm8cILL9jsPi+99BIymYyXXnqJq1evEhAQwKhRo3jjjTcqPUehUBAdHc3XX3/NtWvX8PPz48Ybb2Tnzp106NDBdNzChQtRKpWMHz+evLw8Bg4cyPLly82CphUrVvDEE0+YdlWNHj2ajz/+2GYfX2VkkjV715xAZmYm3t7eZGRk4OVVsx+wDcXKA5d47pcTDGgTwFcPdK/WNfbHpDL+8z34e2g48OJAuyeTCXXT1K8P8tfpRF4Z1Z7JNQxyP9xyjg82n2Vsp1AWTehsoxEK9U1+fj4xMTE0b94cF5fq/wEm1A1VfT2t+f0tdksJ1xWXXjxzY2WNm9KiGnujVsi5ll1AzLUcWw1NqGdMMzc1SCY2Mm4BP35V7JgSBMGcCG6E66puX6nSXFQKOjYx/DISW8Ibpsz8krYL7Wq4LAUlScUXknPIzC+s8fUEQXAeIrgRris+s+bBDZTqMxUjOoQ3RNHF9W1CvV3wdlNd5+jra+SuprGvYTbxpKh3IwhCKSK4Ea4rwVTAr/rLUoCpOrGYuWmYalqZuCJRYmlKEIQKiOBGuC7Tbqkaztx0DfdFLoNLqbmmpS6h4SjJt6l+ZeKyokSHcEEQKiCCG6FK2QVFZOUXATUPbjxdVKa/2veL2ZsG53R8zSsTl2WcuTl2WczcCIJQQgQ3QpWMS1KeLko8NDUvi2TqMxUjgpuGRKeXTB28a1rjpjRjQcir6XmkZFveWVkQBOcmghuhSvE22ClVWneRd9MgXUrNJa9Qh0Ypp7l/9dsulOXloqJFgOF6Iu9GEAQjEdwIVSoJbmqWTGzUrTi4iU7IIj1Xa5NrCnWfMd+mTXDN2i5UxLgl/LhYmhIEoZgIboQq2aLGTWkBnhpaFP/lfjBWbAlvKKKLgxtb1LcpK7J4aerE1XSbX1sQhPpJBDdClWy1U6o0U96NWJpqME4VJxPbcqeUkbE45LErGTSwbjKCE0hISODxxx+nRYsWaDQamjRpwqhRo9iyZYvpmN27dzN8+HB8fX1xcXEhMjKS999/H51OV+5627ZtY+TIkQQEBODi4sINN9zAXXfdxY4dOywaz7p16+jfvz+enp64ublx4403snz5crNjYmNjkclkpje1Wk3Lli15/fXXzb4HL1y4wMSJEwkNDcXFxYXGjRszZswYzp49W71PlhVEcCNUqaTGje2CG2O9G7FjquGwR40bo/Yh3ijkMpKzCkjIFCUGhPojNjaWrl27snXrVhYsWMCJEyfYuHEjAwYMYMaMGQD89ttv9OvXj8aNG7Nt2zaio6OZOXMmb7zxBhMmTDALJpYsWcLAgQPx8/Nj5cqVnD59mm+//ZZevXoxa9as647no48+YsyYMfTq1Yt9+/Zx/PhxJkyYwCOPPMLs2bPLHf/XX38RHx/PuXPnePXVV3njjTdYtmwZAFqtlkGDBpGZmcmvv/7KmTNnWLlyJREREWRk1MISstTAZGRkSICUkZHh6KHUC0MW/i2FP7dO2n4myWbXvJSSI4U/t066Yc56Kaeg0GbXFeqmjDytFP7cOin8uXVSeo7WLvcYumiHFP7cOmnjyXi7XF+ou/Ly8qRTp05JeXl51b6GXqeT9Hq9+WN6vaTX6Wo6vCoNGzZMCgsLk7Kzs8s9l5aWJmVnZ0t+fn7S7bffXu75tWvXSoD0448/SpIkSRcvXpRUKpU0a9asCu9V9uMr69KlS5JKpZKeeuqpcs99+OGHEiDt3btXkiRJiomJkQDpyJEjZsfdcsst0vTp0yVJkqQjR45IgBQbG1vlfcuq6utpze9vMXMjVMnWu6UAGvu6EuzlQpFe4uildJtdV6ibjFvAbdV2oSJRxXk3opifACBJEvrcXIvfkhcu4spjj6O9cgV9bi7aK1e48tjjJC9abNV1JCuWRVNTU9m4cSMzZszA3b38DkIfHx82bdpESkpKhbMmo0aNonXr1vzwww8A/PLLLxQWFvLss89WeD+ZrOpE/lWrVlFYWFjhvR5++GE8PDxM96rIwYMHOXz4MD169AAgICAAuVzOqlWrKlw+s7eaFy4RnFautoiMPENDQlvm3MhkMro3b8TaY3Hsj02lV0t/m11bqHts2Qm8MlFNvFl58DLHRY8pAZDy8jjTpavV52WXynMxvp/yxRcWn9/m8CFkbm4WHfvff/8hSRJt27at9Bhjbkq7du0qfL5t27amY86ePYuXlxfBwcGm53/55Rfuv/9+0/t79uwhMjKy0nt5e3sTEhJS7jm1Wk2LFi3K5cr06tULuVyOVqulsLCQhx56iPvuuw+AsLAwPvzwQ5599lleffVVunXrxoABA7jnnnto0aJFpR+zrYiZG6FSxp1SHholXi62/Yv7RpFU3GCUVCa2fTKxkWk7uEgqFuoJ4+v0ejMqpY+t6PHS55e91pAhQzh69Cjr168nJyfHNIPi4eFhenvkkUcsHm/Z669cuZKjR49y7NgxVq5cyZo1a3j++edNz8+YMYOEhAS+++47evbsyc8//0yHDh3YvHmzRfesCTFzI1QqwQ47pYyMxfwOX0ynUKdHpRBxtrMyzdzYYRu4UesgT9QKORl5hVxKzSXcz3aFAoX6R+bqSpvDh6w+L//0aS7ecy/hK77DpZLZkuvd11KtWrVCJpNx+vRpxo4dW+ExrVu3BuD06dP06tWr3PPR0dG0b9/edL2MjAwSEhJMszceHh60bNkSpdL8V/3Ro0dN//fy8jLdKyMjg7i4OEJDQ82O12q1XLhwgVtuucXs8SZNmtCyZUvAMLt04cIFXn75ZV555RVcXAy/Nzw9PRk9ejSjR4/m9ddfZ8iQIbz++usMGjTIkk9TtYnfKEKl7JFvY9Qq0ANvVxV5hTpOisqyTktfqu2CPXZKGamVctqFGq5/TCxNNXgymQy5m5vVb7LiX8gyF5fqnW/BLIxRo0aNGDJkCJ988gk5OTnlnk9PT2fw4ME0atSI999/v9zza9eu5dy5c0ycOBGAO+64A5VKxTvvvHPde7ds2dL0FhgYCMC4ceNQKpUV3uuzzz4jJyfHdK/KKBQKioqK0GorLtAqk8lo27ZthR+vrYngRqiUcVttsJftgxu5XGbaEi6WppzXxVJtF5r5WZaLUF0di5toHr+cbtf7CIKtLFmyBJ1OR/fu3fnll184d+4cp0+f5sMPP6Rnz564u7vz+eefs2bNGh566CGOHz9ObGwsS5cuZfLkydxxxx2MHz8egKZNm/L++++zePFi7r//frZt20ZsbCyHDx/mww8/BAzBR2WaNm3KggULWLRoES+++CLR0dGcP3+eDz74gGeffZann37alCxslJKSQkJCAleuXOGPP/5g8eLFDBgwAC8vL44ePcqYMWNYtWoVp06d4r///mPp0qUsW7aMMWPG2O+TWkwsSwmViku3fY2b0ro39+Wv04nsj0njob52uYXgYNGl2i4o7bz0aKxULHpMCdWlDAjAf8YMlAEBtXK/5s2bc/jwYd544w2efvpp4uPjCQgIoGvXrnz66aeAYUZm27ZtvPnmm/Tt25e8vDxatmzJiy++yJNPPmk2W/T444/Trl07PvjgA+644w4yMzPx8/OjZ8+ebNy4sdJkYqNZs2Zxww038N5777F48WJ0Oh0dOnTg008/5YEHHih3/K233goYgqaQkBCGDx/OG2+8AUDjxo1p1qwZr776qqnon/F9S2ru1JQIboRKleTc2KavVFmlZ270egm5jXsOCY5Xkm9jv2Rio45NfAA4eTUDnV6yeQ8rwfmpAgMJePyxWr1nSEgIH3/8MR9//HGlx/Tp04c//vjDouvdeuutpqCjOoz5MVVp1qzZdRP3/f39Wbx4cbXHUVNiWUqolD1zbgAiwrxxVSnIyCvkXFK2Xe4hONbpWsi3MbohwAM3tYJcrY7zyeL1JAgNmQhuhEoZc25CfOwT3KgUcrqE+wCiFYOzqo2dUkYKuYyIUGMxP7E0JQgNmQhuhArlF+pIzTFkvId42WdZCkr1mYoRwY2zycwv5EqaIW/LnjVuSotqLCoVC4IgghuhEonFszauKgVervZLzTLWuzkQkyqKrzkZ4xbwEG8XfNzUtXLPqOK8G7EdXBAaNhHcCBUqnW9jTe0Ga3Vu6otSLiMhM9/0V77gHIw7pWoj38bI2GPqdHwm2iJ9rd1XcDzxx5FzsNXXUQQ3QoXiMwyBhj2qE5fmqlYQWbyUIJamnMup4rYLtbFTyijczw1vVxXaIj1nE7Nq7b6C46hUhtYwubm5Dh6JYAvGAoBV1eSxhNgKLlQo3o6tF8rq3qwRRy6lsz8mlXFdG9v9fkLtiE6wf8PMsmQyGVGNvdl57hrHrqQTUTyTIzgvhUKBj48PSUlJALhZWSlYqDv0ej3Jycm4ubmVaxlhLRHcCBUy1rgJtVONm9JubNaIz3dcEJWKnUjptgvtaymZ2CgyzBDcnLiSAT2uf7xQ/xl7KRkDHKH+ksvlNG3atMYBqghuhArV5szNjc0aIZPBhWs5JGcVEOCpsfs9Bfu6lJpLrlaHWimnWS03sYwq7hAukoobDplMRkhICIGBgRQWFjp6OEINqNVq5PKaZ8yI4EaoUIKdC/iV5u2mok2QJ9EJWRyMTWVYZIjd7ynYl7G+TZsg+7ddKKtjE8NS1NnELPK0OlzVNVu7F+oPhUJR41wNwTmIhGKhQrU5cwMl9W72iaRip2CsTFybycRGwV4uBHhq0OklThUHWYIgNCwiuBHK0RbpuZZdAEBILeTcANzYXHQIdyanHbAN3Egmk5m2hItifoLQMIngRijHWMBPrZTj66aqlXsai/mdjs8kK1+smdd3JTulan/mBkrybkQbBkFomERwI5RTWwX8Sgv2dqFpIzf0Ehy6mFYr9xTsIyu/kMupxW0XaqGnVEWimoiZG0FoyERwI5RjLOBXG8nEpYk+U87BuAU82MsFX/faabtQlnFZ6sK1HDETKAgNkAhuhHJKdkrVTr6NUffmvoDIu6nvSvJtHLMkBeDnoSHMxxVJghNXxdKUIDQ0IrgRyqntnVJG3Zv7AXDscgb5hbpavbdgO6adUg5IJi7NuCX8hMi7EYQGRwQ3Qjm1WeOmtGZ+bvh7aNDq9CIRtB5z5E6p0iLDfACRVCwIDZEIboRyTE0zvWo3uJHJZKalqf0xKbV6b8E2SrddaOeAGjeldSxuyHpMJBULQoMjghuhnHgH5dxAqaTiWLFjqj4q3XahuX/ttl0oK6I4uLmSlkdqjtahYxEEoXaJ4EYwU6jTk2ws4OdTuzM3AN2Li/kdvpiGTi/V+v2FmjHWt2kd5FHrbRfK8nJR0aI4wBJbwgWhYRHBjWAmKasASQK1Qk4jt9rfxts22AtPjZLsgiJT7oZQf5yKNy5JOTbfxiiqsbHejci7EYSGRAQ3gpmE4nybIG8NcnntFPArTSGX0bWZIe9G9Jmqf6LjjZWJ60pw4wOI4EYQGhqHBzdLliyhefPmuLi40LVrV3bu3Fnl8QUFBbz44ouEh4ej0Wi44YYbWLZsWS2N1vmZ8m28aj/fxsiYd3NABDf1zukEx9e4Ka1k5ibdsQMRBKFWKR1585UrV/Lkk0+yZMkSevfuzeeff86wYcM4deoUTZs2rfCc8ePHk5iYyNKlS2nZsiVJSUkUFRXV8sidV4KDatyU1qNUE01JkmqtBYRQM3Wh7UJZHUK9UchlJGUVkJCR79DXtSAItcehwc0HH3zAlClTmDp1KgCLFi3izz//5NNPP+Wtt94qd/zGjRv5+++/uXDhAo0aGX4BNmvWrDaH7PTi0h1T46a0yMbeqJVyUnK0XLiWww0BHg4bi2C5utB2oSxXtYJWgR5EJ2Rx/Eo6wd7Bjh6SIAi1wGHLUlqtlkOHDjF48GCzxwcPHszu3bsrPGft2rV069aNBQsWEBYWRuvWrZk9ezZ5eXmV3qegoIDMzEyzN6FyCZnFNW4cGNxolAo6NfEBRJ+p+qSkMnHdWJIyEknFgtDwOCy4uXbtGjqdjqCgILPHg4KCSEhIqPCcCxcusGvXLk6ePMlvv/3GokWLWLVqFTNmzKj0Pm+99Rbe3t6mtyZNmtj043A2jqxxU1p3kXdT79SVysRlGZOKRTE/QWg4HJ5QXDafoqocC71ej0wmY8WKFXTv3p3hw4fzwQcfsHz58kpnb+bMmUNGRobp7fLlyzb/GJyJo1ovlGWsd7NfNNGsN0w7pRxcmbisjsXBzYmrGUiSqJ0kCA2Bw4Ibf39/FApFuVmapKSkcrM5RiEhIYSFheHt7W16rF27dkiSxJUrVyo8R6PR4OXlZfYmVKxIpycpq7iAn4ODmy7hvshlhuqycemVLzsKdYNeLxFdvCzVvo7N3LQJ9kStkJOeW5LwLAiCc3NYcKNWq+natSubN282e3zz5s306tWrwnN69+5NXFwc2dnZpsfOnj2LXC6ncePGdh1vQ3AtW4tOL6GUy/Dz0Dh0LB4aJR1CDUHsATF7U+ddTqs7bRfKUivlpq3pYmlKEBoGhy5LPfXUU3z55ZcsW7aM06dPM2vWLC5dusQjjzwCGJaU7rvvPtPxd999N35+fjzwwAOcOnWKHTt28Mwzz/Dggw/i6urYHBFnEGcs4OflgsIBBfzKMvWZEnk3dd7p4srEdaHtQkVKivmlO3QcgiDUDoduBb/rrrtISUlh/vz5xMfHExERwYYNGwgPDwcgPj6eS5cumY738PBg8+bNPP7443Tr1g0/Pz/Gjx/P66+/7qgPwanUhRo3pXVv3ohl/8SImZt64LQp36ZuLUkZiR1TgtCwODS4AZg+fTrTp0+v8Lnly5eXe6xt27bllrIE24ivI8nERjcWt2E4m5hNWo62ztROEcqLTqibO6WMjDM3J69moNNLdWJmUhAE+6l788eCwxj7StWV4MbPQ8MNAYb8DTF7U7edNjXMrFs7pYxaBnrgplaQo9VxITn7+icIglCvieBGMIk3LUvVnfyl7qVaMQh1U3ZBEZdSc4G60zCzLIVcRkSoWJoShIZCBDeCSV2pcVNaSb2bNAePRKjMmeIlqSAvDY3q8NJhpGiiKQgNhghuBJP4OpZQDCU7pv69mkFOgWiQWheZlqTq6KyNkTGp+JiYuREEpyeCGwEAnV4iMbPuzdw09nUj1NuFIr3EkUvpjh6OUIG6vlPKyFip+FR8JoU6vWMHIwiCXYngRgAgJbuAIr2EXAYBDi7gV9aNohVDnWasTNyujjXMLCvczw0vFyXaIr2pg7kgCM5JBDcCULIkFeTlUueKsJmSikUxvzpHr5dMPaXq+rKUTCYrVcxPLE0JgjOrW7/FBIepi/k2RsYO4Ucup6EtEssJdcmVtDxytDrUCjkt6ljbhYoY825OXE137EAEQbArEdwIQN2rcVNay0APfN1U5BfqOXFV/MVdl5wqnrVpVUfbLpRlSiq+LF5HguDM6v5PI6FWxBcnEwd71Z0aN0YymYxuzUS9m7qorlcmLsu4LHUmMYv8Qp1jByMIgt2I4EYAID697u2UKq2HyLupk0p2StXtZGKjEG8X/D006PSSadZJEATnI4IbAah7TTPLurHUzI1eLzl4NIKRcadU+3oyc2NIKi4u5nc53bGDEQTBbkRwIwAQn1l3c24AOoR64aZWkJlfxJlEsY23LsguKOJiSt1uu1AR0SFcEJyfCG4E9HqJxIwCAEJ86l7ODYBSIadLU0OXcJF3UzcYa8XU9bYLZRmL+R0XyemC4LREcCOQmqtFq9Mjk0GgZ90q4Feaqc+UyLupE+pLZeKyjD2mzidnky1aegiCU1JW56QtW7awZcsWkpKS0OvN644sW7bMJgMTao8x3ybAQ4OqDm/nLZ13I0kSMpnMwSNq2OrbTikjfw8NYT6uXE3P48SVDHre4OfoIQmCYGNW/yZ79dVXGTx4MFu2bOHatWukpaWZvQn1T3wd7AZekc5NfVApZCRmFnApNdfRw2nwShpm1o+dUqWJYn6C4Nysnrn57LPPWL58OZMmTbLHeAQHiC8u4FdXd0oZuagURIZ5c/hSOvtjUgn3q/sVcZ2VXi+Zcm7q28wNGOrd/HEyQXQIFwQnZfXMjVarpVevXvYYi+AgJTM3dTOZ2Eivl8yWpgAkSRJbwx3gSloe2QVFqBVymteDtgtlleyYSnfYGCS9Hkkyf+1KkoSkFy1GBKGmrA5upk6dyvfff2+PsQgOklBPlqUWbTnHwYuGpc8DsWlcyy5g+orDLN5yzsEja3hOJ5S0XajLeVqViQgzBDeXU/NIy9E6ZAzXPv6EqzOfRHv1Khm//442Lo6rM5/k2idLHDIeQXAmVi9L5efn88UXX/DXX38RFRWFSqUye/6DDz6w2eCE2lFflqXaBHny7Z5YAGKu5TDw/b9RyGWMjAp17MAaoPq6U8rI21VFC393LlzL4fjVDPq1Dqj1MWhatyLthx+4MGw4klaLTKNB7uaG1/BhtT4WQXA2Vgc3x48fp1OnTgCcPHnS7Dmxe6V+Sqgny1IjokLo0aIRvd/eSkGRniBPDd8/dBP+HnV3+7qziq7HycRGkY29DcHN5XSHBDdeQ4eiaduWC8NHACDpdDRf/RuqoKBaH4sgOBurg5tt27bZYxyCg0iSVG92S4FhG29UY28OxKZxa4cgEdg4yOl6ug28tKjGPqw5GufQpOKcHTvAmGNTVETBuf9EcCMINlCjxfIrV65w9epVW41FcIC03EIKigw/XAO96kegYFw+Syhu9inUrpzSbRfqScPMinR08HZwSZJI+/4Hs8cy161zyFgEwdlYHdzo9Xrmz5+Pt7c34eHhNG3aFB8fH1577bVyBf2Eus+Yb+PvoUajVDh4NJYJK24RcTU9z8EjaZiMzTIDPTX41eOZs/ahXshlkJhZQGJm7QfKuXv2oI2NReZSMmOatXkz+nwRtAtCTVkd3Lz44ot8/PHHvP322xw5coTDhw/z5ptv8tFHH/Hyyy/bY4yCHdX1buAVMXagTnLALySh/lYmLstNraR1kGHm6ZgDOoSnFu869Ro2DL/p01EGBaHPySFbLP0LQo1ZnXPz9ddf8+WXXzJ69GjTYx07diQsLIzp06fzxhtv2HSAgn3Vlxo3pXVqYmigeTUjH71eQi4Xiey1ybRTqh4nExtFNfYmOiGLE1czGNwhuNbuWxgfT/ZWQxDjN+VBNC1bQlERKV98Qca69XgNEzumBKEmrJ65SU1NpW3btuUeb9u2LampoqFhfVNfatyUFurjgkohQ1ukJ17M3tQ6406p9vV85gYgsrhDeG0nFaetXAl6PW7duxsCG8BrpGHXVPaOHejS02t1PILgbKwObjp27MjHH39c7vGPP/6Yjh072mRQQu2Jr4fLUkqFnCa+bgDEXstx8GgaFr1eMuXc1NcaN6V1LFWpuGy1YHvRa7Wk/7wKAN+77zY97tK6NZo2baCwkMw/N9XKWATBWVm9LLVgwQJGjBjBX3/9Rc+ePZHJZOzevZvLly+zYcMGe4xRsKOETENSbn2auQFoVlyALTYlh94t/R09nAbjanpJ24UWAfWv7UJZbYI9USvkpOcWciUtjyaN3Ox+z6xNm9GlpKAMDMRz4C1mz3mPGknSmTNkrluH713j7T4WQXBWVs/c9OvXj7Nnz3LbbbeRnp5Oamoqt99+O2fOnKFPnz72GKNgR/HF26mDvepPzg1As+KmmWLmpnadKs63aRlYP9sulKVRKky5Q8dqqc9UWnEisc/48cjKVHj3Gj4cgNwDByiMj6+V8QiCM7J65gYgNDRUJA47gfpWwK+0Zv6Gv7BjruU6eCQNS0ll4vq/JGUU1dib41cyOH4lw+6tPPKjo8k7fBiUSnzG31nueVVoKG7dupF78CCZ69fjN3WqXccjCM7KouDm+PHjREREIJfLOX78eJXHRkVF2WRggv1l5hWRV6gD6lfODZTM3FxMETM3tcm4U6o+t10oK6qxD3CpVjqEG4v2eQ66FVVgYIXHeI0aRe7Bg2SsE8GNIFSXRcFNp06dSEhIIDAwkE6dOiGTySpMvpPJZOh0OpsPUrCP+OJ8m0bualxU9aOAn1Fz/+LgJjVXbAevRc5S46a0jsU7pk5ezbTra0mXmUnG778D4DtxYqXHeQ0ZTMLrr1MQHU3BuXNoWrWyy3gEwZlZFNzExMQQEBBg+r/gHEw7pbzq16wNGJbRjNvB4zLyaOxr/0TQhi6noIiLqfW/7UJZNwS446pSkF1QxIVr2bQMtM/HlrF6DVJeHppWLXG78cZKj1P4+ODRpw/ZW7eS8fs6Ap+aZZfxCIIzsygjMDw83NTx++LFi4SFhREeHm72FhYWxsWLF+06WMG26mONGyOlQm7a2WLscyTY15nELCSp/rddKEupkBMRZpiJOm6nejeSJJH2g2FJymfiRNPP08p4jxoJGHpN1dYWdUFwJlZvdxgwYECFxfoyMjIYMGCATQYl1I744t5M9S3fxqh5cd5NjNgxVStKKhM7z5KUUVTx0pS9gpvcPXvQxsQgd3fHe/SY6x7v0b8/cjc3CuPiyDtyxC5jEgRnZnVwI0lShX91pKSk4O5e/+teNCT1daeUUbjYDl6rSnZKOc+SlFFUcTE/e20HN87aeI8Zg8Lj+j8n5a6ueA4aBGDK0xEEwXIWbwW//fbbAUPS8OTJk9FoSqaldTodx48fp1evXrYfoWA3CZnG6sT1q8aNUfPi7eCxYlmqVph2SjlBZeLS9HqJyDBDcHMqLpNCnR6lXIYkYZPk4sL4eLK2bAXAd+IEi8/zGjWKjDVryPpjI8EvvFCuJo4gCJWzOLjx9jZ880uShKenJ66uJb8Q1Wo1N910E9OmTbP9CAW7Mc7chNbTmZtmxTumYsV2cLuTpJK2C860Uwpg0ZZznE3Mwl2jIKdAx/6YFL7be4nWQZ7MGtS6xtc36yNlxc4n95t6oPDzQ5eSQvY//+DZv3+NxyIIDYXFwc1XX30FQLNmzXjmmWdwcxO7U+q7hHrYV6o0Y62bSym56PQSCrEd3G6upDlX24XS2gR58t3eixQU6gGY+vUhXNUKmxT0kyrpI2UJmVKJ1/DhpH37LZm/rxPBjSBYweqcm/vuu4+rV6+We/zcuXPExsbaYkxCLcjKLyS7oAiov8FNqI8raoUcrU5PfEaeo4fj1E47WduF0kZEhbBpVl/T94FWp2fF1B6MiAqp8bUzq+gjZQnjrqmsrVvR54gZSkGwlNU/pSZPnszu3bvLPb5v3z4mT55sizEJtcA4a+PtqsJNXa0uHA6nkMto0siwPBor2jDY1eniZOK2TphMDODvoeHjiZ0B0Okl3v3zjE22YFfVR8oSLpGRqJo2RcrLI2vr1hqPRxAaCquDmyNHjtC7d+9yj990000cPXrUFmMSakFcPd8pZWRcmooReTd2ZaxM3N7J8m1KUxbPSCnlMrZGJ7Hq0JUaXc+sj9Sd5ftIWUImk+E90jB7I3ZNCYLlrA5uZDIZWVlZ5R7PyMgQrRfqkYSM+l3jxsiYVHxRbAe3K1ONGyfbKVWRe28KB2D+76dqtNxp6iN1662ogiruI2UJr+LgJuef3RRVUGNMEITyrA5u+vTpw1tvvWUWyOh0Ot566y1uvvlmmw5OsJ/6XuPGSOyYsj+ztgtOuiwFhsrLMwe24uG+Lejc1IesgiKe++VEtZanzPpI3V15HylLaFo0x6VDB9DpyPzjjxpdSxAaCquDmwULFrB161batGnDAw88wAMPPECbNm3YsWMH7777rj3GKNhBSeuF+lnjxqiZn6h1Y2/GtgsBnhr8najtQlmBXi7MGtSaEB9X3ruzIxqlnB1nk1l54LLV17K0j5SlvIztGH5fV+NrCUJDYHVw0759e44fP8748eNJSkoiKyuL++67j+joaCIiIuwxRsEO4uv5NnCjstvBBdszViZ2pmaZ13NDgAfPDGkDwOvrT3MlzfLg2do+UpbwGj4cZDLyjh5Fe9n6YEsQGppqbZMJDQ3lzTfftPVYhFpUn5tmllZ6O3hcep6pmaZgO8Z8G2dOJq7IA72b8+e/CRyITePZVcf5bkoPiyoW5+7da1UfKUuoAgNxu6kHuXv2krl+Pf6PPGKT6wqCs6p2wYrc3Fyio6M5fvy42ZtQP8QVJ0rW9+Cm9HZw0R3cPow7pZw536YiCrmMd+/oiItKzu7zKazYf8mi84zbvy3tI2Up75GjAMj4XXQKF4TrsTq4SU5OZuTIkXh6etKhQwc6d+5s9ibUfdkFRWTlGwv41e+cG4Dm/mI7uL1IklSqYWbDmrkBQ8L680PbAvDWhtNcuk4AXd0+UpbwHDwImVqN9vx5CqKjbXptQXA2Vgc3Tz75JGlpaezduxdXV1c2btzI119/TatWrVi7dq09xijYmHFJylOjxENTPwv4lSa6g9vPlbQ8sgqKUClktPD3cPRwHOK+ns3o0bwRuVodz6w6hr6K3K60n36qVh8pSyg8PfEobsGQIRKLBaFKVgc3W7duZeHChdx4443I5XLCw8O59957WbBgAW+99ZY9xijYmCnfxqd+L0kZmWrdiJkbmytpu+CJWulcbRcsJS9ennJTK9gXk8rXe2IrPK4mfaQsZdo1tX49kl5vl3sIgjOw+qdVTk4OgYGGglSNGjUiOTkZgMjISA4fPmzb0Ql2EW8q4Ff/l6QAmhurFIuZG5szdQJvQDulKtLUz405w9sB8M7G6Apfa5mbNqO7dq3afaQs4dGvH3JPT4oSE8k9cNAu9xAEZ2B1cNOmTRvOnDkDQKdOnfj888+5evUqn332GSEhNW80J9ifaebGyzlmbsKLa91cTs0T28FtzDhz0xDzbcq6t0dTbm7pT36hnmd+PlbutWba/l3NPlKWkKvVeA4ZDEDmOtGOQRAqU62cm/j4eADmzZvHxo0badq0KR9++KHYHl5PxGc6R40bo7LbwQXbMc7cNLSdUhWRyWS8PS4SD42SgxfT+OqfGNNz+WfOkHfoUI36SFnKuGsq889N6LVau95LEOorq4Obe+65x9T9u3PnzsTGxnLgwAEuX77MXXfdZevxCXYQn+4c28CNFHIZTU2VisXSlK3kaotMn08xc2PQ2NeNl0YYlqfe/fMM/yVlA5C2wrD9u6Z9pCzhdmM3lEFB6DMzydmxw673EoT6yqrgprCwkBYtWnDq1CnTY25ubnTp0gV/f3+bD06wD2epTlyaqQ2DyLupMb1eQpIkziQY2i74e2jwc1dXuUuoIbnrxib0bR1AQZGe2T8foyA9w2Z9pCwhUygMFYsRu6YEoTJWBTcqlYqCggKblBMXHCch0zn6SpVmbMMgekzV3KIt55i+4jAHY9MAuCHAnekrDrN4yzkHj6xukMlkvDMuEk8XJUcvp/PnB8ts2kfKEt7Fu6ayt21Dl51dK/cUhPrE6mWpxx9/nHfeeYeioiJ7jEewszytjvTcQsB5toJDqe7gYuamxtoEebIvJpX3Nhk2Dhy9nM6+mFRaB4m8G6MQb1fmjmwPkoTrhtWA7fpIWULTrh3qG25A0mrJ2rS5Vu4pCPWJ1RXc9u3bx5YtW9i0aRORkZG4u5uXF//1119tNjjB9oyzNu5qBZ5OUMDPyDhzI6oU19yIqBB6tGhE/3e3U1CkJ8TbhVWP9nLqjuDVcUfXxpxat4XG2Unkq1xwL070rQ0ymQzvkSNIXvwhmet+x+f222rt3oJQH1j9283Hx4dx48bZYyxCLSipcePiVMuLzfyN28EN3cEVFjQ4FCrn56429S96alAbEdhUQCaTcU/CAQqBTY27sOtAAo8PrL3ZLa+RI0le/CE5e/dRmJSEKtC+icyCUJ9YHNxs3bqVvn378tVXX9lzPIKdxac7X74NQKi3K2qlHG2R6A5uC/EZ+eRodQA0buQ8y5e2VBgfT+GO7QCsb96L+K3nGNguiPahtbOzTN2kCa6dOpF39ChZf/xBo/vvr5X7CkJ9YHHOzaBBg0hNTTW9f9NNN3H16lW7DEqwnwQnq3FjJJfLaFoc0IhKxTV3pri+DYBaoXDgSOqu0n2k2t7UkUKdxOyfj6Etqr22CF4jDYnFYteUIJizOLgxTlEb/fvvvxQUFNh8QIJ9GZelnKXGTWnGvBvRY6rmjMX7WgV6EOgplqTKKttH6o3bIvF1U3EqPpNPtv1Xa+PwGjYUFAryT56kICbm+icIQgPRMDvhNWCm1gtOtiwF0NzfOHMjtoPX1JkEQ9uFsZ3DCHSSNh22VLaPVICnhvljIgD4ZNt/nLyaUSvjUPr54d67l2FM69bXyj0FoT6wOLiRyWRmCahl3xfqh3hTcON8v7DCTbVuxMxNTRlnbtqI7d8VqqiP1KiOoYyIDKFIL/H0T8coKNLVyli8jUtT634vN8MuCA2VxQnFkiQxcOBAlErDKbm5uYwaNQq1Wm12nOgMXrclOGF1YqPm/iK4sYVCnZ7zyYbCcG0aeDfwilTVR2r+mA7svZDCmcQsPtxyjmeGtLX7eDwHDkTm6krhxUvknzyJa2Sk3e8pCHWdxcHNvHnzzN4fM2aMTQawZMkS3n33XeLj4+nQoQOLFi2iT58+1z3vn3/+oV+/fkRERHD06FGbjMXZ5RfqSMkxNNpzxpkbYyG/y6m5FOn0KBVi1bU6Yq7lUKiT8NAoaezrfMuXNZX2vWHWpqI+Un4eGl4fG8GjKw7z6fbzDG4fTMcmPnYdj9zdHc9bbiFz/Xoyfv9dBDeCQA2CG1tYuXIlTz75JEuWLKF37958/vnnDBs2jFOnTtG0adNKz8vIyOC+++5j4MCBJCYm2nxcziqxeKeUi0qOt6vKwaOxvRAvF9N28PiMfLEdvJpOxxvybVoHeYil5zJ0WVnX7SM1LDKE0R1DWXssjqd/Psa6x2/GRWXfHWdeI0eQuX49mRv+IOjZZ5EpnadApyBUh0P/tP3ggw+YMmUKU6dOpV27dixatIgmTZrw6aefVnneww8/zN13303Pnj1raaTOIb5UMrEz/tKSy2WEi+3gNWbcBt4mWHQCLyvjt9VIubnX7SP16ugOBHhq+C8pm4V/nbX7uDxuvhmFjw+6a9fI2bfP7vcThLrOYcGNVqvl0KFDDB482OzxwYMHs3v37krP++qrrzh//rxdZpKcnSnfxol3vzQTeTc1Zgxu2op8GzOSJJUkEl+nj5Svu5o3bzMsD/1vxwUOXUyz69hkKhWew4YCkClq3giC44Kba9euodPpCAoKMns8KCiIhISECs85d+4czz//PCtWrDAlNl9PQUEBmZmZZm8NlWnmxokaZpbVzM8wcxMrtoNXW7QIbiqUu3cv2pgY5O7ueI++fs7hoPZB3N4lDL0Es38+Rp7WvrunjLumsjZvRp+fb9d7CUJd5/CMy7J//UiSVOFfRDqdjrvvvptXX32V1q1bW3z9t956C29vb9NbkyZNajzm+irBiQv4GYmZm5rJyi/karrhddJWLEuZSfv+ewC8x4xB4eF+naMN5o3sQJCXhphrOaYu6/bi2rkzqtBQ9Dk5ZG/fbtd7CUJd57Dgxt/fH4VCUW6WJikpqdxsDkBWVhYHDx7kscceQ6lUolQqmT9/PseOHUOpVLJ169YK7zNnzhwyMjJMb5cvX7bLx1MfxJu2gTvvDhhjleJYkXNTLWcTDbM2wV4ueLs5X9J5dRUmJJC1xfAzxnfiBIvP83ZT8fbtUQAs+yeG/TGp1zmj+mRyuWjHIAjFLFrb+fDDDy2+4BNPPGHRcWq1mq5du7J582Zuu+020+ObN2+ucJu5l5cXJ06cMHtsyZIlbN26lVWrVtG8efMK76PRaNBoRPl4KLUs1QBybi6nie3g1WEq3ieWpMykrVxp6iOladXKqnMHtA1kfLfG/HTwCs+sOsYfM/vgprbPbiavkSNI+eILsnfsQJeejsLHxy73EYS6zqLvsIULF5q9n5ycTG5uLj7F3zjp6em4ubkRGBhocXAD8NRTTzFp0iS6detGz549+eKLL7h06RKPPPIIYJh1uXr1Kt988w1yuZyIiAiz8wMDA3FxcSn3uFCxeCcu4GdUejt4XHo+Tf3EdnBrRMeLfJuyzPtIVbz9+3peGtmeXeeucTEll3f+iObVMfb5meXSujWaNm0oOHOGzE2b8B0/3i73EYS6zqI/a2NiYkxvb7zxBp06deL06dOkpqaSmprK6dOn6dKlC6+99ppVN7/rrrtYtGgR8+fPp1OnTuzYsYMNGzYQHh4OQHx8PJcuXbL+oxLK0RbpuZZtaHTqzDk3ZtvBRd6N1c6ImZtyMjeX7iM1sFrX8HJR8c4dhuWpr/dcZPf5a7YcohnvUYalKbFrSmjIrJ6zf/nll/noo49o06aN6bE2bdqwcOFCXnrpJasHMH36dGJjYykoKODQoUP07dvX9Nzy5cvZXkVi3CuvvCKqE1vIWMBPrZTTyF19naPrN+PSlOgObh1JkogubpgpgpsSxorEpftIVUefVgHc3cNQnPTZVcfJLiiyyfjK8ho+HIDcgwcpjI+3yz0Eoa6zOriJj4+nsLCw3OM6nU5UC67DEjJLGmY6YwG/0ow9pkQhP+skZOaTmV+EQi6jZaCHo4dTJ1TVR6o6XhjejjAfV66k5fHWhtM2GGF5qtBQ3Lp1A0kic8MGu9xDEOo6q4ObgQMHMm3aNA4ePGjqQHvw4EEefvhhbr31VpsPULCN+AZQwM8o3FTrRgQ31jAmE7fwd0ejtG+7gPqiqj5S1eGhUfJu8fLUin2X2HkuucbXrIjXqFGA2DUlNFxWBzfLli0jLCyM7t274+LigkajoUePHoSEhPDll1/aY4yCDTSEGjdGzf2My1KikJ81TPk2Qe6mP1yMJElC0usdMSyHsaSPVHX0aunPfT0NeYXPrTpOZn75mfCa8hoyGFQqCqKjKTh3zubXF4S6zurgJiAggA0bNhAdHc3PP//MTz/9xOnTp9mwYQOBgTX/y0awj7h0569xY2TMublU3B1csIwxuBl2aD1XZz5JUUoKAEUpKVyd+STXPlniyOHVOkv7SFXHc0Pb0rSRG3EZ+byxzvbLUwofHzz69AEgY916m19fEOq6ahcBad26NaNHj2bMmDFWVQwWHCMhoyTnxtkFe7mgUcop0kumarvC9Rm7gXu3b0vugQOcHzqM8yNHcX74CHIPHEDTqqWDR2gnej2UnanS6y3uI1Ud7holC4qXp1YevMy2M0mG+0oSer1U1akWM+2aWreu3EycIDg7qytJPfjgg1U+v2zZsmoPRrCf+Eznr3FjJJfLCPdz42xiNrEpuYT7WVYqvyEr1Ok5n5wNQPjtowi57Vb+u3UQ2v/+Q+7tTfPf16Ly93fwKO3k77ch6TSM+AA8AiA7mdyPphr6SLm5WdRHqjp2n0+hmZ8bsSm5PP/LcX6YdhPv/nmG1kGezBpU8z8YPfr3R+7mRuHVq+QdOYpbl842GLUg1A9Wz9ykpaWZvSUlJbF161Z+/fVX0tPT7TBEwRaMOTehDWBZCkQbBmvFXMuhUCfhrlYQ5uOKLiMTKc/wmtFnZJC5Zq2DR2hHge3g4j+wuCO83RQ+6kLadsNSkfdYy/tIWatNkCcZeYXIZZCYWcDQRTvZF5NK6yDbbMOXu7riOWgQAJnrfrfJNQWhvrB65ua3334r95her2f69Om0aNHCJoMSbKtQpycpy1DAryHM3EBJ3o3YDm6Z0m0X5HIZmRv/MHs+6f33cYmIwL1Hd0cMz7463AYpMbD1VSiEwnwNWZe9AD2+E22XSFzWiKgQerRoxD3/28eZxCy0Oj3zR3dgRFSIze7hNWoUGWvWkPnHRoLmzKlRnR5BqE9s0nhHLpcza9ascm0ahLohOasASQKVQoafkxfwM2rmJwr5WeOMqXifoRN41h8lwY1Hv36g13P1qacodMZaVvv/ZwhsiqWdVVe7j5S1/D00vD++o+n9t/6IJseGxf3cb+qBws8PXVoaObt32+y6glDX2ayr4Pnz5ykqsk/FTaFm4ouXpIK8XJDLnbuAn1Ez/+JaN2I7uEWMO6XaBntScO4cBef+A6WSRlOnEPTiC2jatkWXksLVJ2chabUOHq0N7f8fbJht+H/Hu5F0kH7B8Nrx7d2sVocS4KHhanoe7/55xmbXlCmVporFouaN0JBYvSz11FNPmb0vSRLx8fGsX7+e+++/32YDE2wnvgHtlDIyztxcThXdwS1Relkqc933AHjcfDNBsw2/+Bt/uJiYcXeQd+QIie++R/CLLzhsrDZzYGlJYNN7JnS4jcy1v6LLV6B01eF57StInwo+TWplOI/f0pK5a//l6z2xjIwKoVuzRja5rveokaR9+y1ZW7agz8lB7i4S7AXnZ/VP/CNHjpi9HT9+HID333+fRYsW2Xp8gg0kZDScGjdGYju45bLyC7mSZvgctQnyILN4Scpr+DDTMeqmTQld8A4Aad9+W/9rpxz8CtYb/lCTbnoMaeAr4BlCWlJbALw7eoM2A1Y/atgqbkeBnhpmDmzF0Ihg7uzaGEmCZ385Tn6hzibXd4mMRNW0KVJeHllbt9nkmoJQ11k9c7Ntm/jmqG8a4syNXC6jmZ87ZxKziLmWI7aDV+FsomHWJshLg+uVWLQxMcjUajxuucXsOM8BA/B75GFSPvuc+JdfRtO6FS71scbVoeWw7knD/3s+xrUzQRRsmIXvPXeTdz4JFAryZW25dvoaAbKdsO9T6DnDbsMJ9HIxbf1+aUR7tp9N5kJyDh9uOcezQ9vW+PoymQzvkSO5tmQJGet+N9W/EQRnVu25+uTkZHbt2sU///xDcrJ9+qMIttGQCviVJnpMWaZkScqLzA2GWRv3vn1QeJRvnhnw+OO49+qFlJfH1SdmosvOrtWx1tihr+H3mYb/3zQDBr+OpnVrcvfv59KUKQDI5HLyo8+j6VvcKPOvVw11cGqBt5uK18ZEAPD5jgucvJphk+t6jTQENDm7/qEoNdUm1xSEuszq4CYnJ4cHH3yQkJAQ+vbtS58+fQgNDWXKlCnk5orkzboovgH1lSrN2B1cJBVXzZhM3K70ktTQYRUeK1MoCH3/PZShIWhjY4mfM6f+VL89/C38/oTh/z0ehSFvgEyGR79+qFu0gCLDMpD6hhtose53vGa8Ay0Hga4Afp0GRbWTSD00IpgRkSHo9BLPrjpOoQ1aiGhaNMelQwfQ6cjcuNEGoxSEus3q4Oapp57i77//5vfffyc9PZ309HTWrFnD33//zdNPP22PMQo11BBzbqCk1k2s2A5eJePMTcf8RAovXULm4oLngP6VHq/09aXx4sXIVCqyNv9F6tKltTPQmjjyHax93PD/Ho/A0LdAJkOfl8flR6eTd/gwMhdD8B/yxuso/fxAJoMxH4NrI0g4AdvfqrXhvjK6Az5uKk7FZ/LFjgs2uaaXsR2D2DUlNABWBze//PILS5cuZdiwYXh5eeHl5cXw4cP53//+x6pVq+wxRqEGdHqJxOICfg1t5sa4LCW6g1dOkiTTzE2zE4Y6KB79+l13R41rZCRBL74IQNIHC8nZu8++A62JIytgzWOABN0fhqFvGwKb/HyuzJhB7t69yN3cCHr55fLnegbDqMWG//+zCC7uqZUhB3hqmDuyPQCL/zrHf0k1X/7zGj4cZDLyjhxBe+VKja8nCHWZ1cFNbm4uQUFB5R4PDAwUy1J1UHJWATq9hEIuw99D4+jh1CrjstRl0R28UomZBWTkFaKQgWqnYbOA17ChFp3rc9d4vMeONRT4e/rpulng7+j3sGYGIMGN02DYOyWBzfQZ5Ozeg8zNjSZf/g+Xtm0qvkb70dDxbpD08NvDUJBVK0O/rXMY/dsEoNXpee6X4+hq2FBTFRiI2009AMis77vdBOE6rA5uevbsybx588jPzzc9lpeXx6uvvkrPnj1tOjih5kwF/Dw1KBpIAT+jIM+S7eDGrc6CudPFlYn7S8kUXb2KzM3NUJHYAjKZjOB5c0sK/M18sm4V+Dv2I6yejiGwmQrD3zUENgUFXHnscXJ270bm5kbTLz7HrUsXlAEB+M+YgTIgoPy1hr0N3k0h/SJsnFMrw5fJZLxxWyTuagWHLqbxzZ7YGl/Te+QoADLW/V5/cqUEoRqsDm4WL17M7t27ady4MQMHDuTWW2+lSZMm7N69m8WLF9tjjEINmHZK+TSsfBso2Q4OIu+mMsYlqYEJhnpVnv37I3e1/LUid3Wl8YeLkXt5kXf0KInvLLDLOK12bCX89gggQbcpMPw988Bm1y5krq40/fwz3Lp1AwwzGwGPP4YqMLD89Vy84bZPARkc+Raia2fmI8zHleeHtwNgwcYzXE6t2ey45+BByNRqtP+dpyA62hZDFIQ6yergJiIignPnzvHWW2/RqVMnoqKiePvttzl37hwdOnSwxxiFGog3JRM3rHwbI1MbBrEdvEJnErKQSXranTkAgKeFS1KlqZs2JfSdtwFIW7GCjN8d3IH6+E+wujiw6fpASWCj1XLliSfI2bkTmYsLTT77DLcbb7T8us1uhl7FSclrn4Ds2imBcU/3pnRv3oi8Qh1zfj1RoxkXhacnHv37A5CxTiQWC86rWnVuXF1dmTZtGu+//z4ffPABU6dOxdWKv/aE2pOQWTxz49VAgxs/sR28KtEJWbRNvYRL2jXkbm549O1bret4DhiA36OPABA/dx75Z8/acpiWO/6zIS9G0kPXyTDiA5DL0Wu1XH1iJjl/7ygObD6tXofzW16CwA6Qe82wrbwWlnbkchnvjItCo5Sz679r/HyoZsnApl1T6zcg2bn6siA4ikUViteuXWvxBUePHl3twQi2J2ZuxLJUZQp1es4nZfPA1aMAeAwciFxT/aTzgMceI//4CXL++Yerjz9Bs1U/o/D0tNFoLXBiFfz2kCGw6XIfjFgIcjmSVsvVJ2eRvX07Mo2GJp8uwf2mm6p3D6UGbv8C/jcAzmwwLFF1uc+2H0cFmvu789Sg1rz1RzSvrztF/9YBBFbzDxaPfv2Qe3pSlJBA7oGD1QvyBKGOsyi4GTt2rEUXk8lk6HS26Yci2EZ8urGAX8OcWTPN3IhlqXJir+VQWFRE3zhDvo3XsIoL91lKplAQ+t67xIwbh/biReLmzKHxRx8hk9VCIvvJXwyF9iQ9dJ4EIxebApsrs54ie+vWksCmphsfgiMMMzib58IfzxuWqxq1sM3HUYUpNzdn3fF4TlzN4KXVJ/l8UtdqfW7lajUegweR+cuvZK5bZwpuJEkCSUImF01mhfrPolexXq+36E0ENnWPmLkx5NxcTsuzSaVXZxKdkEWHlBga5Wci9/TE/ebeNb5m6QJ/2X9tIeXLL20w0us4+Sv8UhzYdLoXRn1oCGwKC7n69NNkb9mCTK2m8Sef4N6rl23u2fMxCO8NhTmGxGW9/X/2KRVyFtwRhVIuY9OpRDacSKj+xYqrMWf88Qd6rZai4t1u1z5ZYqPRCoJjiRDdien1EonGnJsGGtwEebrgopKj00tcFdvBzZxJyKLP1WMAeA4ciFyttsl1XSMjCXrpJQCSFy4iZ+9em1y3Qv/+Br9MBUkHne6B0R+VCmxmk7X5L2QqFY0/+RgPGwRvJnIFjP0U1J5weZ+hwF8taBfixfQBLQGYt/YkaTnV23rv0a8vyGRI2dnE3jWBCyNGknvgAJpWLW05XEFwGIuDm61bt9K+fXsyMzPLPZeRkUGHDh3YsWOHTQcn1My1nAKK9BJyGQR6NqwCfkalt4PHiLwbM2fi0rk57gQAXsNrtiRVls/4O/G+7TZDgb+nnqYwoQazDJU5tQZWTTEENh3vLglsioq4+syzZG3aZAhsPv4Ijz59bH9/33AYXrz1fdubEH/M9veowIwBN9Aq0INr2VpeW3eqWtfwHj4c37snAlBw+jS6nBy8x4zGJSLClkMVBIexOLhZtGgR06ZNw8vLq9xz3t7ePPzwwyxcuNCmgxNqxljjJtDTBaWi4U7Sie7gFZOOHqZRQRZ6D8+a56GUYSrw164dutRU2xf4O7UWVj1oCGyiJhh6QMkVSEVFxD37LFkbN4JKRdhHH1pclLBaOk6EdqNAXwS/PgSF9p8d1CgVLLgjCpkMfj1ylW3RSdW6TuAzz+A5YrjhncJCUpd/zflbB3Fx8gNk/P47+lKFWgWhvrH4N96xY8cYOrTyGhiDBw/m0KFDNhmUYBsNPd/GyLhjSvSYKpFdUETbM/sBcB94KzKVyub3kLu4lBT4O3aMxLffsc2FT/8Oqx4wBBRRd8HYJaUCm+fI3PAHqFQ0XrwYz+KaLnYjkxmSl90DITkatsy37/2KdW7qy4O9mwPw4m8nyMovtPoachcX/B58EICAp2bh3rs3yGTk7t1L3DPPcq5PX+JffZW8EydFNWOh3rE4uElMTERVxQ9ApVJJcnLtFLUSLFOyU6phBzfNjctSYubG5MzVNNOSlP/oEXa7j7pJE0IXGIKatO+/r3mBv9Pr4OfJhsAmcrwh70WuQNLpiHt+DpkbNhQHNovwvGVAzT8AS7j7wZhPDP/fuwQubK+V284e3IamjdyIy8jnnY01qzbs3rs3TZd+Scu/NuP/+GOowsLQZ2WR/sOPxN55JzFjxpL69dcUpaXZaPSCYF8WBzdhYWGcOHGi0uePHz9OSEiITQYl2EZ8ppi5AQgXLRjKidu6C29tDrmunrj36GHXe3n274//9EcBiH95LvlnzlTvQtHr4ef7iwObO+G2z0oCmzlzyFy3DpRKGi/8AM9bbrHhR2CB1oOhm2EWhNXTIS/d7rd0VSt4e1wkAN/tvcTeCylWX6NsPy1VWBgBM2Zww+ZNNP1qGV4jRyJTqyk4e5bEt97mXN9+XJn5JNk7diCJ3bFCHWZxcDN8+HDmzp1r1jDTKC8vj3nz5jFy5EibDk6oGVNfqQYe3Bi7g18R28FNZH//BUBip57IlBaVu6oR/xkzcO/dGyk/nytPPIGugo0JVTrzB/xUHNhEjIOxJYFN/Asvkrn2d1AoCPvgfTxvvdU+H8T1DH7dUO8m8ypseKZWbtnrBn8mdm8CwPO/HCe/0LqAo7J+WjK5HPeePQl7711a7dxB0NyXcenQAQoLyfrzTy4/9DD/3TKQpEWL0F66ZLOPRxBsxeLg5qWXXiI1NZXWrVuzYMEC1qxZw9q1a3nnnXdo06YNqampvPjii/Ycq2CleFNw0zAL+BkFeWlM28FFd3CQtFpCj+8DQDZgUK3c01jgTxkaQuHFS8TNecHy0v9nNsLKSaAvhA63w21fgEKJpNcT/9LLZKxZYwhs3n8fr8GD7fuBVEXtbhibTA4nfjIUFqwFc4a3I8hLQ2xKLgs3277thcLbm0Z3303zX1bRfPVv+E6ahMLbm6LERFI++5zzg4dwcdJ9pK9ejT5PfH8JdYPFwU1QUBC7d+8mIiKCOXPmcNtttzF27FheeOEFIiIi+OeffwgKCrLnWAUriZkbA5lMJioVl5K9Zw+uBTmkajxp0t9GRe0sYCjw96GhwN+WLaR8ufT6J539E34yBja3we3/KwlsXn6ZjN9+Kw5s3sNr6BD7fxDX0+RG6DPb8P91T0FmnN1v6eWi4o2xhuWp/+28wPEr6Xa7l0vbtgS/+AItd+4gbNFC3Pv0MSQhHzhA/PNzOHdzH+LnziPv2DGRhCw4lFX7g8PDw9mwYQPXrl1j37597N27l2vXrrFhwwaaNWtmpyEK1SFJkim4aeg5N1C6gaYIbpLWrgdgd1gULUO8a/XerpERBL1cXOBv0SJy9uwpeVKvN29EeXYT/HgP6LTQfizc/qUpsEmYN4+MX34FuZywdxfgVcVOzlrX71kI6QT56Yb8m1poTnlr+yBGdwxFL8Gzq46jLbLvPeVqNV5Dh9L0f1/QcusWAmY+gapxY/Q5OaT/9BOxd00gZvRoUr5aTlGK9blAglBT1Sp+4uvry4033kj37t3x9fW19ZgEG0jN0aLV6ZHJDHVuGjpTA80GPnOj12rJ374NgP863ISLSlHrY/C58068b7+9pMBffLzhib/fhp/ug+xkOPcX/Hi3YcbGvzWMKx3YvEL6z6tALid0wQK8hg+v9Y+hSgqVYYZJ6QIXtsGBWmhBAcwb1Z5G7mqiE7L4dPv5WrkngCokBP9HH+WGTX/SdPlyvEaPQqbRUHDuP5LeeYdz/fpz5fEnyNq+Hb1WW25GR5Ik0Z1csLmGW9nNyRnzbfw9NKiV4svcrLiQX0wDr3WTs+sf5DnZXHPxQtmpk0PGIJPJCJ77Mpr27dClpXHlySfRa7UQ2A4u/gMfdYHv7zQENgo19HsOFCrDbOT8+aT//LMhsHnnbbxH2m8be40EtIZBrxn+v/llSLZ9LkxZfh4a5o1qD8DH285xNjHL7vcsTSaX435TD8IWLKDVzh0EvzIPl8hIKCoia/NmrjzyKGdv6smF4SPIPXoUQPS0EuxG/NZzUvEi38ZMSSG/hj1zk/nHHwDsCu1I2+DaXZIqTe7iQuPFhgJ/+ceOk/T224acmumGRGckPbj6wsxjEHkHkiSR+NprpP+4EmQyQt96E+9Roxw2fovcOBVuuAWK8uG3h0BnfaE9a43uGMqt7QIp1Ek8u+o4Or1j8l4UXl74TphA859/ovmaNTS6/z4UPj5IubloY2K4OGEiF8beJnpaCXYjghsnlZBh2LUQ7CWCGxDbwQH0+flkb9kCwI6wjrQJ9nToeNRNmhD2rqE3U9r3Pxh2PalcQVs8uzbmE/AKNQQ2r79B2vc/gExGyJtv4j1mjANHbiG53PAxuPhA3BHY8a7dbymTyXh9bCSeGiVHL6fz1T8xdr/n9bi0aU3QnDm02vE3YYsX49qlCwAF0dGomzenxbrf61bOlOAURHDjpIwzN6E+DXsbuFGgpwZXlaJBbwfP3rkTfW4uya4+RDdqStvg8n3iaptHv374T58OQPy8V8jf/hNIRYYnvcIMgc2bb5G2YoUhsHnjDXxuG+u4AVvLKxRGFvfc2/EeXD5g91sGe7vwwoh2ALy36Uydma2UqdV4DRlM408+Nj3mN306Sj8/B45KcFYiuHFSYqeUOZlM1uAbaGYVL0ntCOuIq0ZFY9+6Efj6z5iO+803Gwr8zf8EnVYGGBJNE996i7RvvwUg5PXX8Ln9NkcOtXoibjdUVJZ0huUprf1ffxNubELPFn7kF+p5/pcTdWpbttLXF3VzQ1+s/BPHHTwawVmJ4MZJiZyb8oxLUw2xx5Q+L4+sbdsBQ3DTOsgTuVzm2EEVkykUhL67AGVICIUpucTt80FqN5akpb+S9o0hsAl+bT4+48Y5eKQ1MPxd8AqD1Auw6SW7304mk/H2uEhcVHL2XEjhxwOX7X5Pa7h26ghA/rFjDh6J4KxEcOOk4kXOTTnGHlN1ZZq+NmX//TdSXh65fkGc9WlCuxDH5tuUpfT1xf3GjoBE9lVXYn/KIvX7VQC49++P7513OnaANeXqa+heDnBwmaGGj52F+7kze3AbAN5cf9r0M6Eu8OjbF0B0HBfsRgQ3TkiSJNF6oQLN/RvudvDMDYYlqZOtbgSZjDZBdSu4AfBo5oJMafhFl//vaQBkbm74jK0HycOWaNEfbjLkF7FmBuTYv7jdA72b06mJD1kFRbz0W90JJDxuuQWZRoMuNZWCc+ccPRzBCYngxgml5xZSUFyhNNBL4+DR1B3hDbQFgz4nh+y//wZgU2AHANrUgWTisrz842g5MgmFlyEgVzVuTMvNm5xrJ83AuRDQFnKSYN1M84rMdqCQy1hwRxQqhYwt0UmsPWb/dhCWkGs0uHXrBkDO7t0OHo3gjERw44SMszZ+7mqHVKCtq0q2g+favTx9XZK1bTtSQQHKpk35R+YPQFsHbwMvR6+DC9tRuuhp8vbzAIQtXuR8O2lUrnD7FyBXwenf4diPdr9l6yBPHr+lFQCv/n6KlOwCu9/TEu69DH3NRHAj2IMIbpxQQqZhbT3ER+TblGbcDq6XDAFOQ2Es3JfXuz/IZAR6avB1Vzt2UGXFHTX0YtJ4G2Y2nFlIRxgwx/D/Dc9A2kW73/KRfjfQNtiT1Bwtr/5+yu73s4R7b0Nwk3vgoKFCtSDYkAhunJBx5ibYS+TblGa2HbyBJBXrsrPJ2bEDgP/a9wRwePG+Cp3favi3RV+QN4DZxt5PQuPuoM2C1Y8aZq7AsExlhz5LaqWcBXdEIZfB2mNx/HUq0eb3sIReL5nyfjStW6Pw8zMkuh8+4pDxCM5LBDdOKEFsA69Uc1MDzYYxc5O9ZQtSYSHqFi04pjIs8dS5JSkoCW5uuAVlQAD+M2agDAhw7JjsSa6A0E4gUxj6ae35xNAw9Kf74O937HLLqMY+TOvbAoAXV58gM9/+7SDKWrTlHNNXHOZadgEyuRzljd0B2P79ulofi+DclI4egGB7cemigF9lTN3BG8jMTeYfGwHwGjqU6MRsgDpRmdhMfiZc2W/4/w23oPINJODxxxw7ptoQ3guOrjAU9ftrnqE9g0JlKPpnJ7Nubc2mfxOJuZbDWxtO89btUXa5j7ZIT2qOlmvZBaTkaEnNKSAlW0t0fCZ/n03mr7e24O+hoeM1b2YC7icP88WO87hrlHholLirlbhrlHi6GP511yjw0ChxVSmQyayvz6TXS8hkmJ0rSRKSRJ2p9yTYlghunJAp50YEN+WYuoM3gB1TuowMsv/5BwDPYUM584Ohz1CdW5aK3QX6ImjUAnybOXo0tafDbRDeGz7uDvlphiDnjmXQYazdbumiUvD27ZHc9cVefth/mVFRofRqaUgyr+qXfaFOT1qOlmvZWlJztKQUByspOQXFQUzx48XBTFZ+0XXHEp+Rj9bPkOgcGBfD46sPk612q/IcuQzc1Uo8TEGPEg+NwvSYh+kxJe5qhSlA2vhvAgkZ+TwzpA1dmvqSkqPl5dUnaR3kyaxBravxmRTqOhHcOKF40XqhUs1Mhfycf1kq668tUFiIplUrMoKakJ57FoVcRstAD0cPzVypJakGxyMQ7voWvh5paM/wy1TQaSFqvN1u2aOFH5FhXpy4mskzq46z4I4oLqbk8O3ei3hqlLQM8iS1OHBJydGSkq0lI8/6JSylXIavuxo/dzV+Hmr83DU0clfj76EmV6tjyfbzDB8QSebRJnglXuYhj1ROtGxOTkEROQVFZBcUkVOgM/yrLTKkI0mQVVBEVsH1g6eKjPt0D2E+ruRqi5DJZIyMCq3WdYS6TwQ3TkaSJFPOTago4FdO2e3gaqXzpp1lbjQsSXkOG8qphCzAMHNV58oDNOTgBkBTPJPWYgBc2Aa/ToOMK3DzLKjGEowl7u/VjGdXHedqeh73fLnP7Ln9sWkVniOXQSP3kiDFELCo8fMoCVoauWtMj3u5qCpd8jl5NYMl289z141N8R/Sn7RvvuVO4njivocrPF6vl8gr1JULerLNAiHj/4uP0xaRnV/yfGZ+IXHp+VxNzyPcz41fHu2Fv4eoA+asRHDjZDLzi8jVGnZeiJmb8gI8NbipFeRqdVxOy+WGgDo2i2EjRWlp5OzZA4DX0GGcuZIJ1MF8m7RYSD1vSKxt1sfRo3GsgfMgOAJ2fwRbXjUEOMPftcvusTu6NsFDo2Lmj0coKNLjoVFyU4tGhHi7Vhq0+LhWHqzUhHuvXqR9822V9W7kcplpGSqwBveau+Yk3+y5SFJmAXnFPycF5ySCGydjnLXxdVPVvb/Q6wDDdnB3TsdncjElx2mDm6y//oKiIjRt26Jp0ZzoA0eBOphvc36b4d8m3cGljgVetcUzGPo9D14hMPh18GoMG5+Hg0shKx7GLYXr5KJUx9CIYH55tBcjP9rFjw/dRESYt83vUZlATw0zB7Yi0FOD+403gkpF4ZUraC9dQt20qd3uO65LY77Zc5G8Qh2zVh5l5cM9UYiEYqfkvHPyDVScsWGmWJKqlKnHlBNvB88qLtznNWwYANHFy1J1L7hp4EtSYAhuBswx/Atw0yMw/mtQaODMBvh6FORcc+wYbSzQy4VZg1oT6OWC3N0dt46GLuE5xQnw9mIMZFxVCg5eTOOzv8/b9X6C44jgxsmIGjfX5+w9popSUsjZa8ij8Bo2lCKdnv+SDdvA29WlZSldEcQYel416OCmIu3HwP1rDd3Erx6EpYMg9YKjR2U3xmrF9m7FYJwxenaIoVv6ws1nOXElw673FBxDBDdORuyUur7mfs5d6yZr82bQ63Hp0AF106bEpuSgLdLjplbQ2LcOzejFHYH8DHDxhtDOjh5NrdNL+nJduiVJQi8VVyhuehM8uAl8mhoCmy8HwdVDNr136eUhs3vXMlOfqb37kIqqtxPKEsYZo8m9mzE8MpgivcSTK4+I/BsnJHJunExC8bJUiJcIbirj7IX8MjcUL0kNN1+Sah3kWbcKlplaLvRvGC0Xyvj02KecTz/Piz1exM/Vj5S8FN7Y9wYtfVoyvdN0w0EBrWHKX/D9nRB/DJaPhDu+gjYlndIlSaJQX4hWp0Wr16LVaSnUFZr+X9Fj62PWk5CdwPAWw+nWrgVx+bm8eWg5rX1bl9y7FrlERCD38kKfmUn+yZO4dupk1/vJZDLeGBvJwdg0zifn8PYfp3l1TIRd7ynULhHcOBnjzE2ITx36C72OMRbyu5qW53TbwQuTksg9cAAwVCUGOFMc3NS5tgsNPN+mpU9LVkavZPTq0fi6+BKfHQ9AYk4ip1JOmQKTQl0h2mB/tC4t0RbmoN01i8IDc9HK5YagRV/9NgqnUs2baB5OPMzhpMM08Wxiemvq2ZQmnk1wU9k+qdlIplDg3qMHWZs3k717t92DGwBfdzXv3dmR+5bt5+s9FxnQNpD+bWqyF0uoS0Rw42REzs31BXhqcFcryHHC7eBZmzaDJOHSMQpVWBhQR5OJ8zPgiiEIo8UAx47FQYY0G4ICBbN3zCZTm2l6/Pi145WfpFIZ/tXlQSUrKUqZEpVChVqhRi1Xo1aoUcnLvK9QgQSnU0+Tqc1Ejhw9etIK0tgXv4998fvKXbeRSyOzYKexZ2PD+15N8dX4VqstQmnuvXuRtXkzObt3EzC9dmaP+rYOYHKvZizfHcszq47z55N9aeSurpV7C/YlghsnkyBybq7LuB38VHwmsdecazt4ZpldUgDRCYZfnHUquInZaajI69cSfMMdPRqH+Pnsz7y59010kiFKmdh2ImEeYeWCEOP/1XI1arkK1dHvUR/8CrUkoW43GtXgN1Fr3E3HyWWWz0SeSjnFXevu4vsR3xPkHsTlrMtczrrMpcxLXM66zJWsK1zOukxaQRqp+amk5qdyLPlYueu4q9zNZntKz/oEugWiKF521Et6ZMjK93hCMuXd5B09hi47B4WHe00+vRZ7flhb/vnvGueSsnnh1xN8em+XGgdqguOJ4MaJZOUXmsqSB4ucmyo183czBDdO1IahMDGRvEOGhFOvIUMAyC4o4nKqIQ+rThXwa8BLUjq9jvcOvsd3p78DoHdob/6J+4exLcfS3q/99S8wpDMERMLvT8KJXyEnDcZ/A+rqf8/LZDL8Xf3xd/Wnc2D55O4sbZYp8Cn7lpiTSE5hDtGp0USnRpc7VyVXEeYRRhPPJqQXpKPVaXkw4kFuDb+VLG2WWZ6RqnFjCq9cIffAfjwH1M6MnotKwcK7OnHbkn/Y+G8Cqw5d4c5uTWrl3oL9iODGiRhnbbyKm8oJlWvmhNvBs4rbLbh26YIqJASAs4mGJalAT03dmm5voMFNtjabZ3Y8w66ruwB4vPPjpuDGKl3uA88Q+Ol+Q8uG5cPh7p8NhQDtwFPtSXu/9hUGXwW6Aq5mXTULeC5lXeJK1hWuZF+hUF9IbGYssZmxpnOe2/kcHns9kMvkKGQKhjQzBOPuvXqR/tNP5OzeU2vBDUBEmDezBrVmwcYzvLL2X3o096Opn/1yjAT7E78BnYgpmVgU8LsuZ9wxZdolVWpJ6kxdzLdJvQBpMSBXQrObHT2aWnMl6wqPb32c/9L/w0Xhwhs3v8HgZoNJzk3m0Y6PEuAaYN0FWw2CB9bDivGQcMJQC+eeVRDY1uJLBLgGVO/epWgUGlr4tKCFT4tyz+n0OhJzE7mUdckU+FxIv8Cuq7vILsxGLpMzv9f8CoIb+9a7qcjDfW9ge3Qy+2NTeeonUb24vnOebSJCSTKxj1iSup5mTlbrpvDqVfKOHQOZDM/Bg02P18mdUqaWCz1KmkY6ucOJh7l7/d38l/4fga6BLB+6nMHNDF+nALcApneaToBbNQKM0M4wdbMhdynjMiwbDBctDwxqdG8LKOQKQj1CuSnkJu5sfSdPdX2Kjwd+zMcDPwYMOTgv/fMSiw4tokhfhPtNPUAmQ3v+PIUJCXYZU+VjlfH++I54aJSierETEMGNE4kXO6Us1szffDt4fZe58U8A3Lp1QxVUsp21JJm4LubbNIxdUmv+W8PUTVNJK0ijvV97vh/xPR38O9juBr7NYMpmQ7CYnwHfjIF/f7Pd9e2gkUsjAIY1M8wyLj25lEc2P0K6RodLhKHeTM7uPbU+riaN3Hh1tOFrI6oX128iuHEiCZnFfaW8xLLU9QR4GLaD6yW4lFr/k4pNu6SGlyxJSZJU92ZudEUQs8PwfyfPt9FLehYeWshL/7xEob6QQeGDWD50OUHuQba/mVsjuG8NtB0JOi38PBn2fGL7+9jY5IjJvNv3XVyVruxL2Mf4dePJ7nQDYP9WDJW5vUuYqXrxTFG9uN5yeHCzZMkSmjdvjouLC127dmXnzp2VHvvrr78yaNAgAgIC8PLyomfPnvz555+1ONq6LS5dzNxYyrgdHOBiPV+a0l6+TP7JkyCX4zlokOnxpKwC0nILkcugZWAd2e5+9RAUZBp6JoV0cvRo7Ca3MJdZ22ax7OQyAB6Keoj3+r2Hq9KOf3ioXA27pro/ZHj/zxdg4xzQ172ZydK5PkObD+WHET/Q3Ls5SblJvCWtByBnzx4kB4zdWL040FPDheQc3vrjdK2PQag5hwY3K1eu5Mknn+TFF1/kyJEj9OnTh2HDhnHp0qUKj9+xYweDBg1iw4YNHDp0iAEDBjBq1CiOHDlSyyOvm0SNG+s0L04qjqnnO6Yy/zDsknLr0R2lv7/pcWPxvmb+7rio6kh7gwbQciEhJ4H7/riPrZe3oparebvP2/9v77zjoyrWBvycLemNdAIkBAgt9F6kSVdEQAVEQKwXAUVAwXL9RO+9ggqKKE0FsSDSFZUiqASkt1BDT+hJSO/Jlvn+2OySkAQCSXY3YR5+55ezc+bMvMMmZ9995y283PLlu8o/c8+o1ND/I+j9H9PrPfNh9RjQ5VT83HfBrb4+db3qsvzh5fQJ6UNUdSM5WjAkJpJ68jYJDSsQc/ZigO92X2Tb6XibyCG5d2yq3HzyySc899xzPP/88zRq1Ig5c+ZQq1YtFixYUGz/OXPmMHXqVNq2bUtYWBgffPABYWFh/Prrr1aW3D65bq4rJZWbUhGSH+pZ2Z2K0zblb0n161+o/XS+v41dVQKv4iHgR28cZfhvwzmdfBpvJ28W913Mw3Uetq4QigKdX4HHFoNKCyd/ge8HQVaSdeW4S1y1rszqNotJHaYSFWz6aFq85BViUmNsIo85ezHA66uPkpSZZxM5JPeGzZSbvLw8Dh48SJ8CkR0Affr0YVcp91qNRiPp6el4e3tXhIiVisxcPWk5+Qn8pHJTKszh4BcrcSK/vJgYck9GgVqNe5/eha7ZXdmF7BS4esB0XgVLLmyM3sgzm54hMSeR+tXqs/zh5bTwb2E7gZo+DqPWgqMnXNoNS/pC8kXbyVMKFEVhdPhowvs9CUBQVALDfx/O1otbbSLPG/0bEubvxo30XN5ce7RIFXeJ/WIz5SYhIQGDwUBAQGHnuoCAAGJLGQI4e/ZsMjMzGTp0aIl9cnNzSUtLK3RURWLTTGZnd0cN7k5aG0tTOagK21JmR2LXjh3RVKtW6Jrd5biJ3g7CCL71wavqZIA1CiPzIucxdftU8ox5dK/Zne/6f0eQW5CtRYPQrvDsJvCoAQlnTLlwrhctn2BvNOhreqaHX4G87AwmbZvE7AOz0Rv1VpXDnL1Yq1bYfCKOVQevWHV+yb1jc4fiW2t4CCFKVddj+fLlTJ8+nRUrVuDvX3Il1xkzZuDp6Wk5atWqOg/Vgkh/m7vHvC11LSWbXH3ljIgw+9t49O9XqF1vMHI2PgOwo0ipKrglla3PZur2qSw8shCAZ8KfYU6PObhqrVMXqVQENIbnt4J/OGTEwTcPwbk/bS3VbXEMC0Pj54dWJ3hZbbLuLz2xlBf+eIGE7ASrytKkhieTezcA4L31J7hUiS299xM2U258fX1Rq9VFrDTx8fFFrDm3smLFCp577jlWrlxJr169btv3zTffJDU11XJcvny5zLLbI9elcnPXFAwHN9dfqkzknj9P7pkzoNXifsvfQUxiFnl6Iy4OampVs4M08kLA+fwP1Cqi3MRnxfPspmfZHLMZjUrD+53eZ3KbyZYikXaFRxA8u9FkycnLgGVPwKEfCvcRwm4iqxRFwbVTRwAeTqzB7G6zcdG4cCDuAEN/HcrheOsGkbzYtQ7tanuTmWdg8spI9Ab7+H+SlIzNlBsHBwdat27Nli1bCrVv2bKFTvnVYYtj+fLljBkzhh9//JGHH76zo56joyMeHh6FjqrI9RTpTHy3KIpyswxDJdyaMpdbcOvUCbWnZ6Fr5i2psAB3VPaQQj7pAqRcMjm4hnS2tTRl5mTiSZ78/UmOJx7Hy9GLr3p/xeCwwbYW6/Y4ecJTa6DpUFNF9vXjYcu7JqUm4wasHA0RH9paSgvmKuGZO3fRp3YffhrwE3U963Ij+wbPbnqW709+bzUfGJm9uPJh022pyZMn8/XXX7NkyRKioqKYNGkSly5dYuzYsYDJ6jJ69GhL/+XLlzN69Ghmz55Nhw4diI2NJTY2ltRUmUXyeprZciMT+N0NlbUMgxCCtPxCme63bEnBzczEDQPsbEsquAM42knOnXtk68WtjNk0hviseOp41uHHh3+kTWAbW4tVOjQOMHgRNMz/YrhzDnxcF+a2hIs776ouVUXj0tFkucmJikKflESoZyg/Pvwj/Wv3Ry/0fLT/I6Zun0qWzjrbRAWzF8/ZepajV1KsMq/k3rCpcjNs2DDmzJnD+++/T4sWLdi+fTsbNmwgJCQEgOvXrxfKebNo0SL0ej3jx4+nevXqlmPixIm2WoLdECtLL9wT5jIMlU25yT1zlrzz51G0Wtx79ixy3Rwp1bC6vSg3+fWkKnHJBSEEXx39iknbJpGtz6ZzUGd+eOgHarlXMj8+lQqG/wi9pgMKZCVCXropsWJeFujtI+RZ6++PY/36IASZu02lGFy0LnzY9UPeaPcGGkXDpphNPPn7k1xIvWAVmYa0qsHDTaujNwpeXREpsxfbMTZ3KB43bhwxMTHk5uZy8OBBunbtarm2dOlStm3bZnm9bds2hBBFjqVLl1pfcDtD1pW6NyyWm4TK5SRozm3j2qULaveiCoxdRUoZdJW+5EKuIZe3/nmLuYfnAvBUo6f4oucXuDvYwf/vvfLAJFMVcQCtKySeg1/GwWfNYdfnkGP7yFLL1lSB9CCKovBUo6dY0m8J/s7+XEi9wJO/PcnmmIrPVq8oCv8b3IQAD5m92N6xuXIjKR9iLQn85LbU3VC7EoaDCyFIz/e38ejfv8j1zFy9pV5WQ3tI4HflQL5lwBsCm9tamrsmMTuR5zc/z28XfkOtqHmnwzsmy4FKY2vRyo5rfkbrp1ZCr/fALRDSr8Ef/4ZPm8DW6ZAeZzvxOpuVm91F/Gta+rdkxSMraBvYlix9Fq9FvMZH+z9CZ9RVqExeLoWzF/8tsxfbJVK5qQLk6AwkZ5n+oGW01N1httxcS6084eC5UVHkXbyI4uiIW4+i2zxn4kxWGz93R7xdHawtXlEKVgFXVa5HzpnkM4z4fQSRNyJxd3BnYe+FDG1Qcl6tSouDGzzwKrx6FAZ+Dj5hkJsK/3wKc5rA+lcg4ZzVxXJp0wZFq0V//Tp50TFFrvs6+/Jl7y95tsmzAHx/8nue3/w8N7JuVKhcXcJuZi+eKrMX2yWV60kjKRbzlpSLgxoPpyrwbdKK+Lo54OaoQQi4XEmqg5tz27h17YrarWg+FburBF5J89tEXI5g1IZRXMu8RohHCMseWkaH6h1sLVb54h4I3d4w/QTQOEKr0TB+n8kvp2Y7U5XxQ9/CF21gxUiTJc5KqJydcW7VCii5SrhGpWFS60nM6TEHN60bh+IPMfS3oRyIrVg5ZfZi+0YqN1UAc02pQE+nUiVAlNzEVB3c5FQcXQn8boQQlqzEHg8V3ZKCAmUX7CFSKisJrh0ynVeSkgtCCL498S0v//UyWfos2ge2Z9lDywj1DLW1aOWPeyD0ePOmcmNGpTJFVD2/BZ7dDPX7AwKifoWve5oSAZ7ZbAojr2CK87spjp7BPVn+8HLqedUjITuB5/94nm9PfFthSoeTVs2c4TJ7sb0ilZsqgIyUKhs3a0zZv99NzvHj6K5cQXF2xq1bt2L7mMPA7cKZ2Fxywa8heNawtTTFYhRGywegzqBj+u7pzDowC4Hg8fqPs6D3AjwdPe8wShUmuAOM+AnG7YUWI025ii7uhB+HwoJOELm8QiOszMpN1t69CN3t/Wlqe9Zm2UPLeLjOwxiEgVkHZjElYgoZeRkVIlt4kMxebK9I5aYKYMlO7CGdie+FUJ/K41Rs2ZLq3g2VS9HMw0IIy7ZUo+p24ExcCbakFhxZwJSIKcSkxvDilhdZe3YtAJ2DOvN/Hf4PrUrWagNMOXAGzTP55XR6GRzcIf4k/DwW5raA3fMgN73cp3Vq3Ai1pyfGzEyyjx27Y38XrQszHpjB2+3fRqPSsOXiFob/NpyzSWcL9RNCYBRlzzRcMHvxJJm92G6Qyk0VwGy5CfKSlpt7wbwtZe+5bkyJ+0qOkgK4kZ5LcpYOlQL1/G2cLE+IAvlt7Fe5qedVjwOxBxiyfggH4kx+Gm5aNwaHDZbbvMXhEQR9/guTjpty5bgFQNpV2PwWfBoOf74PGeUXQaSo1ZaEfpk7b781ZblHURjecDhL+y0lwCWAi+kXefy3x1l1ehVgioCbEjHFUhOsLBTMXnxQZi+2G6RyUwWQdaXKRqhv5ch1k3PkCPpr11G5uOBWIB9UQcz+NrV9XXHS2rjGUeJ5SL0EagcIKbmkiq3pW7sv73Z81xJCXL9afX4b/Bt9a/e1sWR2jrOXKVfOxKPwyFzwqQc5qbBjtimM/NeJpt+BcsBcZ+pOfje30tyvOSsfWUk9r3oYhZH397zPY+sfY9AvgzgQe4C6XnXLRb5a3i68/6jMXmxPSOWmEmM0mpIYxqbdrCslhMBolF77d4PZ5+ZaajY5OvsNBzc7Ers9+CAqp+IVWbuKlCpYcsHBjqpk34JRGFl0dJHl9X86/wcfZx8bSlTJ0DpB66dh/H4YtgxqtgVDLhxcCp+3hhWj4MrBMk3h2slUjyz76FEM6Xe39eXt5M3qR1YzouEIwBTeX921OuseXVeuCuzgljJ7sT0hlZtKzJw/zzJu2SGuJpuUGyeNmnHLDvHZn2fvcKekID6uN8PBryTbp/VGGI2kbTJlYC0pSgoKRkpJf5vS8uv5X4lKisJFYwfV0yszKhU0GgDPbYFnNkL9fpgirNbD1w/C0gGmCKtbK4+Xohq5Q80aaEOCwWAga9++uxZNrVLzRrs3aO5nSr7noHbA28n7rse5HbdmL/5gg8xebEukclOJaRDgzu7ziZYEfuOWHWJvdBL17SEEuBJhqg5u3+Hg2YcPo4+LQ+XmhusDD5TY73ScnURK6fMgZofp3I6VmyxdFnMPmUoqDAkbYmNpqgiKYtqGHLECxu2B5iNApTH9Pvw4FD4MgX1fgkF/V9XIC1YJvzexFJ5p8gwAR24c4e/Lf9/TOLejYPbi7/fI7MW2RCo3lZiHm1VnQLPqltd1/d34Y1JXHi7QJikdIZYaU/bpVJyWX27BvWdPVA7FZx3WG4yciTOFvNp8W+rKfsjLABdfCGhqW1luw9ITS4nPjqeGWw1GNhrJS81fws/Zz9ZiVR38G8HgBSa/nI4TQOMEuWmw4XWYEw7z2pW6Gnlp893cjuquN5+NH+3/iBx9zj2PVRJdwvx4pnNtwJS9ODEjt9znkNwZqdxUYpIy81hz6Krl9XsDw/F1c7ShRJUXczi4PUVMCaMp/4owGEj7w7Ql5d6/H6IEE35MYhZ5eiPOWjXB3jbeYqkEJRfiMuP45vg3AExuPZka7jUY12Icfi5SuSl3PGtA3//BlNPQZQqgQHosqNQm60744DsO4dq+PahU5MXEoLt27Z7E8HP24/kmz+Pr7MvVjKssOb7knsa5E9P6FcxefExmL7YB9vnUkZSKJf9Ek60zUNfPfp01Kwtmp2J7Um4SvpjH1Ymvkv7X3xhuJKC4u5O6eg0J8+YX29/sTFw/0B2VysYhzJXA32bu4bnkGHJo5d+K3iG9bS3O/YGzF/T8P+j9H9PrzBuwf3GpblV7eODc1GQFvFfrjZ+LHxNbT+SNdm8AsPjYYi6nX76nsW6HOXuxRgV/nIxj1YGb2Ytl0Id1kMpNJSU1W8e3u2IAGNYm2LbCVAFqm3Pd2JHPjWP9MLL27+falCmmhtxcsg4exDGsXrH9T+dnJm5oa5+rrCS4dth0bqclF04knmD9+fUAvN72dZnPxtqEdrl5HjETDpTOgnKzSvi9b00B9AnpQ/vq7ckz5vHR/o/KNFZJhAd50i7UFHU3ff1xLiVmkZCRK4M+rIRUbiop3++OIT1XT/0ANwY2r87EnmH4u8stqXvFHsPBPfr1o/p//4PIM6W21wYHU+e3X/Ho16/Y/pZIKVv721zYBgjwbwwe9uf/JYTg4/0fAzCgzgCa+DaxsUT3Ma2eNv38fQpE/XbH7ha/m917StyeLQ2KovBWu7fQKBq2Xd7G9ivb73ms2/Fku2A0KoUsnZEBn++g1+wIGfRhJaRyUwnJytOz+J9oAMb3qEeglzOTetfH30Mm8btXfFwdcLez6uD6pCRi3/+P5XXQzJlofErOv3I6zk5y3Nj5ltRfl/7iYNxBHNWOTGw10dbi3J+Yq5F3f9NUhVwYYc1zcGnPbW9zbt4clYsLhuRkcqLKFmpdx6sOIxuPBODDfR+SZyj/+liPNA9i3bhOaFQKaTl69EbBrxM6y6APKyCVm0rIj3svkZylo7aPCw83lX8k5YGiKIRYwsFt73cjjEauTXsDfVwcmqAgU+Ntdk4yc/VcylfKbGq5KVRywf62pPIMecw+OBuAp8OfJtA18A53SCoEczVyj+rw8KemquP6HPhxGMSfKvE2RavFpV07oOxbUwBjm4/Fz9mPS+mX+PbEt2Uerzia1vSyhIdn5Op5d/1JWX/KCkjlppKRozOwaPsFAF7qXheNWr6F5UVtH3N1cNtbbhK/Xkzmjh0ojo74v/baHfufiUtHCPB1c8THlhFzCWch7QqoHSHY/kouLD+1nMvpl/F19uW5Js/ZWhwJgFoDjy8xZTbOSYEfHoO0kqOhyiMk3DKW1pUpbUw+bV8e/ZLrGdfLPGZxmOu8adUKW6PimLbmmHQqrmDkJ2MlY9XBK9xIzyXI04nBLWvaWpwqhbnGVLSNI6ayDhzgxmefARD4zr9xadMa3/Hj0fiVHKJ8sxK4nWxJhXQEB/vK+Juck8yiI6YyC6+0fAUXrX3Jd1/j4AIjVoJPmEk5/uExyE4ptqvZqTj74CGM2dllnvqh0IdoHdCaHEMOHx/4uMzj3Y43+jVErVJYc+gK/9sQJUPEKxCp3FQidAYjC7eZCtH9q1tdHDTy7StP7CGRnz4piatTXgODAY+Bj+D52GNo/f3xe3kCWn//Eu+7WXbBTpQbO/S3WXhkIem6dBpUa8DAugNtLY7kVly8YdRacAuE+JPw0wjQFU2y51CnDpqAAEReHlkHylazCkxb0m+2exO1ombLxS3svra7zGPeir+7IxN7hvFI8yA+eqwZAIv/iWb+NllBvKKQn46ViHWHr3I1JRtfN0eGta1la3GqHKH5Pje22pYq6GfjEBpK9XffLXWI8ml7iJTS59ptyYULqRdYcXoFYAr9VqtsXDFdUjxewTByNTh6mDIXr3sRjIWjFxVFwbWzqZBmeWxNATTwbsDwhsMBmLFvBjqDrlzGNePv4WQJ+nisdU3eGdAYgI83n+aHPRfLdS6JCancVBIMRsGCfC3/xa6hOGnlw7m8Mfvc2CocvKCfTY05c1C5li45oxCiQKSUDQtmXt4Huixw9Qf/cNvJUQyfHPgEgzDQvVZ32ldvb2txJLcjsCkMXwZqBzj5C2x6w+SoXoDy9LsxM67FOLydvIlOjeaHqB/KbdzieO6BUF5+0JSv6p1fjvPb0XvLuCwpGancVBJ+P3ad6IRMvFy0PNU+xNbiVEm8C4SDX7JyOPitfjZODeqX+t4bGbkkZeahUiAswK2iRLwzdlpyYfe13URciUCjaJjcerKtxZGUhtCuMHgRoJiKbP7zaaHLrh07AJB7+jT6hIRymdLDwYNJrScBpi3MuMy4chm3JCb3rs/IDsEIAZNWRBJx5kaFzne/YT9PIEmJGI2CeX+dA+DZzqG4OmpsLFHVxFQd3Pp+N8X52dwN5i2p2j6utrXo2aG/jcFoYNaBWQAMaziMUM9QG0skKTVNhkC/mabzP9+Dw8sslzQ+Pjg2agRA5u7y85EZWHcgzf2ak6XPsqQMqCgUReG9gU0Y0Kw6OoNg7PcHOXQpuULnvJ+Qyk0lYGtUHKfj0nF31PB0p9q2FqdKY+0aU2XxszFz6rod+NtkJsD1I6bzOt1tJ8ct/HzuZ84kn8HDwYOxzcbaWhzJ3dJhLHTOT7S4/mU4u8VyybVTRwAyd5bf1pRKUfFW+7dQUNgYvZH9sfvLbeziUKsUPhnagq71/cjWGXjmm/2WLyuSsiGVGztHCMEXf5usNqM6huDprLWxRFUbc42paCvVmLpXP5uCmCOlbOpvYy65ENDElKDNDsjUZfL54c8BU7I2Lycv2wokuTd6Todmw0EYYOVouGKKkCrod1OeIdWNfRoztMFQAD7Y+wE6Y/k6F9+Kg0bFwpGtaBXsRWq2jlGL99pNlvTKjFRu7JwdZxM4eiUVJ62K5x6QJvWK5mYiv4q33JTFz6Ygp+NMBTNtarmxw6zEi48tJjEnkWD3YIY3GG5rcST3ikoFj35h2u7UZcGPT0DieVxat0ZxcEAfH0/e+fINqX655ct4OXpxLuUcK06tKNexi8PFQcOSMW1pEOBOfHouoxbv5UZ6boXPW5WRyo2d80W+r82IdiG2zTx7n2Atn5uy+tmYMRgFZ+MyABvWlBLC7vxtrmdc57uT3wEwuc1ktGpp8azUqLUw9Duo3gKyEuH7wah0qbi0aQ2Ub9QUgKejp6Xu2LzIeSRkl4/T8u3wcnHgu+faUcvbmZjELEYv2UdqdsVajaoyUrmxY/ZeSGRfTBIOahUvdq1ja3HuC8zbUtdScyosHLw8/GzMxCRmkqs34qxVE+xto4y7N05D+jXQOEFwR9vIcAtzDs0h15BLm4A2PFjLPhQuSRlxdIenVkG1UEi5CD8+gWu7fOWmHP1uzAyuN5hwn3AydBl8evDTO99QDgR4OPH9s+3xdXMk6noaz3+7n+w866elqApI5caOMfvaPNGmJoGesuK3NfB2dcDdyRSNVlHh4OXhZ2PG7HxYP8ANlereFKQyYym50Am0zraRoQBHbxxlQ/QGFBReb/v6PSuOEjvEzR9GrgEXX7h+BNe0XwHI3L8fkVe+Vb3VKjVvtX8LgPXn1xMZH1mu45dEbV9Xvnu2He5OGvbHJDP+x0PoZKHNu0YqN3ZK5OUUdpxNQK1SGNutrq3FuW9QFOVmjakK2JoqLz8bM6fsITOxHW1JCSH4eL+pPtDAugNp7NPYxhJJyh2fuiYLjtYVx8zdqF21iKwsso8cKfepmvk1Y0jYEMDkXGwwWseK0jjIgyVj2uKoUfHXqXimrj4qC23eJVK5sVPMvjaDWtSglq22G+5TQirIqbi8/GwKcuq62ZnYRpFS+lyI+cd0bgfKzR8X/yDyRiTOGmdebvmyrcWRVBQ1WsGw71DUGlx9UgHIKGe/GzMTW03E3cGdqKQoVp9ZXSFzFEfb2t4sGNkKjUph3eGrvP/bSVlo8y6Qyo0dEnU9ja1RcSgKjOshrTbWJrQCwsHL08+mIOayC41sZbm5tAf02aZih/62tZLkGnItvhHPhD9DgGuATeWRVDD1esGj83ANNEUVZW5eVyHTeDt5WxTluYfnkpxjvUR7DzYMYNYTzQFYuiuGz/O/9ErujFRu7JB5+b42DzWtTl0/G6bTv0+piOrg5elnYyYrT2/xC7LZtlTBLSkb+7Ysi1rG1Yyr+Lv483T40zaVRWIlmg/H9fHxAOREx2LY/X2FTPNE/SdoUK0BaXlpfHboswqZoyQGtazB9EdMXxw+2XKG73fHWHX+yopUbuyM8zcy+P3YdQAm9KhnY2nuT8zh4OW1LVXefjZmzsRlIAT4ujnaLk2AnfjbJGYn8tXRrwDTNoKLVm7l3i9oH/k3Dv5uIBQyl0yDCxHlPodGpeHtDm8DsPbsWo4nHC/3OW7HmM6hTOwZBsD/rT/BL5FXrTp/ZUQqN3bGgm3nEQJ6NfKnUXUbZpy9jzE7FJdHOHhF+NmYOR1r8rexWX6bjBsQe9R0buOSC/Mj55Ohy6CRdyMG1BlgU1kkVkZRcO01EIDM6xr46Sm4frTcp2np35JH6jyCQPC/Pf/DKKwbwfRqrzCe7hiCEDBl5RH+Ph1v1fkrG1K5sSMuJ2Wx7rBJIx8vrTY2o5qL1hIOfjHx3v1uKsrPxozNI6Uu5GclDmwGbn62kQE4l3yO1WdNjp5T205FpcjH2v2G6wOdAchM8IS8dFj2OCTHlPs8k9tMxlXryvHE46w7WzE+PiWhKArvPhLOoy2C0BsFL/1wkAMxSVaVoTIhnwJ2xKLt5zEYBV3CfGkZXM3W4ty3FAwHL0sBzYrwsynIaVsrN3ayJTXr4CyMwkiv4F60CWxjU1kktsGlXTtQq9Gl6MlzagQZcfDDY5CZWK7z+Dr7Mq75OMCUKDI1N7Vcx78TKpXCrCea072BHzk6I88u3U9UfsSkpDBSubET4tJyWLn/CiCtNvZA7TI6FVeUn40ZIUSBgpk2UG7spOTCP1f/YefVnWhUGia1nmQzOSS2Re3mhnNzU1RRZvXnwLMWJJ6DH4dCXvmmdHiy0ZPU86pHSm6KpTCrNdGqVSx4qjVtQqqRlqNn9JJ9VqmFV9mQyo2d8OX2C+QZjLStXY32od62Fue+x1yG4V4sNxXpZ2PmRkYuSZl5qBQI87eBchN/0vTtWOMMwR2sPz+gN+qZtX8WACMajiDYI9gmckjsA0uV8EMnTFmMnavB1QOw6hkw6MttHq1Ka8lcvOrMKqISo8pt7NLi7KBm8Zi2NAx050Z6LqMW7yM+LcfqctgzUrmxAxIzcvlx7yUAJjwYJtPF2wE3C2jenc9NRfvZmDFvSdX2ccXZQV3u498Rs9Wm9gOgsU2k1tqzazmfeh4vRy9ebPaiTWSQ2A8W5WbPHoR3PXhyhane2dnN8NtEk7WxnGgb2Jb+tftjFEY+2PuB1Z2LATydtXz3bDuCvV24lJRfaDNLFto0I5UbO2DJzmiydQaa1vCka5ivrcWRUEC5uUvLTeLiivWzMXO/+9uk56UzL3IeAC81fwlPR0+byCGxH5ybNUXl5oYxNZWckychuD08/g0oKjj8A/z138I3CAHGe1dKJreZjLPGmcgbkfx24bcySn9v+Hs48cNz7fFzd+RUbDrPykKbFqRyY2NSs3V8t+siABMerCetNnaC2efmempOqR8WWQcPcmNOxfnZFMSmkVK6bLiYn+reRsrN18e+JiknidoetXmiwRM2kUFiXygaDS7t2wOQuXOnqbHhQzAgv6L3jlnwT/55xg1YORoiPrzn+QJdAxnbfCwAnxz4hPS89HseqywE+7jw/XPt8HDScPBiMi8tO0ieXhbalMqNjfluVwzpuXoaBLjTu5FMF28vVHPR4nEX1cH1yclcnTylQv1sCnLals7El3aDPgfcg8CvgdWnv5J+he9PmjLRvtbmNbQqrdVlkNgnrp06ApC5s0CdqdZjoPEg0/nW6bCoK8xrBxd3gn/DMs03qtEoanvUJjEnkfmR88s0VlloGOjBN8+0xUmrYtvpG7y26sh9X2hTKjc2JDNXz+Kd0YCphpRKJa029sLdVAc3+dlMq3A/GzMGo+BMnNlyY4NEjzYuuTDn0Bx0Rh0dqnega82uVp9fYr+Y/W6yIiMxZhb4u31iKTQbZjq/fgQMuTB4EYQPLtN8WrWWN9u9CcDyU8s5k3ymTOOVhdYh3iwc2RqNSmH9kWu8u/54oUKbQoj7SuGRyo0N+XHvJVKydIT6ujKgWZCtxbF7hNFYpCquEAJRhn3z22GpMXUHv5vExYvJ3F7xfjZmYhIzydUbcdKqCLZFxfjz+cn76vaw+tSR8ZFsjtmMgsJrbV6T27iSQjjUro02KAh0OrIOHLh5QVFg0AJob9pGIi/TlOhv7b8g7XqZ5uxUoxO9gnthEAZm7J1h08rd3Rv488mwFgB8v+cS/9tgiuRKyMhl3LJDfPbnWZvJZm2kcmMjcnQGvtxxAYCXutVFLa02dyThi3lcfWUiaVu2krZhA1lHj3J14qskzKsYc3BpakxZ08/GjMWZOMDd+r836bEQdxxQoI51lRujMPLR/o8AGBI2hAbe1t8Sk9g3iqLg2jk/amrXrsIXVWpo/qTpvH5/08+jP8HnrWHHbNDdeyj1621fx0ntxIG4A2yM3njP45QHA5sHMaxtLQC+3hFN30+30/uTCPZGJ1E/wEYBCDZAKjc2YuWBy9xIz6WGlzODWtawtTh2jTAaydq/n+yjR0jfupWrL7/M1clTuDh0GOlbtpAREUHi11+TdeAAxpzyy/UQ6muyipS0LWVtPxszNnUmvrDN9LN6c3D1serUG6M3cizhGC4aFya0nGDVuSWVB0tI+K3KTUG6vwEv/AU124IuE/58H+a3h6jf7ilkPMgtiOebPg/A7AOzydTZNqneh481Y2y3OgCcjkvHYBQsf6EDDzerblO5rInG1gLcj+TpjSyKMFltxnarg4NG6pi3IoxGsiOPkLZpI+mbNqOPL1AkTq0Gg8FkahaCnOPHyTmeX6VXo8GpUSOcW7TAuUVzXFq2RFO9+j1tX1i2pYrJdWNtP5uCmAtm2tzfxork6HOYc2gOAM83fR5fZ5kyQVI8Lh06gKKQe/Ycurh4tAH+Ny+6B0K3N0w/3QPh2T/g2CrY+q6pFtWKp0xFYPvNBP9GdzXvmCZj+OX8L1xOv8yiI4uY3GZyua7rbpnWz+QsvTDiAmk5ekZ8tYePn2jGgw3vj8AVqdzYgJ8PX+VqSjZ+7o480aaWrcWxG0S+opK2YSNpmzahv35zL1zl7o57r154PNQftYcHMcOGE7L8RzAayT4cSXZkJFmRhzHcSCDn2DFyjh0j+XtTRI3G3x/nli0tCo9TeDgqB4c7yhOar9zEppnCwQsmy7O2n01BbBYpZTQW8LexrnLz/cnvic2MJdA1kFGNR1l1bknlQlOtGk6NG5Nz4gSZu3fhNWjQzYvugdDjzZuvVSpoPgwaPgz/fAK7vjBZJxd0hrbPQfc3waV0GeMd1Y680e4Nxv85nu9Pfs+gsEHU8axTrmu7GxRFYUCzIBZGXKC2jwsxiVk8u/QAozuG8NZDjXDS2iD5pxWRyo2VMRgF87edA+DFLnWq/C/YnRBCkBsVRdrGjaRt3ITuyhXLNZWrK249H8SjX39cH+hsUUiyT5wAQNFqcQ4Px6VVK8tYuqvXyI6MJPvwYbIjI8k5dQp9fDzpmzeTvnmz5T6n8HCTspOv9BT6dpdPNVcHPJ21pGbruJiUScN8S4kt/GzMZOXpuZgfmm71ban4E5AZD1pXqNXOatMmZCfw9bGvAXi11as4aZysNrekcuLaqZNJudl1i3JTEo5u0PP/oOUo2PIORP0K+76EY6vhwbeh1RhQ3/njsmvNrnSr2Y2IKxHM2DuDL3t/aRdO77OHNmfDsVgW/xPNd7svsvt8Ip8Nb0njIBtYf62EVG6szG9HrxGTmEU1Fy0j2t+ftXCEEOSeOUvaxg2kb9xE3sWLlmuKszPuPXrg3r8fbl26oHIq/QeZoig41KyBQ80aeA54GABjVhbZx4+THXnEpPRERmJISrKcs3QpAJqg6ri0yLfutGyBU8OGKFottX1cOHo5mZgbGTQM9CjkZ+P+yACr+dmYORuXgRDg6+aAr5uVyx7YqOTCF4e/IEufRVPfpvQP7W+1eSWVF9fOnUj86isyd+9GCFF6BcM7FIb9YLLebHrTVEPt9ymwfwn0nwmhd049MK3tNHZf282e63vYemkrvUN6l20xZcDf3ZGJPcOoVc2FdwY0pmt9P15bdYSz8RkMmreTqf0a8Gzn0CqZhkQqN1bEaBTM+9tktXm2cyiujvfXf3/u+fOkbdxE2saN5J0/b2lXHB1x69YNj4f649atGypn59uOo/Hzw3f8eDR+fnecU+Xigmu7dri2M1kahBDoLl3K38aKJPtwJLlnzqC/dp20a9dJ27DBJJOTE05NwhnmGET/mEu4XViFbv5srr/9Nvq4OBQXFxzu0ZenLNi07IIN/G1OJ51m7dm1AExtOxWVIv3TJHfGuVUrFCcnDDcSyD1z9u6tq3W6w792wMFvTGUb4k/At49Ao4HQ5z9QrXaJt9byqMUzTZ5h0dFFfLT/IzoHdcZFa4OUDZjKM0zqfXPt3er7sWliF6atOcbWqDj++3sUEWduMPuJ5vh7VC2L6P316WpjtkTFcSYuA3dHDaM71ba1OFYhLyaGtE2bSNuwkdwzNxNcKVotrl274tG/P27du6N2K73PitbfH7+X7y1aRlEUHEJCcAgJwfPRRwEwZGSSc/wY2YcPmxSeyCMYU1PJPnCQlhw03XgVznXtZomkUDQanBo3vicZykJUvjNxQ2s7E+dlwcXdpnMrKTdCCD4+8DECQd/afWnh38Iq80oqPyoHB1zatCHzn3/I3LXr3raO1Rpo9wI0eQy2zYD9iyFqPZzZDJ1fgQcmgUPxz63nmj7Hr+d/5VrmNb4+9jWvtHqljCsqP3zcHPlqdGuW7b3Ef38/yY6zCfSds50PH2tGn/BAW4tXbkjlxkoIIfjiL5PVZnSnEDydq27K+LwrV/J9aDaSezLq5gWtFrdOnXDv3w/3nj1Ru9tHzgW1myuuHTrg2qEDYIqEyouJIftwJFF/7iRl/0FC0uNQ8hUbbXAtai9fjsbHuqHQYEPLzaVdpqyuHjXBN8wqU26/sp291/eiVWl5tdWrVplTUnVw7dTJotz4PDPm3gdy8YaHPobWz8CmaRC9HbZ/DIeXQe/3oenjRTJ1O2ucmdp2Kq9ue5WlJ5byaL1HCfEIKduCyhFFURjZIYQOdbx5ZXkkJ6+n8eL3BxnRPph3Hm5cKHiisiKVGysRceYGx66m4qxV82znUFuLUyaE0QiKUmhLJu/aNdI2biJ90yZyjh272VmtxrVDBzwe6o97r16oPe2/erOiUuFYpw6Oderg1LYHL83fRaiTYE1YGnH/+Q81Pv3UJooN2DBSqmBWYitsxemMOmYdmAXAyMYjqeles8LnlFQtzMn8svbvx5iXV6oIydsS0BhGr4dTv8HmtyHlIqx9HvZ/ZQodr9GqUPcHgx+kU/VO7Lq+i5n7ZjK/53wURTFlVUfYxRZrPX931o3vxOw/zvDl9gv8uPcSey4kMnd4S5rUsP9n9e2Qyo0VKGi1eap9MD7WdgQtZxK+mEfuuXP4ThhP1p69pK5ffzPPDIBKhUu7dnj07497n95oqlWznbBlxFwdPDpHQRXe1Kay3EjPJTEzD0WBMH9rKzcV729jFEYUTErzqtOriEmLoZpjNZ5r8lyFzSmpujjWr4/a1xdDQgLZhw7j2qF92QdVFGj0CNTrDbu/gB2fwOW98NWD0PIpePD/wD0gv6tCsEcwu6/v5p+r/7AxeiPtq7fnf3v/Rz2veoxrMa7s8pQDjho1bz3UiK5hfkxZFcmFG5kMnr+T1/o04IUudSqts7HtVcf7gL3RSRy4mIyDWsULXW2X96A8EAYDwqAn4++/iR74KHEffGBRbBzq1CHgnX8TFrGNkKXfUG3Y0Eqt2MDNcHCA62nZNpXFbLWp7eNqXbNx2nVT1AiKydGyglhwZAFTIqYQkxrDgiMLAAhwDWBZ1LIKm1NSdVEUBdeO+VXCb5et+F7QOkHX1+DlA9BsOCDg8A+mUg4754I+D4A2gW1wVJu+zE7bMY2eq3qy69oum+a/KYkHwnzZNLErfcMD0BkEMzaeYuTivcSmll/Wd2silRsrYI6QGtq2JgGV1CNdFxdPwoIFnO/dh8RFXyJ0Oss1bc0a1P55HXU3/I73U0+VKoqpMmGuMXVFcSl1lFZFcMqcmdja9WEu5G9JBbUsdUKzeyHYPZidV3cy6JdBpOSmoFJUxGbGUterboXNKanalKoUQ1nwCIIhi+C5LRDUCvLSTXly5neAM5vpG9KHXwb9go+TaRvbIAxk6jKZf2Q+a86sIdeQWzFy3SPVXB1YOLI1M4c0xVmrZtf5RPp9tp1Nx8tWXNQWSOWmgjl8KZkdZxPQqBT+1bVyPaSFwUBGRASXx0/g3IMPcuOzueiuXUPl4UG10aOo8emnANT47DOcGza0sbQVR20fUxjnOYMzfi9PQOtfNOGfNbCZM3EFb0mdTjrNf/f8l//t/R9Z+iwMwgBAHc86/Pzoz/St3bdC5pVUfVw7mSw3OSdOoE9OrriJarWD5/+ER+eDqz8knYcfh8IPjxGUlc78XqbivgPrDsRd6050ajTTd0+n7+q+fHX0K1JzUytOtrtEURSGtwvm91ceoGkNT1KydIz94RDTVh8lM1dva/FKjfS5qWDMVptBLWtQy9s2uQ7uFl1cHClr1pCyejX6azc1dufWrak2bCjuffqgcnKyZAqu6pj9bm5XHdwamAtmNqpuReWmgkouZOmy2BSzidVnVnMs4aYDerB7MF1qdmFZ1DL+98D/8HG2jeO2pGqgDQjAoV5d8s6dJ2vvXjz69au4yVQqk99No0dgxyzYPR/O/2kqyNl8CABPhTzEW1eiWe2l4QfddWIzY5l7eC5fHfuKIWFDGNV4FDXc7KOQch0/N9a81IlPt55hYcR5Vhy4zL6YJOYMa0HzWl62Fu+OSOWmAjl5LY2tUfEoCozrbt9WG2EwkLFjBykrV5GxbZvpQw1QeXriNehRvJ54Asd69WwrpI0Izd+WKqk6uDUwGAVn4syWGyvmuIk7BlkJ4OBmqqBcRk4nnWbVmVX8duE3S+VkjUpDz+CePFH/CdoGtuVU0inpZyMpN1w7dSLv3Hkyd+6qWOXGjJOHKUS81dOw5jm4dtiUH6dGdVj2GK5GFU/3ncGIliPYHLOZpceXcjr5NMuilvHTqZ/oE9KHMU3G0NjH+nm0bsVBo2Jav4Z0DfNj8spIohMyeWzBLib1rs/YbnVR27GzsVRuKpB5+TWkBjQLoo6fm42lKR7d9eukrFlLypo1hQpVurRpg5fZSuNYfHTX3WQKrsyE5G9LxdjQcnMxMZNcvREnrYpga1oALSUXuoDm3kJpb2elebz+4wysO1BaaCQVhmunTiR/9z2Zu3bdXSmGsuJTF17cBsfX4PfrK7yUnIqfLhcMRvjtVbTHVjGg8aM83G0uuzNjWHp8Kbuv72ZjzEY2xmykfWB7ng5/mgdqPGDz+lQd6/qwcWIX3lp3jA3HYvl482m2n7nBp8NaEOR1+4zytkIqNxXEufgMNhwzKQvje9iX1Ubo9WRs30HKypVkbN9usdKoPT3xHDwYr6FP4Fjnzt78ZckUXJkwW27i0nLJytPj4mD9Pxuzv039AHfrflsqg7/NqaRTrD6z+rZWmuJyffg5+/FS85fwc67aSrPEOri2bQtaLbqrV9FduoRDiJWT6TV5DD/vuoz7shu0+xdc2Q/XDkHMDojZgbLhdToFd6BT40c51eApll7ayKboTeyN3cve2L3U86rHmPAxPBT6EFq17ZK/erk4MG9EK1YfvMK760+wNzqJfnO288GQpgxoFmQzuUpCKjcVxIJt5xECejcOsH6q/BLQXbtGyuo1JitNXJyl3aVdO7yGDsW9d68SrTT3M14uDni5aEnJ0nExMYtG1a3/fpr9bawSKZWfpBFdFlzaY2qr08PUrrp9DEJ5WGn8XPzsJgeIpPKjcnXFpXlzsg4cIGPnTrytrdwUpMUIeOgjSL5oqjx+8he4sg8u7YZLu2kIzKzZlolhw/helcmaS1s4l3KOf+/8N3MPz2Vko5E8Xv9x3B1sk91dURSeaFOLtrW9mbgikiOXU5jw42G2nb7B9IHhuNlRvUT7kaQKcTkpi58jrwIwoYdt/VRMVprtpKxYScaOHTetNF5eJivNE0/gWKdyZ0y2BiE+rqRkpRCTkGkT5caqkVIRMyE+Cho/CoY8cK8Bf70P/o2hx5vF3nKvVhqJxBq4du5E1oEDZO7ahfeIEbYWB6qFQKcJpiP16k1F59JuuLKf6lf2MxUYG9ScVdUbsyzzPPFZ8Xxy8BMWHV3EE/Wf4KlGTxHoaptaULV9XVk9tiNz/zzLvL/PsfrgFfZFJ/HZ8Ba0DLaP3GZSuakAFkScx2AUdAnztZlXue7q1fyIpzXo4+Mt7S7t2+M19Ance/cuezry+4hQHxeOXE4hJjHLJvOfjjOXXahAxSovExLPQU4qnNsCp343tWclwMWd0GRIoe7Sl0ZSWXDp0AE+m0vWnr0IvR5Fo0EIAUKg3MEaWS64B0K3N0w/b8WzBnQYazrSY28qOhd34nHtCM9dO8Io4PcaDfjW1YHzulSWnljKDyd/oH9of54Of5oG3g0qfg23oFWrmNKnAV3C/Ji0IpJLSVk8tmAXr/YMY/yDYZbtcyEEQmD1TMdSuSlnYlNzWH3gCgAvP1ixBQZvrfEkdDrSt20jeeVKsv7Zaalgra5WDc8hg/F6/HEcQ6WV5l4wJ/KLsUHEVFae3uLM3LCsYeBCQNo1SDhjUmQSzkDCWdORdqX4e6qFwJgN4GbygZFWGkllI2P7dtBoMGZkkH3sGA7BwcS+9z6OYWHW8Rt0DyzR6lmkX7sXTEfGDVMdq5O/4BC9ncFXT/Mo8I+zE9/4BXJArefXC7/y64Vf6RzUmTFNxtA+sL3VnY/bhXqzYWIX/v3zcX49co1Ptp7lr9PxfP5kK5wd1Lzz83HqB7gzqfc9VGYvAzZXbubPn8/HH3/M9evXCQ8PZ86cOXTp0qXE/hEREUyePJkTJ04QFBTE1KlTGTt2rBUlvj1fbr9AnsFIu9retAutuGyuwmgkfvYn5J4+jffoUWQdOkTKqtUYEhMtfVw6dqDa0KG49ewprTRlxFJjygYRU2fjMhACfN0c8C1tXTJddr7ykq+4JJ7NV2TOge42a3DxwegbhuJdD8XRHfYugCFfkungwsYzq1l7dq200kgqHU4NGpiKVgJXX5mIISPDVCC3Xj1yTp9B410NdbVqKBqbfyTexM0P2jxjOrKS4NTvqKLW0/X833S9FMNxBwe+8XRnq6srO6/tZOe1nTTybsiY8GfoFdILrUpbSNGpyIKdns5a5g5vQTUXLd/tvkjk5VR6zNqGq6MGtUqxicOxTd/JFStW8OqrrzJ//nw6d+7MokWL6N+/PydPniQ4OLhI/+joaB566CFeeOEFfvjhB3bu3Mm4cePw8/Pjscces8EKTOjyclGp1KTkGPhx30UAxnWtjS4vF63D7T+MhF6PIT0dQ0oKxtRUDOYjpcB5aiqG1BQMqakYU1IxpKRgSE+3+M9k/vPPzQEVBbce3QmYNq3CogKMBj2KoipkzhVGI0IYUakr7lfKVvPq9TqCq5nKZpgT+RkNBozCiEZTcdELer0OlaIq5G9TaF4hTGbshDP5ykuBI/UyIIofWFGDdx3wDTMdPmHgW9907uLNgsh5nE85z5sh/Ug8/DU/HP+K32J3WTIHSyuNpLLh0a8fedeuceOjj9HfuAGY/joS5s8nYf58Sz+VpyeaaiZFR+3tna/0eKOuVs107u2Nupq3RRlSOd8+DPpW6zpwb9thLt7QapTpyE6BM5tocvIXZp/byuXkVL7zdOdnN1eikk4xbcc03HY5E+RRi896fEZN95okZifyvz3/NRXsbDn+bv7rSo2iKEwf0JAhzYN4ask+MvMM1PN3Y+FTLfFxtX6UlyKEKOEJWPG0b9+eVq1asWDBAktbo0aNGDRoEDNmzCjSf9q0aaxfv56oqChL29ixYzly5Ai7d+8u1ZxpaWl4enqSmpqKh0f5+C/89GxnnOJTudDzcXZGQ3Mljg5HIzC4OtKw9xBEWgYiPRORnoFIy0SkZ2LMPyezbD4ceRrQokbRGxBBviwZnINfQCCjHni3XNZWHN/98x4Xs2/wcrupeHkGk5J6ic/3fURtZ78qOe+sjW8Sr08i9trDZBp9eKO3Fyuj5uKrqsbIzv9GIDBiemCZ/oEQRss5CIxCQH6LEJYzy7cpMFXF5mYv1h74nGRjGgHaRzh+JY+u1RM4ZdyDn1Doo3YhLyMenTEXHQo6RSFPUdAp5P9U0Gkc0Tl5kOfojs7BhTytEzq1I3kqNXphIM+Yh86gM/006sgzmH7m6HPQGXVF/h98nXwZHT5aWmkklRKRl0fcrFkkf/c9Lu3bg8GAPjkZQ1IShpQUyzb+3aA4O5uUIe8CClA1b4tilLFzF4bERPwmT8Kxbl1EXl75boflpMHZP+DkzySf38oKFy3LPdxJUpsK6ypANY0LafocEEbqq5yp7hGCSlGhUalRoUKtqFCr1KgUtelcUee/VqFWNKgVNSqVGk3+z5vXNahValMflRqVSkPEqdXc0KXRsfqjfLdPx7whj7Ly4ETqudVi3ODlZV7u3Xx+20y5ycvLw8XFhVWrVjF48GBL+8SJE4mMjCQiIqLIPV27dqVly5Z89tlnlrZ169YxdOhQsrKy0GqLaoe5ubnk5t4sTpaWlkatWrXKVblZ/f5oQtbux60MxVOzHCHDCTKcId1JIcP55usMZ+XmeYFrmU6g1yiExgo+/MbAtGfURAfab8ZISeXEy8GD/+s4nZ4hPaWVRlKpyT5xgpjHHqf2mtU4h4db2oXBgCEtzaToJCWhT0rGkJyEITnZdJ6UhCE5CX1yiqVPweLBpUHl7o6iVoOiEPju/5V/tuTcDDi3hZwT61h/dRvfuLtwpZjPRGvhYDTiIkwWnbfrDaNvl3fKPObdKDc225ZKSEjAYDAQEBBQqD0gIIDY2Nhi74mNjS22v16vJyEhgerVqxe5Z8aMGbz33nvlJ3gxPP5/37Gh8Xxc//05CpCnhqs+kOYKmfmKSWa+cmL6qZh+OkGms0mxMajvXilRAyqjQMnXTxUhcDCatPWKRgA6BYSioAiBVtxf8zoKgQrT3Eq+DDcPUUK7ub8oob3oGAYgTqMmR6XC2WikrsoNV2dPHBzc0Tp54uDkhVbthIPaAY1Kg4PaAa1Ke/OnygGtWotWlX+o89sK9sn/ae5nfh2dEs3zW55nUZ+v7CIVvERSUShqNZpq1dBUqwZ175x0VQiBMTMTQ77lR5+UhCFfITJZg/Lbk5PRJySgj43FmJ6Oc8uW1PziczQ+FWD5dHSD8ME4hQ9maF4Wjx1fzaqd/+V/Ho6MS88h0K0GBpUKgzBiEEaMQmDAiEGYLM96YcSY/9qAyZJsQJiO/D7m10Yh0EOBNtO5HshBcEqrIUOtooFBw+ePrMDH17rOxGAHDsW3enbfKT12cf2Lazfz5ptvMnnyZMtrs+WmvOn/2Ev8nZNJ9f8uIfHfz/PQk1PKfY6SOPn7POALZjQdR+OHK2Y/tdh5T/3MsL3v8FOH/9K44aAqP+/m3T/y2pkZfNzgLfp2tF6uDPO8/2n4tlXnTchOsNpcEok1KK+SMYqioHZzQ+3mBqX4PDFbjAL+/XbFKDa34uCCutVomrl4wN536NbrY6s+K/ccXsULR9/nlZZv2USxARsqN76+vqjV6iJWmvj4+CLWGTOBgYHF9tdoNPiU8Avj6OiIoxWy7iqKAmobmeyrubPqAYXHq9kma6VEIpFUBu6XkjG2xsNZW+inLbDZBrqDgwOtW7dmy5Ythdq3bNlCp06dir2nY8eORfr/8ccftGnTplh/m/sGbw9WdVGDt32UeZBUDWSNJ4mkcuPn6GUq2OnoZWtRrI5NvQMnT57M119/zZIlS4iKimLSpElcunTJkrfmzTffZPTo0Zb+Y8eO5eLFi0yePJmoqCiWLFnC4sWLee2112y1hEIE1W/Jkc4+BNVvadV5/bzDeMmjCX7eFZs08H6ft3ZgIwYY61I7sNF9Ma+5xpOfi1RuJJKyUF7bYXeLn29DxjV/CT/fhtad10bP6ILYNBQcTEn8PvroI65fv06TJk349NNP6dq1KwBjxowhJiaGbdu2WfpHREQwadIkSxK/adOm3VUSv4oIBZdIJBKJRFKxVIpQcFshlRuJRCKRSCofd/P5LZNWSCQSiUQiqVJI5UYikUgkEkmVQio3EolEIpFIqhRSuZFIJBKJRFKlkMqNRCKRSCSSKoVUbiQSiUQikVQppHIjkUgkEomkSiGVG4lEIpFIJFUKqdxIJBKJRCKpUtisKritMCdkTktLs7EkEolEIpFISov5c7s0hRXuO+UmPT0dgFq1atlYEolEIpFIJHdLeno6np6et+1z39WWMhqNXLt2DXd3dxRFKdex09LSqFWrFpcvX74v6lbJ9VZt5HqrPvfbmuV6KzdCCNLT0wkKCkKlur1XzX1nuVGpVNSsWbNC5/Dw8KgSv0ilRa63aiPXW/W539Ys11t5uZPFxox0KJZIJBKJRFKlkMqNRCKRSCSSKoVUbsoRR0dH3n33XRwdHW0tilWQ663ayPVWfe63Ncv13j/cdw7FEolEIpFIqjbSciORSCQSiaRKIZUbiUQikUgkVQqp3EgkEolEIqlSSOVGIpFIJBJJlUIqN8D27dt55JFHCAoKQlEUfv75Z8s1nU7HtGnTaNq0Ka6urgQFBTF69GiuXbt2x3GPHTtGt27dcHZ2pkaNGrz//vtFamJERETQunVrnJycqFOnDgsXLizv5RXL7dZ8K//6179QFIU5c+bccVx7XXNp1hsVFcXAgQPx9PTE3d2dDh06cOnSpduOW1nXm5GRwYQJE6hZsybOzs40atSIBQsW3HFce1zvjBkzaNu2Le7u7vj7+zNo0CBOnz5dqI8QgunTpxMUFISzszPdu3fnxIkTdxy7Mq63qj2zdu3ahVqtpl+/fhU6z5o1a2jcuDGOjo40btyYdevWFbo+ffp0FEUpdAQGBlaoTPPnzyc0NBQnJydat27Njh07gKr3HlcIQiI2bNgg3n77bbFmzRoBiHXr1lmupaSkiF69eokVK1aIU6dOid27d4v27duL1q1b33bM1NRUERAQIIYPHy6OHTsm1qxZI9zd3cWsWbMsfS5cuCBcXFzExIkTxcmTJ8VXX30ltFqtWL16dUUt1cLt1lyQdevWiebNm4ugoCDx6aef3nZMe17zndZ77tw54e3tLV5//XVx6NAhcf78efHbb7+JuLi4EseszOt9/vnnRd26dcXff/8toqOjxaJFi4RarRY///xziWPa63r79u0rvvnmG3H8+HERGRkpHn74YREcHCwyMjIsfWbOnCnc3d3FmjVrxLFjx8SwYcNE9erVRVpaWpVbb1V7Zj333HNi4sSJwtXVVVy8eLFMY+n1emEwGIq079q1S6jVavHBBx+IqKgo8cEHHwiNRiP27Nlj6fPuu++K8PBwcf36dcsRHx9fJnlux08//SS0Wq346quvxMmTJwv9H1S197gikMrNLdzug97Mvn37BHDbP7T58+cLT09PkZOTY2mbMWOGCAoKEkajUQghxNSpU0XDhg0L3fevf/1LdOjQ4d4XcA+UtOYrV66IGjVqiOPHj4uQkJA7KjeVZc3FrXfYsGFi5MiRdzVOZV5veHi4eP/99wu1tWrVSvz73/8ucZzKst74+HgBiIiICCGEEEajUQQGBoqZM2da+uTk5AhPT0+xcOHCEseprOstjsr6zMrIyBDu7u7i1KlTYtiwYeK9996zXPv7778FIH777TfRrFkz4ejoKNq1ayeOHj1q6fPNN98IT09P8euvv4pGjRoJtVotLly4UGSeoUOHin79+hVq69u3rxg+fLjl9bvvviuaN29e/ossgXbt2omxY8cWamvYsKF44403iu1fWd/jikJuS90DqampKIqCl5eXpW3MmDF0797d8nr37t1069atUPKkvn37cu3aNWJiYix9+vTpU2jsvn37cuDAAXQ6XUUu4Y4YjUZGjRrF66+/Tnh4eLF9qsqajUYjv//+O/Xr16dv3774+/vTvn37Ils5VWW9AA888ADr16/n6tWrCCH4+++/OXPmDH379rX0qazrTU1NBcDb2xuA6OhoYmNjC8nl6OhIt27d2LVrl6Wtqqy3pD6V8Zm1YsUKGjRoQIMGDRg5ciTffPNNkS2U119/nVmzZrF//378/f0ZOHBgIVmysrKYMWMGX3/9NSdOnMDf37/IPCWtq+DvB8DZs2cJCgoiNDSU4cOHc+HChXJc7U3y8vI4ePBgEZn69OlTRCYzlfU9riikcnOX5OTk8MYbbzBixIhChciqV69OcHCw5XVsbCwBAQGF7jW/jo2NvW0fvV5PQkJCRS2hVHz44YdoNBpeeeWVEvtUlTXHx8eTkZHBzJkz6devH3/88QeDBw9myJAhREREWPpVlfUCzJ07l8aNG1OzZk0cHBzo168f8+fP54EHHrD0qYzrFUIwefJkHnjgAZo0aVJItuLkMl+DqrPeW6nMz6zFixczcuRIAPr160dGRgZ//vlnoT7vvvsuvXv3pmnTpnz77bfExcUV8pfR6XTMnz+fTp060aBBA1xdXYvMU9K6Cv5+tG/fnu+++47Nmzfz1VdfERsbS6dOnUhMTCzPJQOQkJCAwWC4o0xmKvN7XFHcd1XBy4JOp2P48OEYjUbmz59f6NqMGTOK9FcUpdBr8zeOgu2l6WNtDh48yGeffcahQ4duK0dVWbPRaATg0UcfZdKkSQC0aNGCXbt2sXDhQrp16wZUnfWCSbnZs2cP69evJyQkhO3btzNu3DiqV69Or169gMq53gkTJnD06FH++eefIteKk6tgW1VbL1TuZ9bp06fZt28fa9euBUCj0TBs2DCWLFli+R0F6Nixo+Xc29ubBg0aEBUVZWlzcHCgWbNmAFy6dInGjRtbrr311lu89dZbJa6rYFv//v0t502bNqVjx47UrVuXb7/9lsmTJ5fHkotwJ5mgcr/HFYlUbkqJTqdj6NChREdH89dff92xfHxgYGARDTs+Ph64qSmX1Eej0eDj41OO0t8dO3bsID4+vpDGbzAYmDJlCnPmzLGYL2+lsq7Z19cXjUZT6KEH0KhRoxI/NKDyrjc7O5u33nqLdevW8fDDDwPQrFkzIiMjmTVrVqEPjoLY+3pffvll1q9fz/bt26lZs2YhucH0jbR69eqF5Lr1G2pBKut6zVT2Z9bixYvR6/XUqFHD0iaEQKvVkpycfNt7C34IOzs7W14HBQURGRlpuWbeyitpXbf7/XB1daVp06acPXu21GsqLb6+vqjV6jvKVNnf44pEbkuVAvMv0NmzZ9m6dWup3uCOHTuyfft28vLyLG1//PEHQUFB1K5d29Jny5Ythe77448/aNOmDVqttlzXcDeMGjWKo0ePEhkZaTmCgoJ4/fXX2bx5c4n3VdY1Ozg40LZt2yLhw2fOnCEkJKTE+yrrenU6HTqdDpWq8J+/Wq22WLGKw17XK4RgwoQJrF27lr/++ovQ0NBC10NDQwkMDCwkV15eHhEREXTq1KnEcSvreqHyP7P0ej3fffcds2fPLvQcOnLkCCEhISxbtszSd8+ePZbz5ORkzpw5Q8OGDYsdV6PRUK9ePcthVm5KWtftfj9yc3OJiooqpDCXFw4ODrRu3bqITFu2bLHIVNnf4wrHer7L9kt6ero4fPiwOHz4sADEJ598Ig4fPiwuXrwodDqdGDhwoKhZs6aIjIwsFAaYm5trGeONN94Qo0aNsrxOSUkRAQEB4sknnxTHjh0Ta9euFR4eHsWG3E2aNEmcPHlSLF682Gohd7dbc3EUFy1VmdZ8p/WuXbtWaLVa8eWXX4qzZ8+Kzz//XKjVarFjx44qud5u3bqJ8PBw8ffff4sLFy6Ib775Rjg5OYn58+dXuvW+9NJLwtPTU2zbtq3Q32dWVpalz8yZM4Wnp6dYu3atOHbsmHjyySeLhIJXlfVWhWfWunXrhIODg0hJSSly7a233hItWrSwREuFh4eLrVu3imPHjomBAweK4OBgyzrN0VJ3YufOnUKtVouZM2eKqKgoMXPmzCKh4FOmTBHbtm0TFy5cEHv27BEDBgwQ7u7uIiYmptzWXRBzKPjixYvFyZMnxauvvipcXV1FTExMlXiPKxqp3IibIYW3Hk8//bSIjo4u9hog/v77b8sYTz/9tOjWrVuhcY8ePSq6dOkiHB0dRWBgoJg+fbol3M7Mtm3bRMuWLYWDg4OoXbu2WLBggRVWfPs1F0dxyk1lWnNp1rt48WJRr1494eTkJJo3b14k50tVWu/169fFmDFjRFBQkHBychINGjQQs2fPLiR7ZVlvSX+f33zzjaWP0WgU7777rggMDBSOjo6ia9eu4tixY4XGqSrrrQrPrAEDBoiHHnqo2GsHDx4UgJg9e7YAxK+//irCw8OFg4ODaNu2rYiMjLT0La1yI4QQq1atEg0aNBBarVY0bNhQrFmzptB1c24krVYrgoKCxJAhQ8SJEyfueY2lYd68eSIkJEQ4ODiIVq1aWcL9q8J7XNEoQtwSVyeRSCQSiZ2zbds2evToQXJycqHwZ4kEpM+NRCKRSCSSKoZUbiQSiUQikVQp5LaURCKRSCSSKoW03EgkEolEIqlSSOVGIpFIJBJJlUIqNxKJRCKRSKoUUrmRSCQSiURSpZDKjUQikUgkFcCMGTNo27Yt7u7u+Pv7M2jQoCJlXoQQTJ8+naCgIJydnenevTsnTpwo1OfLL7+ke/fueHh4oCgKKSkpReY6dOgQvXv3xsvLCx8fH1588UUyMjJKJeeVK1dwcHAosWxFeRIREUHr1q1xcnKiTp06LFy4sND1pUuXoihKkSMnJ+eu5pHKjUQiqTCmT59OixYtbDb/O++8w4svvmiTuXNzcwkODubgwYM2mV9ieyIiIhg/fjx79uxhy5Yt6PV6+vTpQ2ZmpqXPRx99xCeffMIXX3zB/v37CQwMpHfv3qSnp1v6ZGVl0a9fP0sF81u5du0avXr1ol69euzdu5dNmzZx4sQJxowZUyo5ly5dytChQ8nKymLnzp1lWjOYFDa9Xl+kPTo6moceeoguXbpw+PBh3nrrLV555RXWrFlTqJ+HhwfXr18vdDg5Od21EBKJRHLXUEL6d/Px9NNPi/T0dJGQkGAT+WJjY4W7u7uIjo62tHXr1k1MnDixSN9169YJ8+OwW7dut11XSEiIEMJUwmLChAkiNDRUODg4iJo1a4oBAwaIrVu3Wsb97LPPRM+ePStymZJKRHx8vAAsZRSMRqMIDAwUM2fOtPTJyckRnp6eYuHChUXuN5dVSU5OLtS+aNEi4e/vLwwGg6XNXFfu7Nmzt5XJaDSKOnXqiE2bNolp06aJZ555ptB1c6mH5cuXi44dOwpHR0fRuHHjQmUezHJt2rRJtG7dWmi1WvHXX38VmWvq1KmiYcOGhdr+9a9/iQ4dOlhe303JjNshLTcSieSeKPitas6cOUW+bX322We4ubmVqlpxRbB48WI6duxoqXZcWtauXWtZw759+wDYunWrpW3//v3ExMTQunVr/vrrLz766COOHTvGpk2b6NGjB+PHj7eM9dRTT7Fjxw6ioqLKc2mSSkpqaiqApRp5dHQ0sbGx9OnTx9LH0dGRbt26sWvXrlKPm5ubi4ODAyrVzY90Z2dnAP7555/b3vv333+TlZVFr169GDVqFCtXrixkNTLz+uuvM2XKFA4fPkynTp0YOHAgiYmJhfpMnTqVGTNmEBUVRbNmzYqMsXv37kJrBejbty8HDhxAp9NZ2jIyMggJCaFmzZoMGDCAw4cP3/k/4RakciORSO6JwMBAy+Hp6YmiKEXabt2WGjNmDIMGDeKDDz4gICAALy8v3nvvPfR6Pa+//jre3t7UrFmTJUuWFJrr6tWrDBs2jGrVquHj48Ojjz5KTEzMbeX76aefGDhw4F2vy9vb27IGPz8/AHx8fAq1jRs3DkVR2LdvH48//jj169cnPDycyZMns2fPHstYPj4+dOrUieXLl9+1HJKqhRCCyZMn88ADD9CkSRMAYmNjAQgICCjUNyAgwHKtNDz44IPExsby8ccfk5eXR3JysmUL6/r167e9d/HixQwfPhy1Wk14eDj16tVjxYoVRfpNmDCBxx57jEaNGrFgwQI8PT1ZvHhxoT7vv/8+vXv3pm7dusV+qYmNjS12rXq9noSEBAAaNmzI0qVLWb9+PcuXL8fJyYnOnTtz9uzZUv9/gFRuJBKJlfnrr7+4du0a27dv55NPPmH69OkMGDCAatWqsXfvXsaOHcvYsWO5fPkyYPI36NGjB25ubmzfvp1//vkHNzc3+vXrR15eXrFzJCcnc/z4cdq0aVPu8iclJbFp0ybGjx+Pq6trkeu3FnFs164dO3bsKHc5JJWLCRMmcPTo0WIVXUVRCr0WQhRpux3h4eF8++23zJ49GxcXFwIDA6lTpw4BAQGo1WpLHzc3N9zc3Ojfvz8AKSkprF27lpEjR1rGGjlyZJEvFwAdO3a0nGs0Gtq0aVPEIlnw7808l5ubG2PHjr3tWgu2d+jQgZEjR9K8eXO6dOnCypUrqV+/Pp9//nmp/z8ANHfVWyKRSMqIt7c3c+fORaVS0aBBAz766COysrIs3zTffPNNZs6cyc6dOxk+fDg//fQTKpWKr7/+2vIA/Oabb/Dy8mLbtm1FzNwAFy9eRAhBUFBQuct/7tw5hBCljiypUaPGHa1MkqrNyy+/zPr169m+fTs1a9a0tAcGBgImi0b16tUt7fHx8UUsHHdixIgRjBgxgri4OFxdXVEUhU8++YTQ0FAANmzYYNn6MW9Z/fjjj+Tk5NC+fXvLOEIIjEYjJ0+epHHjxred81ZFpaCyHxkZaTn38PCwrPdWi1R8fDwajabE7WuVSkXbtm2l5UYikdg34eHhhXwDAgICaNq0qeW1Wq3Gx8eH+Ph4AA4ePMi5c+dwd3e3fBP09vYmJyeH8+fPFztHdnY2wN1HWJSCW79p3glnZ2eysrLKXQ6J/SOEYMKECaxdu5a//vrLomiYCQ0NJTAwkC1btlja8vLyiIiIoFOnTvc0Z0BAAG5ubqxYsQInJyd69+4NQEhICPXq1aNevXrUqFEDMG1JTZkyhcjISMtx5MgRevToUcR6U3C7Va/Xc/Dgwdsq+Oa56tWrh7+/P2Cy/hRcK8Aff/xBmzZt0Gq1xY4jhCAyMrKQ8lcapOVGIpFYlVsfYoqiFNtmNBoBMBqNtG7dmmXLlhUZy+wTcyu+vr6AaXuqYB8PDw+LU2dBUlJSLN8u70RYWBiKohAVFcWgQYPu2D8pKalEOSVVm/Hjx/Pjjz/yyy+/4O7ubrFaeHp64uzsjKIovPrqq3zwwQeEhYURFhbGBx98gIuLCyNGjLCMExsbS2xsLOfOnQPg2LFjuLu7ExwcbHFO/uKLL+jUqRNubm5s2bKF119/nZkzZxbZJjUTGRnJoUOHWLZsWREl5cknn+Ttt99mxowZlrZ58+YRFhZGo0aN+PTTT0lOTubZZ5+9q/+PsWPH8sUXXzB58mReeOEFdu/ezeLFiwtt1b333nt06NCBsLAw0tLSmDt3LpGRkcybN++u5pKWG4lEYte0atWKs2fP4u/vX+jbYL169fD09Cz2nrp16+Lh4cHJkycLtTds2JADBw4U6b9//34aNGhQKnm8vb3p27cv8+bNK5SvxMytCdaOHz9Oy5YtSzW2pGqxYMECUlNT6d69O9WrV7ccBR12p06dyquvvsq4ceNo06YNV69e5Y8//sDd3d3SZ+HChbRs2ZIXXngBgK5du9KyZUvWr19v6bNv3z569+5N06ZN+fLLL1m0aBGvvPJKibItXryYxo0bF2t9GTRoEElJSfz666+WtpkzZ/Lhhx/SvHlzduzYwS+//GL5ElFaQkND2bBhA9u2baNFixb85z//Ye7cuTz22GOWPikpKbz44os0atSIPn36cPXqVbZv3067du3uai6Z50YikZSZknJTvPvuu6J58+aW108//bR49NFHC/UpLvdMSEiI+PTTT4UQQmRmZoqwsDDRvXt3sX37dnHhwgWxbds28corr4jLly+XKNOQIUPElClTCrVFR0cLZ2dnMW7cOBEZGSlOnz4tvvjiC+Ho6ChWrlxZZAxzjo/Dhw8Xar9w4YIIDAwUjRs3FqtXrxZnzpwRJ0+eFJ999lmRPB4hISHiu+++K1FOicSeKelvwN6RlhuJRGLXuLi4sH37doKDgxkyZAiNGjXi2WefJTs7+7ZbSS+++CI//fSTZXsLoHbt2uzYsYPz58/Tp08f2rZty9KlS1m6dClPPPFEqWUKDQ3l0KFD9OjRgylTptCkSRN69+7Nn3/+yYIFCyz9du/eTWpqKo8//vi9LV4ikdwTihD53nESiURShRBC0KFDB1599VWefPJJm8jwxBNP0LJlyxLT5ksk9k5MTAyhoaEcPnzYpqVU7hbpUCyRSKokiqLw5ZdfcvToUZvMn5ubS/PmzZk0aZJN5pdIyoPatWtTGW0g0nIjkUgkEomkSiF9biQSiUQikVQppHIjkUgkEomkSiGVG4lEIpFIJFUKqdxIJBKJRCKpUkjlRiKRSCQSSZVCKjcSiUQikUiqFFK5kUgkEolEUqWQyo1EIpFIJJIqhVRuJBKJRCKRVCn+H501tRY8tKqZAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot cloud core fractions\n",
+    "\n",
+    "fig, ax = plt.subplots(ncols=1)\n",
+    "\n",
+    "# source_type = 1 is simulations\n",
+    "# source_type = 0 is observations\n",
+    "\n",
+    "ds_cogs6[\"low_cloud_fraction_cogs\"].isel(source_type=1).plot(ax=ax, marker=\"1\", label=\"Sim6-75\")\n",
+    "ds_cogs7[\"low_cloud_fraction_cogs\"].isel(source_type=1).plot(ax=ax, marker=\"1\", label=\"Sim7-150\")\n",
+    "ds_cogs8[\"low_cloud_fraction_cogs\"].isel(source_type=1).plot(ax=ax, marker=\"1\", label=\"Sim8-300\")\n",
+    "# Observations\n",
+    "ds_cogs7[\"low_cloud_fraction_cogs\"].isel(source_type=0).plot(ax=ax, marker=\"1\", label=\"COG-OBS\")\n",
+    "\n",
+    "ax.legend()\n",
+    "ax.set_title(\"Comparisons of Simulations for WRF Cloud Fraction\")\n",
+    "ax.set_xlabel(\"Time (UTC)\")\n",
+    "ax.set_ylabel(\"Cloud Fraction\")\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "079f44d4-a9d8-4a30-9c34-089a642497e0",
+   "metadata": {},
+   "source": [
+    "# cloud core fraction profiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a3e26072-0ab0-450e-bdd4-3022bd993076",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Fraction of cloud core grid points\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 3, figsize=(20, 5))\n",
+    "\n",
+    "ds_stat6[\"CSP_A_CC\"].plot(ax=ax[0], y =\"z\") #one plot\n",
+    "ds_stat7[\"CSP_A_CC\"].plot(ax=ax[1], y = \"z\")\n",
+    "ds_stat8[\"CSP_A_CC\"].plot(ax=ax[2], y = \"z\")\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# simulation 6, 7, and 8 from the left to the right."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56c3c08f-b177-4647-a6f0-c584139d1496",
+   "metadata": {},
+   "source": [
+    "# Based on the above figures, We can see that Simulation 6 produces more cloud core fractions, and Simulation 7 is the closest one to the observation. What we learned today. \n",
+    "\n",
+    "\n",
+    "# Based on them, I assume the different large-scale forcing lengths impact turbulence and thus cloud core fraction.\n",
+    "\n",
+    "# I know you all know this, but just to recap, TKE is the turbulent kinematic energy and thus it is the energy for turbulence from the surface to the atmospheric boundary layer. TKE = (U^2 + V^2 + W^2)/2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "249dea83-f748-4826-a9af-ab5ff17e917a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NJkZ+uR3QsOTpjvi6O5GQkcu03w7RKMCDCb0bVXSXhSgXQ4YMQa/X8+2331KvXj1iY2NZu3YtSUlJAAQGBpb5Hnl5efTu3Rt/f39++eUXatWqxYULF25ZMf7KlSuFnv/999888cQTDBkypNDx0aNH8+abb1qem3eKVSQJiIQQlVJYgAcLt0Rzx3vruXr8R6uBjjPW0jTIkwvJWWg0GgZGBFdYP0X1ZTIpaDSFF/QqioKigFZrmwXbKSkpbN68mQ0bNtCtWzcAQkNDad++vaWNRqNh+fLl3HvvvZw9e5a6deuydOlSPvnkE3bv3k14eDiLFi0iNTWVMWPGcOzYMbp06cL333+Pn58fAF9//TVJSUls3boVBwcHy31u5dpg7Pfff6dHjx7Uq1ev0HFXV1erBG7WJFNmQohKKVDnjMFo4trJMJMCBpPCgUup1K7hyr8TujIgovTJ2ISA/AR/eYYSfXzw73Ge/n4PF5IyycozcCEpk6e/38P//Xu8RNe5dmr4Ztzd3XF3d+e3334jNze32OdNnz6dqVOnsnfvXuzt7RkxYgQvv/wyc+bMYdOmTZw+fZrXXnvN0n7FihV06tSJcePGERAQQHh4ODNmzMBoNBb7nrGxsfz555888cQT1722aNEifH19adasGZMmTSI9Pb3Y17UVGSESQlQ6+84n88jXO8nSm4iopePAxVS+fbwdvu5OJGbkMe23Q5xLyqJD3Rr4ujtVdHfFbSBbb6Tpa/+U6tx/j8Re9/zzDaeLff6RNyNxdSzer2N7e3sWLlzI6NGjmTt3Lq1bt6Zbt24MHz6ciIiIG543adIkIiMjARg/fjwjRoxg7dq13HHHHQA88cQTLFy40NL+zJkzrFu3jgcffJC//vqLkydPMm7cOAwGQ6HA6Wa+/fZbPDw8uO+++wodf/DBB6lbty6BgYEcOnSIyZMns3//flavXl2s69qKjBAJISrc1QuoD1xM4eGvd5KRa6BDXW9eG9gUAB83J5oF6+jayI++4epQ+79H4yqsz0JUlCFDhnD58mVWrFhBZGQkGzZsoHXr1oUCmmtdHSwFBAQA0Lx580LHzOUvAEwmE/7+/syfP582bdowfPhwXn31Vb744gtAHeExj1a5u7uzadOm6+759ddf8+CDD+Ls7Fzo+OjRo+nVqxfh4eEMHz6cX375hTVr1rB3795SfT2sRUaIhBDlqqh1F7PXnOBEbAYPdazN2EV7Sc8x4O3qQJvQGjg7XJ+Gv3uYH/P+O8PZhEwOXUolvKauPN+CuA25ONhx5M3IUp175HIa98/dxi/PdKJpsGep7l1Szs7O9O7dm969e/Paa6/x5JNPMn36dB599NEi25vXAUHB/71rj5lMBTs4g4KCrqsi36RJE2JiYsjLy2PQoEF06NDB8lrNmjUL3W/Tpk0cP36cpUuX3vK9tG7dGgcHB06ePEnr1q1v2d5WJCASQtjMjYOfdMb1aEBMWi77ziezeOd5UrP0rDocA4B9/oLUZsE6/D2cGN+zIf4eBVNj9f3caejvzsm4DH7efUECIlFmGo2m2NNW1zIH7c4OdqW+Rlk1bdrUqrmH7rjjDhYvXozJZEKrVSeTTpw4QVBQEI6Ojjg6Ot50x9mCBQto06YNLVq0uOW9Dh8+jF6vL1NhVmuQgEgIYTPmrfNvDGpGYmYeG47H8d32c6Rk6Vl1OPaG54XX1PHVI20t64Ou3VLv7+nM1IFNeeTrnfwWdZnJ/ZsUOZIkxO0mMTGRBx54gMcff5yIiAg8PDzYvXs3M2fO5J577rHafcaMGcMnn3zC+PHjee655zh58iQzZszg+eefv+W5aWlp/Pzzz3z44YfXvXb69GkWLVpE//798fX15ciRI0ycOJFWrVpZ1jNVFAmIhBA2ExbgwffbztLx3bXcKHdiDTdHRravTetQLxQFnvh2N2/fG37LxdJdGvgSpHPmSmoOa47GytZ7UWGKGsW0FXd3dzp06MCsWbM4ffo0er2ekJAQRo8ezZQpU6x2n5CQEP79918mTJhAREQENWvWZPz48fzvf/+75blLlixBURRGjBhx3WuOjo6sXbuWOXPmkJGRQUhICAMGDGD69OmlrlJvLRqlJPv9qrG0tDR0Oh2pqal4epZ8jliI6mrJzvO8suwgAJ7O9oxoX5tuYX442mm5f+42Vj7XxTLldehSKgM/2Vzo2M383z/H+XT9Kbo18uPbx9vfsr0QADk5OURHR1O3bt3rFvyKqulm39Pi/v6WXWZCCJs6FZdhebx4dEcm929C5/q+Vpniur9NLQD+OxnPldTsMl9PCFF9SUAkhLCpveeTi922pFMPdXzdaF+3BooCy/ZeKm0XhRBCAiIhhO3kGowcupRW5GtFBT/+ns5M6N0If8/iT2M8kD9K9NPuCyXK+CuEEFeTgEgIYTNHLqeRZzTh5eLA+J4Nyhz8FKVvs0BcHbScS8xiZ7Ra3FJRFEw3WsUthBBFkIBICGEze8+nANAm1JsJvcPKHPwU5cvN0dTI35H2856LJGTkMnbRXuasPWn1ewkhbl+y7V4IYTP78tcPtartZbN7hAV4kJqtB+C3fZdYezQWjUYj2/CFECUiAZEQwmb25Y8Qta7tbbN7DIgIonVtLzq/tw6DSaGWtwvfPNZeir4KIUpEpsyEEDYRm5bDpZRstBqICPGy6b2CvFwI9XEFYHCrmhIMCSFKTAIiIYRNmKfLGgV44O5k+8Foc0B0NjHL5vcSQtx+JCASQtiEeUF161DbTZddLdTHDYDohMxyuZ8QlZVGo7FqodfqQgIiIYRNWBZU23i6zMx8n0vJMkIkbm9xcXE8/fTT1K5dGycnJwIDA4mMjGTbtm0AXLlyhX79+pXpHt27d0ej0Vz3MWDAgJuet2zZMiIjI/H19UWj0RAVFVWsaw8fPrxQm+TkZEaNGoVOp0On0zFq1ChSUlLK9J5updIERO+++y4ajYYXXnjBckxRFF5//XWCg4NxcXGhe/fuHD58uNB5ubm5PPfcc/j6+uLm5sagQYO4ePFioTYV8YUVojrLM5g4cDEVKL8Rog71fAA4n5RNrsFYLvcUoiIMGTKE/fv38+2333LixAlWrFhB9+7dSUpS83AFBgbi5FS2dXTLli3jypUrlo9Dhw5hZ2fHAw88cNPzMjMzueOOO3jvvfdu2m706NGFrj9v3rxCr48cOZKoqChWrVrFqlWriIqKYtSoUWV6T7ekVAI7d+5U6tSpo0RERCjjx4+3HH/vvfcUDw8P5ddff1UOHjyoDBs2TAkKClLS0tIsbZ555hmlZs2ayurVq5W9e/cqPXr0UFq0aKEYDAZLm759+yrh4eHK1q1bla1btyrh4eHKwIEDS9TH1NRUBVBSU1PL/H6FuN3tv5CshP5vpRLx+j+K0Wgql3uaTCYlfPoqJfR/K5XDl+T/qbix7Oxs5ciRI0p2dnbZLmQ0Korpmn/fJpN63EaSk5MVQNmwYcMN2wDK8uXLFUVRlOjoaAVQli5dqnTp0kVxdnZW2rZtqxw/flzZuXOn0qZNG8XNzU2JjIxU4uLibnjNWbNmKR4eHkpGRkax+mm+7759+657rVu3boV+11/ryJEjCqBs377dcmzbtm0KoBw7dqzIc272PS3u7+8KHyHKyMjgwQcf5Msvv8Tbu+AvSUVRmD17Nq+++ir33Xcf4eHhfPvtt2RlZbF48WIAUlNTWbBgAR9++CG9evWiVatW/PDDDxw8eJA1a9YAcPToUVatWsVXX31Fp06d6NSpE19++SUrV67k+PHjFfKehbjd7T1XkH9Iq9WUyz01Gg2NAz0AOB5bdLkQIW5IUSAvs2Qf696EJQ9C8jn1efI59fm6t0p2nRKUnHF3d8fd3Z3ffvuN3NzcYp83ffp0pk6dyt69e7G3t2fEiBG8/PLLzJkzh02bNnH69Glee+21G56/YMEChg8fjpubW7HveTOLFi3C19eXZs2aMWnSJNLT0y2vbdu2DZ1OR4cOHSzHOnbsiE6nY+vWrVa5f1EqPA/RuHHjGDBgAL169eLtt9+2HI+OjiYmJoY+ffpYjjk5OdGtWze2bt3K008/zZ49e9Dr9YXaBAcHEx4eztatWy1zqjf7woaFhRXZr9zc3EL/2NLS5AesEMVlXlDdKqR8psvMGgd6sutsMsdi0m/dWIir6bNgRimTeR7/8/rnmz8q/vlTLoNj8QINe3t7Fi5cyOjRo5k7dy6tW7emW7duDB8+nIiIiBueN2nSJCIjIwEYP348I0aMYO3atdxxxx0APPHEEyxcuLDIc3fu3MmhQ4dYsGBB8d/TTTz44IPUrVuXwMBADh06xOTJk9m/fz+rV68GICYmBn9//+vO8/f3JyYmxip9KEqFjhAtWbKEvXv38u677173mvlNBwQEFDoeEBBgeS0mJgZHR8dCI0tFtSnNF/bdd9+1rDnS6XSEhISU7M0JUY3tu6COELUO9SrX+4aZR4gkIBK3sSFDhnD58mVWrFhBZGQkGzZsoHXr1jcMaIBCwZL592rz5s0LHYuLiyvy3AULFhAeHk779u0txxYtWmQZrXJ3d2fTpk3F7v/o0aPp1asX4eHhDB8+nF9++YU1a9awd+9eSxuN5vqRZUVRijxuLRU2QnThwgXGjx/Pv//+i7PzjesbXfvmi/MFubZNab6wkydP5sUXX7Q8T0tLk6BIiGKIT8/lQlI2Gg20KKcdZmbmKbNjVyQgEiXk4KqO1JRGzAH4ui88vgoCbzxKc9N7l5CzszO9e/emd+/evPbaazz55JNMnz6dRx99tOhbODhYHpt/9117zGQyXXdeVlYWS5Ys4c033yx0fNCgQYVmXmrWrFni92DWunVrHBwcOHnyJK1btyYwMJDY2Njr2sXHx183SGJNFRYQ7dmzh7i4ONq0aWM5ZjQa+e+///j0008t63tiYmIICgqytImLi7N8QQIDA8nLyyM5ObnQKFFcXBydO3e2tCnNF9bJyanMq/SFqI7M2+0b+rvj6exwi9bW1Sg/IIpJyyE1S4/OtXzvL6owjabY01bXsXcp+Fzaa5RR06ZNbZJ76KeffiI3N5eHHnqo0HEPDw88PDysco/Dhw+j1+stv+s7depEamoqO3futIxK7dixg9TUVMvvdluosCmznj17cvDgQaKioiwfbdu25cEHHyQqKop69eoRGBhomVMEyMvLY+PGjZYvSJs2bXBwcCjUxrw90Nzm6i+sWXl8YYWorvaWQ/2yG/F0dqCml/rL6ViMrPsTt5/ExETuuusufvjhBw4cOEB0dDQ///wzM2fO5J577rH6/RYsWMC9996Lj49PsdonJSURFRXFkSNHADh+/DhRUVGWJSqnT5/mzTffZPfu3Zw9e5a//vqLBx54gFatWlnWMzVp0oS+ffsyevRotm/fzvbt2xk9ejQDBw684bpfa6iwESIPDw/Cw8MLHXNzc8PHx8dy/IUXXmDGjBk0bNiQhg0bMmPGDFxdXRk5ciQAOp2OJ554gokTJ+Lj40ONGjWYNGkSzZs3p1evXkDhL6w5z8FTTz1l8y+sENWNyaSg0RSucK8oCopCue00A3Xa7FJKNsdj0y25iYSwKY9A6PaK+tnG3N3d6dChA7NmzeL06dPo9XpCQkIYPXo0U6ZMseq9Tpw4webNm/n333+Lfc6KFSt47LHHLM/NCRenT5/O66+/jqOjI2vXrmXOnDlkZGQQEhLCgAEDmD59OnZ2dpbzFi1axPPPP2/ZNDVo0CA+/fRTK72zomkUpQT7/Wyse/futGzZktmzZwPqOp833niDefPmkZycTIcOHfjss88KBVI5OTm89NJLLF68mOzsbHr27Mnnn39eaL1PUlISzz//PCtWrAAKvrBeXl7F7ltaWho6nY7U1FQ8PT2t8n6FuJ18tPoEx2PS2Hg8nhyDiZ+e7sg3W87SKMCDCb0blVs/Zq46xucbTjOifW3eva/5rU8Q1U5OTg7R0dHUrVv3pmtYRdVxs+9pcX9/V6qAqDKTgEiIm/vzwBUmLztAWo4Be60GD2d7NBoNb90TzoCIoFtfwEp+j7rE+CVRtK7txbKxd5TbfUXVIQHR7ccaAVGF5yESQtweBkQEkZmn5+VfDmIwKdTzc2feqDb4upfv5oQmQeoPvBOxGTbfpiuEuH1UeKZqIcTtIz2noIbYG4OalXswBFDX1w0HOw0ZuQYuJmeX+/2FEFWTBERCCKu5VAkCEAc7LfX93AEkY7UQotgkIBJCWM2llKyK7gJQkKDxuGy9F0IUkwREQgiruZSijhDdHRGEv0fFJTZtnL+OSEaIhBDFJQGREMJqLqfkADCmewP8PStu947UNBNClJQEREIIq8jKM5CUmQdATW+XCu2LecrsTEImuQbjLVoLIYQEREIIK7mcP13m4WSPzqVia4gFejrj6WyP0aRwKi6jQvsihKgaJCASQliFeYt7RY8OgVq527yOSKbNRHWj0WhsUuj1dicBkRDCKszrh4K9Kj4gMpkUwgLUrffmgEhRFEwmScwvqr64uDiefvppateujZOTE4GBgURGRrJt2zZALXLer1+/Mt9n9uzZhIWF4eLiQkhICBMmTCAnJ+em57z++us0btwYNzc3vL296dWrFzt27CjUJjc3l+eeew5fX1/c3NwYNGgQFy9eLNQmOTmZUaNGodPp0Ol0jBo1ipSUlDK/p5uRgEgIYRXmLfc1K0FANHvtSfaeTwHgaEw6CRm5jF20lzlrT1Zsx4SwgiFDhrB//36+/fZbTpw4wYoVK+jevTtJSUkABAYG4uRUtl2eixYt4pVXXmH69OkcPXqUBQsWsHTpUiZPnnzT8xo1asSnn37KwYMH2bx5M3Xq1KFPnz7Ex8db2rzwwgssX76cJUuWsHnzZjIyMhg4cCBGY8F6v5EjRxIVFcWqVatYtWoVUVFRjBo1qkzv6ZYUUSypqakKoKSmplZ0V4SolMb/uFcJ/d9K5YsNpyq6K8rK/ZeV5tNXKaH/W6k0evUvpeUb/yit3vxXWbn/ckV3TVQC2dnZypEjR5Ts7OwyXcdoMiomk6nQMZPJpBhNxjJd92aSk5MVQNmwYcMN2wDK8uXLFUVRlOjoaAVQli5dqnTp0kVxdnZW2rZtqxw/flzZuXOn0qZNG8XNzU2JjIxU4uLiLNcYN26cctdddxW67osvvqh06dKlRP01/+5cs2aNoiiKkpKSojg4OChLliyxtLl06ZKi1WqVVatWKYqiKEeOHFEAZfv27ZY227ZtUwDl2LFjRd7nZt/T4v7+lhEiIYRVmHMQVYYRogERQfzxXBcAcg0matdw5d8JXcu1yKyoWhRFIUufVaKPj/d+zPj147mYfpEsfRYX0y8yfv14Ptn7SYmuo5Sgxrq7uzvu7u789ttv5ObmFvu86dOnM3XqVPbu3Yu9vT0jRozg5ZdfZs6cOWzatInTp0/z2muvWdp36dKFPXv2sHPnTgDOnDnDX3/9xYABA4p9z7y8PObPn49Op6NFixYA7NmzB71eT58+fSztgoODCQ8PZ+vWrQBs27YNnU5Hhw4dLG06duyITqeztLEFKe4qhLCKyrSGCCDUx40ATydi03IZ1q52hdRVE1VHtiGbDos73LphEdZfWH/d868OfVXs83eM3IGrg2ux2trb27Nw4UJGjx7N3Llzad26Nd26dWP48OFERETc8LxJkyYRGRkJwPjx4xkxYgRr167ljjvuAOCJJ55g4cKFlvbDhw8nPj6eLl26oCgKBoOBMWPG8Morr9yyjytXrmT48OFkZWURFBTE6tWr8fX1BSAmJgZHR0e8vb0LnRMQEEBMTIyljb+//3XX9ff3t7SxBRkhEkKUmcFoIiZNDYhqVYJdZphMoCjU9XEDIDohAxRFPS5EFTdkyBAuX77MihUriIyMZMOGDbRu3bpQQHOtq4OlgIAAAJo3b17oWFxcnOX5hg0beOedd/j888/Zu3cvy5YtY+XKlbz11luAusbIPFrl7u7Opk2bLOf26NGDqKgotm7dSt++fRk6dGihaxdFURQ0Go3l+dWPb9TG2mSESAhRZjFpORhNCo52Wvwqw0jMxvcg7ijNvJ5kO5AYexF+ehf8m0KPmy8KFdWTi70LO0buuHXDIhxLOsYjqx7h277f0rhG41Ldu6ScnZ3p3bs3vXv35rXXXuPJJ59k+vTpPProo0W2d3AoyA1mDiquPWa66g+GadOmMWrUKJ588klADZ4yMzN56qmnePXVVxk0aFChKa2aNWtaHru5udGgQQMaNGhAx44dadiwIQsWLGDy5MkEBgaSl5dHcnJyoVGiuLg4OnfuDKiLwmNjY697D/Hx8ZZgzhYkIBJClJm5yn2QlzNare3+gis2/yawcz5Tcv5ipKMfvhcywMURwu+r6J6JSkqj0RR72upazvbOls+lvUZZNW3a1Kq5h7KystBqC08i2dnZoSgKiqLg4eGBh4dHsa6lKIplvVObNm1wcHBg9erVDB06FFDTBBw6dIiZM2cC0KlTJ1JTU9m5cyft27cHYMeOHaSmplqCJluQgEgIUWaXU9WAKFhXCabLAHQhoNFipxior73CCVMt3Mesx87j+nUJQlQliYmJPPDAAzz++ONERETg4eHB7t27mTlzJvfcc4/V7nP33Xfz0Ucf0apVKzp06MCpU6eYNm0agwYNws7OrshzMjMzeeeddxg0aBBBQUEkJiby+eefc/HiRR544AEAdDodTzzxBBMnTsTHx4caNWowadIkmjdvTq9evQBo0qQJffv2ZfTo0cybNw+Ap556ioEDBxIWFma193gtCYiEEGV2qSKzVJtMoNGoHwCHlsHyZ8CYi6J1QGPSk6vYczbbhfrF+4NWiBLxc/FjTIsx+Ln42fxe7u7udOjQgVmzZnH69Gn0ej0hISGMHj2aKVOmWO0+U6dORaPRMHXqVC5duoSfnx93330377zzzg3PsbOz49ixY3z77bckJCTg4+NDu3bt2LRpE82aNbO0mzVrFvb29gwdOpTs7Gx69uzJwoULCwVaixYt4vnnn7fsRhs0aBCffvqp1d5fUTRKSfb7VWNpaWnodDpSU1Px9PSs6O4IUalMXnaAH3deYHzPhkzo3ah8b75+BsQdhQEfwt7vYJ266JMa9aHXG/DTQwDsuOMrOvR+oHz7JiqlnJwcoqOjqVu3Ls7OzhXdHWEFN/ueFvf3t+wyE0KUWYXWMfNvAue2wKzwgmDI3hnuehW8QizNau+fpe40E0KIIkhAJIQos8sVmZSx2WDo/wEY85PUedeBFw5B+BDwCORQ7YfJUhwJyjgMJ/4p//4JIaoECYiEEGWiKErFZ6k+vLzg8QPfgnv+Wg6PQDK6v863RjUhHevfllxEQogiSUAkhCiTpMw8cvRqkBHkVQHrMdKuwLG/bvhy40AP5hkGkq64QMxBOPZHOXZOCFFVSEAkhCgT8+iQv4cTTvZFb8e1qX0/gGKEwOZFvuzl6oiLzo+vLaNE74Ipv6q2ZK+u1mRP0e3DGt9LCYiEEGViXj9UITXMTEbYs1B93GoUdHsFPAKva9Y40AMnDBg19hB/VN2anxEPPz0MG98v3z6LCmfO0JyVlVXBPRHWYv5eXp19u6QkD5EQokwqdIfZydWQdhFcvKH1I+BQ9JRd4yBPDpyshxF77DDAyhfAzlHNXSTZq6sdOzs7vLy8LPW1XF1dbVojS9iOoihkZWURFxeHl5fXDZNGFocEREKIMjFPmdWqiBGiPd+on1s+eMNgCNQRoi9MHaFGCz5PfBLyMiCwBTz0a8ECbFGtBAaqI4m3KjoqqgYvLy/L97S0JCASQpRJhWWpTjlfsI2+zaM3bdo0SE3GtjHeHaVmSzRX9kG97hIMVWMajYagoCD8/f3R6/UV3R1RBg4ODmUaGTKTgEgIUSYVVsds73eAAnW7gm/Dmzat6+uGo52WzDwj6X6t8LyyDy7uLJ9+ikrNzs7OKr9MRdUni6qFEGVSISNERj3s/V593OaxWza3t9PSMMAdgGiX/JpKl/eBIc9WPRRCVDESEAkhSi0rz0ByljrdUC4BkcmkbpU//jdkxICbH4QNKNbW+caB6rTZTlMTcHAFQ46MEgkhLCQgEkKUmnl0yMPZHk/n0m93LbaN76lb5XfMU583uw+WPVmsrfNNgtRS93uTnaHxQPXgqbW26qkQooqRgEgIUWrlXrLDvwmc3QTnNqvPDyxVC7v6N77lqeYRomMx6dCgp3rw9Dpb9VQIUcVIQCSEKLVyD4iaDS68o8wvDMbuUI/fQuP8EaKziZlkhdypHryyHzITbNBRIURVIwGREKLUyn1BtaIUrlvWb2axt877ujvh6+6EosCJTDcIaA4ocGaDTboqhKhaJCASQpRauY8QxRyEhOOgLd16JfM6omNX0qB+D/WgTJsJIZCASAhRBuY6ZuU2QnTwJ/Vz7U4lPtVkUgjL33p/LCYd6t8FgHJ6nTryJISo1iQgEkKUmMmkoCiKZcos2MsFRVEwmWwYWJhMcPBX9XHEAzcs5Hojs9eeZGd0MgBHr6SR4NOaPI0TmvQrEHfUFj0WQlQhEhAJIUps9tqTPPPDHq6k5gDg6mDH2EV7mbP2pO1uem4LpF8GJx1EDIMek0sUEIUFeHA2MROAPeeS6f3xDnYpTdQXZdpMiGpPAiIhRImFBXiw/UwSCqDVwIgvt7MjOolGAR62u6l5uqzpILB3KvHpAyKC+OeFrmgAg0kh2MuFFj2GqC+elnxEQlR3EhAJIUpsQEQQH9wfAYBJgXp+7vw7oSsDIoJsc0NDLhz5XX0cMbTUlwnycrEsrO7VxB/3Jn3UF85tBX12WXsphKjCJCASQpRKjqGgXMYbg5rh617yUZtiO7kaclLBIxhC7yjTpQoSNGaoRWHdg9QyHue2qg0UpVilQIQQtxcJiIQQpRKbv36oXJiny8LvA23ZKpObA6LjMWlqyQ/7/C38p9dBRrxaGqQYpUCEELcXCYiEEKUSk1ZOAVFOGhxfpT4uw3SZWZeGPgCcS8wi2zsMspLUF/Z+B5+1L3YpECHE7UUCIiFEqZgDoq4NffH3sMF0mbmy/dE/wJgLvmFqdukyTmc1DdZR08sFBdjr3g1Gr1dfyE0Dr9rFLgUihLi9SEAkhCgV85TZsHa18fd0tv4NzJXto35QnzfuDz8/YpXprNah3gDsO58Mfo0gIFx9ocFdxS4FIoS4vUhAJIQoldh0NSAK8LTRYmpLZfv8xc67v7HadFarEC8A9p5PUQ/U7qh+PretzNcWQlRNEhAJIUpMURRi03IBCLDF6BCo01Y9Xy94XoLK9rdy9QiRoigFpUAu7ZXt90JUUxIQCSFKLDlLT17+tnubBUQAcYcLHpegsv2tNA3yxNFeS3KWnrOJWepWfkcPda3S2c1WuYcQomqRgEgIUWIx+euHfNwccbS34Y8RG01hOdpraV5TB8Dec8ngGQTN71dfPPGPTe4phKjcJCASQpSYef2QTRZTm+WkQuwhm12+dW0vAPaeVwu+0ihS/XzyH3V3mxCiWpGASAhRYuYdZoG2WlANcGEnoKhb4UtY2b44Wtc2ryNKUQ/U7Qp2TpByHuKPWfVeQojKTwIiIUSJmXMQBepsOEJk3l1Wp2uJK9sXR6v8gOhYTBqZuQZwdIO6d6ovyrSZENWOBERCiBKLTTNvubdhQHQ+f/2QeUu8lQXqnAnWOWNS4MDFVPVgo77q55P/2uSeQojKSwIiIUSJ2XzLvT4HLu1RH4d2ts09gFb52+8t64ga9lE/n98O2ck2u68QovKRgEgIUWIxljVENgqILu8DYx64+UONera5BwUJGveZAyLvUPBrDIpRLfYqhKg2JCASQpSYzafMzuevHwrtBBqNbe7B1QkaU9QEjVAwSiTriISoViQgEkKUSK7BSGJmHmDDRdXm/EPmDNI20izYE0c7LYmZeZxPylIPWrbfrwaT0ab3F0JUHhIQCSFKJD5dXT/kaKfF29XB+jcwGfO33GPzgMhBq6VpsAdQsI5IqdkWxckTspMK1jEpCphMNu2LEKJiSUAkhCgR83SZv6cTGltMZ8UdgdxUtZRGYHPrX/8qs9eeJCVLD6jTZgkZufz1+UtkKPkjXyf+gYx4+Olh2Pi+TfsihKhY9hXdASFE1RKTqo4Q2WxBtXm6LKQ9aO1sc498YQEeJGSo03+/RV3ij/2X6Y0vkdpMtcHOL2H31+o6pvD7bNoXIUTFkhEiIUSJmJMyBthq/ZB5QbWNp8sABkQE8dPT6n3Ssg2E1HDl5Rdfwf6p9WqD3FTQ1YKxO6DZYJv3RwhRcSQgEkKUSJw5IPKwQUCkKGoOIFB3mJWDpsGeNPR3B+COBr74ujuBfxiE5CeEDGoJ7n7l0hchRMWRgEgIUSIFZTtsUMcs+SykXwGtA9RsY/3r38CdDX0B2HwyvuCgefv9yX+l2KsQ1YAEREKIEjEnZbRJDiJzuY7gVuDgYv3r38AdDdSA6OClNOLS1fdHnTvUzxkxBaNWQojblgREQogSMe8ys+qiapNJHYU5d1VCxnLc6t68po5AT3XE6++DMepB7zoQkL/L7cCScumHEKLiVGhA9MUXXxAREYGnpyeenp506tSJv//+2/K6oii8/vrrBAcH4+LiQvfu3Tl8+HCha+Tm5vLcc8/h6+uLm5sbgwYN4uLFi4XaJCcnM2rUKHQ6HTqdjlGjRpGSklIeb1GI24qiKAWLqq0ZEG18T93afnaz+ty/Wbludff3dObJO9USIX8euKIe9AiEyLfVx4eXgyG3XPoihKgYFRoQ1apVi/fee4/du3eze/du7rrrLu655x5L0DNz5kw++ugjPv30U3bt2kVgYCC9e/cmPT3dco0XXniB5cuXs2TJEjZv3kxGRgYDBw7EaCzIMDty5EiioqJYtWoVq1atIioqilGjRpX7+xWiqkvLMZCjV0dtrJql2r+JGgwlR6vPV/0Pzm0B/8bWu8ct9G8eBMCuc0mWaUHq3AkewZCTKqU8hLjdKZWMt7e38tVXXykmk0kJDAxU3nvvPctrOTk5ik6nU+bOnasoiqKkpKQoDg4OypIlSyxtLl26pGi1WmXVqlWKoijKkSNHFEDZvn27pc22bdsUQDl27Fix+5WamqoASmpqalnfohBV1vGYNCX0fyuViNf/sf7FD/6qKNM91Y+veitKepz173EL932+RQn930rl681nCg7+O03t048jy70/QoiyK+7v70qzhshoNLJkyRIyMzPp1KkT0dHRxMTE0KdPH0sbJycnunXrxtat6jqDPXv2oNfrC7UJDg4mPDzc0mbbtm3odDo6dOhgadOxY0d0Op2ljRCieGxa5T4jruBxv5kVstV9QP4okWXaDCBimPr5xD+QlVTufRJClI8KD4gOHjyIu7s7Tk5OPPPMMyxfvpymTZsSE6MubAwICCjUPiAgwPJaTEwMjo6OeHt737SNv7//dff19/e3tClKbm4uaWlphT6EqO5irirbYXXxx6x/zRLq3zwIjQZ2n0vmSmq2ejCgmbq42qRX1xIJIW5LFR4QhYWFERUVxfbt2xkzZgyPPPIIR44csbx+ba0kRVFuWT/p2jZFtb/Vdd59913LImydTkdISEhx35IQt604W+wwM0s4Yf1rllCgzpl2oTWAa0aJWuSPEh1YWgG9EkKUhwoPiBwdHWnQoAFt27bl3XffpUWLFsyZM4fAwECA60Zx4uLiLKNGgYGB5OXlkZycfNM2sbGx1903Pj7+utGnq02ePJnU1FTLx4ULF8r0PoW4HRQkZbRBQBR/XP3c+hF1h1cFGRCRP2128KqAqNl9gAYu7ICk/IXf5ZgWoDRMJgXlmoSSiqJgMkmSSSGKUuEB0bUURSE3N5e6desSGBjI6tWrLa/l5eWxceNGOnfuDECbNm1wcHAo1ObKlSscOnTI0qZTp06kpqayc+dOS5sdO3aQmppqaVMUJycnSzoA84cQ1Z25sKvVkzJmJUFWgvq477sVGhBFNlP/UNp3PoWLyVkAKHsWoripyRs58BNkxJdrWoBbKSr4mb3mBGN+2EtChvo9S8jIZeyivcxZe7IiuihEpVeh1e6nTJlCv379CAkJIT09nSVLlrBhwwZWrVqFRqPhhRdeYMaMGTRs2JCGDRsyY8YMXF1dGTlyJAA6nY4nnniCiRMn4uPjQ40aNZg0aRLNmzenV69eADRp0oS+ffsyevRo5s2bB8BTTz3FwIEDCQsLq7D3LkRVFGuLHERQMDqkqw2Obta9dgkt3nkBb1cHkrP0/H0whsGta7L8hDOP5GTiCLB1DuyYCxoNhN9XoX01m732JCdj03nr3nB83Z2ITcvhnyMxnInPZN17cdSu4UpCRi5ajYaBEcEV3V0hKqUKDYhiY2MZNWoUV65cQafTERERwapVq+jduzcAL7/8MtnZ2YwdO5bk5GQ6dOjAv//+i4eHh+Uas2bNwt7enqFDh5KdnU3Pnj1ZuHAhdnZ2ljaLFi3i+eeft+xGGzRoEJ9++mn5vlkhbgM2yVINBQuq/RpZ97qlEBbgQa5BnQr7dP0pPt9wCo0mgvq9fueuf/tCXib4BMNjf1eaoq9hAR78sP0cXWeux9nBjqTMvKteVTgVl4GjvZbPH2xNryY3XiogRHWmUa4dZy1CaXZY3W5TTGlpaeh0OlJTU2+79yZEcRiMJhpN/RuTAjtf7Ym/Navdr5oM2z+HjuOg7wzrXbeUjsek03f2fyhAfT83lj7dCV93J/j9Wdj3PfiGwbgd6ihRJfHhv8f5ZN0py3NXRzta1fYiwNOZZXsvWY4/3bUeL/ZphJO9+kej0agGf3Z2BSsoFEVBUUCrrTzvT4jSKu7v72KNEHl5ed1yZ9fVNBoNJ06coF69esU+RwhRucVn5GJSwE6rwdfNytvuzVNmlWCECCAs0IMhbWrxy56LZOYacXfK/1HZ/H41IEo4DmfWQ/27Kraj+X7adaFQMPTpyFb0Cw/CTqvh0KVUlu29xF2N/Vl3LI55/51h7dFYpt3dDDsNTFl2EI1Gw7xRbWjg705Ktp5pvx2iUYAHE3pXju+HEOWh2FNmv/zyCzVq1LhlO0VR6N+/f5k6JYSofMxJGf09nKw/cmDecu9XfqU6bmVY2xB+2XORmLQcvvzvDM/1bAjOXgUN/vuwUgREq4/E8sqyAwAMaV2LX/depI6PG3bXfI9e7N2I+n7ufLXpDKfiM3nk652FXu87ZxMuDnY42Gmwt9PKWiNR7RQrIAoNDaVr1674+PgU66L16tXDwcGhTB0TQlQusWk22mGWmwGp+WktfCvPiISLY8E6xM82nOK+NrWo6REIHcfCjvlwbjOc3w61O5Zrv0wmBY1GHYnfcSaRcYv3YlLggTY1ebhTKL/uLVzc2t/DifE9G+Lv4cSrA5owpE1NRn65naRMPQ52Glwd7cgzmMjWm8jWGzEYNSx5uh1tQm/9B7AQt5NibbuPjo4udjAEcOjQIUlkKMRtxmYLqs2jQ25+4Fp5fgmrgUQDWoZ4kaM3MePPo2o6gL7vQit1pyubPir3fs1ee5Kxi/ay9VQCT367mzyDCX8PJwJ1LgR4OluCH8v78HRmQu9G+Od/3xoHevLd42opo+Vj72D/9EiOvtWPRU+qx/QmhbGL9hKdkFnu702IilTp8hAJISonmyVlNAdEvpUrDYYaSITx7n3N0WrURI1bT+XnSrrjBdBo4eQ/cOVAufYrLMCDbWcSeXDBDtJzDdhrNeiNJhoHel4X/JSEzkUd1Q+t4UpsWi7D523jXGJBUCRJHcXtrsQB0cWLF8nIyLjuuF6v57///rNKp4QQlU9sqo3qmFWyBdXXahLkyUMdagPw+h+H0RtN4FMfpdlgtcHm8h0l6hceSONAT8z7g8Nr6lj9YjdLhu2yemdwODVcHYhNz2Xo3G2cT8ySpI6iWih2QHTlyhXat29PaGgoXl5ePPLII4UCo6SkJHr06GGTTgohKl6MrafMKtkI0dVcHO1xsNNwIjaD77edIyEjl3dS1LxmHP4NEvJ3eJVDOY+vNp9h+5lEHOzURdNv5ydjLImr1xVde6xRgAcv9W2MVgOx6bn0mb2Ru/5vAzuik2gU4HGTqwpRtRU7IHrllVews7Njx44drFq1iiNHjtC9e/dCdcSKkdJICFFF2S4po3mEqPIGRBG1vHDMz9Pz1p9HuPP99fhfWkOOkx+gwOZZ5VLOI+pCCjNXqV+vp7rWL/V1ippau/rYiPa1+ev5O3Gy15KjN5GZa+Dd+5pbbRRKiMqo2AHRmjVrmDNnDm3btqVXr15s3ryZWrVqcdddd5GUlAQUXVVeCFG1metkWXaZ6Zytt57EkAdJZ9THlTggGhARxLpJ3fF2dUBRIFtvJNe7EY6oXxOifoBPWqGc24LJRqkD0nP0PP/jPgwmhQHNg+jbzLYZpxsHefL1o+0AMCowbtEefttXeAebrCsSt5NiB0Spqal4e3tbnjs5OfHLL79Qp04devToQVxcnE06KISoWLPXnuSp7/eQkWsAwF6rsd56kqTToBjB0QM8KvfoQ4CnM9893h5QvwYfXm5Gl8wPyHVSd+AqeZl84D2N2ZebFjpPURRMSumm0czBqKIoTFl+iPNJWQTrnHl7cHiRO8qszbzQ+o4GPhhM8MLS/cxcdQxFUUjIyGXMD3uYveZEoXMkSBJVVbEDonr16nHgQOHdFPb29vz888/Uq1ePgQMHWr1zQoiKFxbgwc5odRTYXqvh3s+2WG89ydULqqvACLN5FPzDoS3w83DgssGdoWnj1dcUE/rMD9id8QGJ2YkAJGYnMnHjRObun1uq+5m32H+z+Sx/7L+MVgO1fVxZuOVsmXaUldT/IhvTq4k/AJ9vOE37d9Zyx3vrWH0klqW7L/D+30dZfSSW+PQcWXwtqqxiZ6ru168f8+fPZ8iQIYUvkB8UDRkyhIsXL97gbCFEVTUgIgiNBsYu2ovBpFDPz515o9qUeCFvkarAguqi1PdzZ2TkcX4/vJeck2GccbBnt6OOze4mYtJ30OeXPtTR1SE2KxYtWiLrRN7ymibFhAZNoaUHjfzdWbj1DH8figHAyd6OE7EZjOpYx1ZvrRDzQutAnTNfPdKOz9af4oN/jhOfkWtpE5uWyxcbzwBncLTT4uZkJ1muRZVU7IDonXfeISsrq+iL2NuzbNkyCYiEuE1l5xktj98Y1Mw6wRBUiQXVV7t6d1ZTfSN+cVxCUsM93MM1AYApjxPJJ3Cyc+K9Lu/Sq3bPW177i/1fcDrlNC+2eRFfF18y9ZmsTfqAGjWdSTvVBYCmQZ7Me9hKwWgxmEehzMb1aECApxOTfj7AiHYheLo6kJKpZ/XRWJIy88gzmghwcqKOj2uh60ixWFEVFDsgsre3v2mVWDs7O0JDQ63SKSFE5WLecm91VS0guipAaG3fGld7V9Ly0tQXTfbUy9bQypiMSaOw3MODXGMuEza+yCCXOkwa9B3ezuo6TKNJDTDttAXlQdzs3dh4YSOrz63GTmOHvdYejeJAcuzdONpryDMovHFPM3zcHDEpJrSaismr2zhQ/T3wYMdQwmvqABh1KZWBn2zG192RC8nZ3P3pZib3a8KTd9YlMTNPisWKKqHE/6Nk8bQQ1Y+5sKtVmYyQmL/WpBLVMCuOtLw0nlnzDDFZMdRw8gPg+aazaRA3iJcTMhiepuZo652VC4rCiuyzDFw+kLn757L02FL6LevH3b/dTUxmDJsubuKxvx/jwz0fkmfKA8CoGMk15pJjzMLRbw33tFEXN6fmJZdpTZKtfTy8Fc2CPTEp8M5fR+ny/np6f7RRchiJKqFEAVF0dDRdunSxVV+EEJWUeYSoR5if9XY1pZwHQw7YOYF3Hetc00ZMismSZy3HkMPz657nRPIJfJx9mNDqfwA0CdLRfsAT9NDPJk1Rp4weT06hVkobNGhJy0vjs6jPeHvH21zJvMKF9Av0/qU3Y9eOZXfcbgAifCN4q/NbADgTCBoTdk7xrEp7Ede6c5i07SF2x+ymvlfpcxCVVVFJHc08XRz449kuPHdXAwAupWSj1Wj454U7JYeRqPSKHRAdOnSIO++8k0cffdSG3RFCVEbmEaKRHUKtt6vJvKDapwFcNXVUGX2x/wsmbpxIXGYc//vvf+yJ3YOdxo67at9FI1+1kHUNN0eGtQth+cuDmWUax5jkVAKMelbkbWV176+p5V4LAHcHd4LdgnHQOFiu7+fix6J+i/mh/w80qqGOliVG30/upYeo6aouRbBzvoKnowfL71lerEXatlJkUsergiStVsPEPmFMHdAEgMTMPH7Yfr6iuitEsRUrINq6dStdu3blkUceYcqUKbbukxCikjGPEAVZs7BrJa9hdrUGXg3YHbObfsv6se7COgBc7F3oENQBPxc/xrQYg5+LOnUW7OXKtH6dGJuSSobeD4esOPx+eYoPO04HYEHkAv4Zsoo9D+7iy95fAvBpz09Zs9+JsYv2ciEpO/+uGmo7daSP7v+4r8F9AMRlxXA543L5vvliKCpI6ljPx/J4ztqTLNkpQZGo3IoVEPXp04dRo0bxzjvv2Lo/QohKRm80kZC/zTrAmjlvEswBkW0yO1tTZJ1IJrWdZFnjU9ezLisHrySyTiR+rn6MbTkWP1c/S3tnB/VH6/vax0lXnNEmnUL5c5L6YlYy/PQwmv9m4ulUsFElLMCDHWeSePb7k+TG90Rj8CQxM5ewQB1Dw4YCYFAMTNgwwZLnqCoY2lYdGZuy/CDrjxWsQZUEjqKyKVZA5ObmxpUrV6RWmRDVUFx6LooCDnYafNwcy35Bk0ktgmoeIfJtVC5FUcsix5DD5/s/tzx/r+t7+Lj43LC9wdWf2Yb76N2jFx8b7sekgCbhmPriTw8XWeKjZW0vQn1c0ed5kJfQm4a+wZYq9ubcREFuQcRmxfLSfy9hMBms/0atyDyNNrF3I5oEemBSYMwPe9h/IYWEjFxJ4CgqnWIFRJs3b2b37t089thjtu6PEKKSiUlVp3D8PZytk0dm43tqEdS4/ADBzc/mRVHL6ptD33Ap4xI1nGsUq32NgNoo3SZzZ6twTjZ4hCfzJuJjMDEmORUfo4lX/D5j9uWmlum2qLNG+s7ayL4LKTjbqz+W/++BFpZ8Q+Z2M7rMwMXOhV0xu5i1Z5blfmUpD2Ir5mm0AJ0LY7rXx95OQ47BxP1zt9L9gw2y80xUOsUKiBo2bMjmzZvZs2cP48aNs3WfhBCVSEyqOl1mtfVD/k3g7CbIS1ef//QwnNsC/pVz6uxi+kUWHFoAwKimo4p1ztVrah5oE8IWu3Z8lvsQY1NSCchK4q4zH/L73nOcvKzh7Kk7mPLzedJzjQR4OvHJyFYFF8ofTTNPy7UNbMudtdSdvt8d+Y6/zvyllgfZ8CJzo76w+nu3lkEta7L2xW64OtqhNypk5Bro1yyQXk39K7prQlgUOzFjcHAw//33HwMGDLBlf4QQlcyV/BGiAGsFRM0Gg9YRlo5Un/s2hGGLwN3v5udVkA92fUCuMZcOgR0YVG8QecY8ywLq4hgQEUSbUC8mfnwejJCn2BNptwsl830e/up5DPk/hp0dNLzaL4wgnUvByRvfg7ij0G+mWustI44+R9bynyvkaOCVTa/gqHXAxWQkMisHroqlMJnUc66uEaco5KeMLuNXpeRCfdz4/on2DPliGwCLdp4n6mIyn4xoTT0/9/zuSUZrUXFK9L/C29ubtWvX2qovQohKKNa8w8yaC6rNo0Og/rKvpMHQ5kubWXdhHfYaeyZ3mIy/m/91C6iLI1DnwhuDmgFwKPxlTGjpa7eb+Q4fYY+B9n4G9jdexKCUHwrn+fFtBKfXwkdN4MMwmHcnkXFn+ev8RbyNRhQUck15OBn0uHrVKXRPZcO7mJaOgox49UBGfIVPTTrZq+kVpg5ogrO9lsOX0xnw8SZ+2XNR1hWJClfsESIzFxeXWzcSQtw2ruTnIAq05pb7lMq7BdtcZFVv0vPezvcAGNF4BHV1dct0XfNC674tB6P1zoXNH3KXXRQ7tOPwyjZid8ERWgxRp9t6NYQjv8G6tyEv86qraMDOCT+tHXNjExgWHICPwUCsvT1jT/3AXTkXmdZ5Oho0vJO6hwbxBxg7qxnoakFWAmjtIfy+Mr0Pa+hYz4dpA5syfcVhsvUmJv28H2cHLa6O9lIYVlSYEgdEFy5cQKPRUKuWupVy586dLF68mKZNm/LUU09ZvYNCiIplHiGqLgGRuchqHc86nEs7Rw3nGlzMuMjc/XMZ23Jsqa9rcA1gtuF+erkGQK/XIKAZyq9P4KNJBwNgyIIVz0P0fxBzEC6p2atxcIX2T8GW2fDUBghuqR4/uhx2vsZH6QoLHLP4z9WFdRc38N9PG3FQFJxNRiKzM8CYC0mn1XPqdANnL3XazDyVVo7TaFePfj3YMZReTQO497MtXEnNIUdvYkirQPo3D7R5P4QoSon/B4wcOZL169cDEBMTQ+/evdm5cydTpkzhzTfftHoHhRAVyzJCZM0ps9QL6uewAeBRuX4BNvBqwM4rO/nyoJo0MceQQ1RcVJnLZVxX8qL5EM73mgdAdo0mgEadStzzzVXBkAsM+Ehdd3Wt/IDGuc/bfBbch/kx8biYTBhQyNaAj8FIgIsfNB9qOUU5uxHT9/fC5x3h0LJyn0a7NoFjgKczXz7c1vL6op3neeXXAxivyk8k+YpEeSlxQHTo0CHat28PwE8//UR4eDhbt25l8eLFLFy40Nr9E0JUIJNJIS5N3WVmkxGiTmMrXUDUJ7QPzXyaWZ438m5klXIZRWVz9ghQp+Gy+30ML5+BgbPVNAQAwa1h/EFoOaLI6/k5eTEmORU/F1+49ws6PbmFr/OXZrkqcMrJkVGe8Iq7lmStlsT75jGxblPmeukg/hj88hjMbl5pdvg931Otf7Z090We+WE3eQaTrCsS5arEAZFer8fJSf0LZ82aNQwaNAiAxo0bc+XKFev2TghRoZKy8sgzqvlt/D2sFBCZTJB6UX2sC7HONa1o3fl1bL2yFTuNugB4cofJN03CWBY18hNd1nBzBNca0PYxePAX9cWBswoWm3sEQrdXCgWPfr6NGdtiDH6++cGMbwO0fdRqArPb/I+2AerIy58xW+kdEsygg7PZ7eRA/Z5vgUd+oVVDNjToBU0G2eT9lUSfpoE82UUNEFcfiaPL++vo/dFGyVckyk2JA6JmzZoxd+5cNm3axOrVq+nbty8Aly9fxsfHNj80hBAVw1zU1dfdCUd7K60xyYwDYx5o7MCzpnWuaSWZ+kxm7JwBwKD65RAkFBHo3LBdj8mF2xV1LJ/OwZ1v+n7DJz0+wVHrQK5Wi0ajZdmgZUQ2fwRGLMlvqYEDS+GnR8CQW3CBcswcfvVU4tSBTfl4eEs0GjVDupO9Hf9O6MqAiKBy6Yuo3kr8E+79999n3rx5dO/enREjRtCiRQsAVqxYYZlKE0LcHswBkVWLupqnyzyDwa7E+zps6tN9nxKXFUeIRwj3NSyH3Vg3CWpKwzKN5uQFQPfa3XnrjrcBSNWns/XK1sIn9H4DNFo49gd8Nwhy0ip8XdGgljV5pa866hWTlsPhy2nl0g8hiv3TKCMjA3d3d7p3705CQgJpaWl4e3tbXn/qqadwdXW1SSeFEBXDXOXeqkVdzQFRJZsuO5x4mMXHFgMwtcNUarrXLFTFvtwUd9SoCOZpNHwL1gTV0dWxPH53x7u0D2yP5cp1u0HXl2DjTDi/HWY1VUeH7J0qdHv+HQ18LY9fXBrF3+PvLLT2SghbKPYIka+vL/369eOLL74gNja2UDAEUKdOHfz9JQ27ELcTm4wQmXeYedW23jVLyaSYUBQFg8nAG1vfwKSY6FunLx2DOxZZxb5clGXU6CbnNvBqQIY+g6lbpmJy9y8IunpMgVHL1RxFuemQlwFOOtDngFFfcIEKKMBbx8eVxMw8XlgaVWjnmRC2UOyA6Pjx4/Tv359ff/2VunXr0q5dO9566y0OHDhgy/4JISpQjC1zEHlV/AjRF/u/YOLGiSw4uICjSUdxc3Ajy5DF3P1zK7prVmMuDPtap9dwtnNmx5Ud/HhpvSVwUhQFU71u8NAy9QQHd0g+A789A3NawN7vICOufKfR8tcVvT8kAhcHLVtPJ/L5+lOW12UrvrCFYgdEoaGhPPfcc6xZs4a4uDhefPFFDh8+TNeuXalbty7jx49n3bp1GI1GW/ZXCFGOYmyRgyglf4SoEkyZNfBqwM6YnXwa9SmgjhgdjD9Y5pxDlYl5pKuVfyvaBqo7zz7a/RFnUs+ohWE3TlQDQGedesKDSyH8fkADaZdgxXMwK7xct+eb1xV1qOdDl4bqCN1Hq0+wMzpJtuILmynVthGdTseIESNYsmQJCQkJzJs3D5PJxGOPPYafnx+LFi2ydj+FEBXAtiNEFT9lFlknkoH1Blqeh3mHWSXnUGV1T/17sNfak2fKY9Rfo7jn93vYHbO7cADo6A73L4DxBwqCVmMueNeFOneWe5/vbVkTR3stCvDwgh30+lC24gvbKPM+Wnt7e/r06cMnn3zCuXPnWLNmDY0aNbJG34QQFSzG2nXMFKVSrSFKy0vj91O/W57bMudQZdC3bl8W91+MVqMlLS8Nk2Li57t/VgPAaxdze9eGYT+ojx3c1OzZ87tD7JGCC5bDuqIBEUGsmdAVJ3stOQb1Xv+8cKdsxRdWV+KA6MCBA0V+HDx4kJMnT9KsWTPatWtni74KIcpReo6ejFwDYMUps6wk0GepjytBDqIfj/5Ihj6DWu61Kror5aaJTxOmd5wOQHpeOu/veh+DyXDzxdyDv1BroKVegK96wdE/ynVdUW0fN94fEgFASraetUfjbH5PUf2UOAlIy5Yt0ZiLAhbBwcGBYcOGMW/ePJydZZukEFWVuairh7M9bk5WyheUck797B4IDhX78yFLn8UPR9URkPsa3sfH+z6u0P6Up8Y+6loge409q8+tZtrmabxz5ztoNerfyIqioKAU/MXsFQp93oaVL4A+E5Y+pOYv0jpCSMfCF7dRsdgG/u6Wx2/8cYR2dWtQ38/9JmcIUTIl/he7fPlyGjZsyPz584mKimLfvn3Mnz+fsLAwFi9ezIIFC1i3bh1Tp061RX+FEOUkJjW/hpktirpWgh1mP5/4mZTcFGp71ObuendXTM6hCjahzQQ0aFgZvZJpm6ehKErhhdZXT6O1HqWuKzKPICkmMObAv1Pg886wawGkx9h81Ciilo5svZEXlkSRZyjfNADi9lbiP/veeecd5syZQ2RkwaLDiIgIatWqxbRp09i5cydubm5MnDiR//u//7NqZ4UQ5edKajZg7QXVlWP9UK4xl28PfwvAE82fINA9kLEtx1Zon8qTeSt+v7r9uJJ5hR+O/sCKMyvYF7+PtNw0tBptwbqiHpMLTtTVhBFLYX43uOs1OLoCrkRB3GH480X4+2V1UbYNkjqat+L3bhrAg19t5+ClVD5afYJX+qmjXYqi5A9M3XgGQ4ibKfEI0cGDBwkNDb3ueGhoKAcPHgTUaTUp9CpE1WaeMrPulvvKkaX691O/E58dT6BbIHfXu7tC+1IRrk46+b/2/+Plti8DcCH9Aml5aXQM7EiIe+HvkaIomJSrRmQa9ISnN8LYHQUBrsmgJnTMTFCnzQpOLvPia/NW/PCaOro0UEfy5m48zdbTCbIVX1hFiQOixo0b895775GXl2c5ptfree+992jcWI3UL126REBAgPV6KYQod1dsmqW6/AMic1ZqvUnP14e+BuCRpo9gp7Ur975UNqOajeLFNi8CoKDw97m/GfbnMIavHM7qc6tJyEoomEa7ln9jGPq9+jiolTqN9tckWDgAUi/apDZa/+ZBOOUXG378m130lK34wgpKPGX22WefMWjQIGrVqkVERAQajYYDBw5gNBpZuXIlAGfOnGHs2Ooz/CzE7cg8QhRgkxxE148y29oX+7/gdMpp2ga05VLGJbycvNgVs4u0vLRqNV12Ix2COgDwZuc3WXZqGVFxURxOPMyLG17EXmuPm71b0dvzrzZwFuz6CqJ+UBM5zmmh1kWzd7bqNNqAiCCa1/Sk96z/yDGYsLfTsmr8nQR5uVjtHqL6KfEIUefOnTl79ixvvvkmERERhIeH8+abbxIdHU3Hjupug1GjRvHSSy9ZvbNCiPJjkxGiCsxS3cCrAbtjdvP+LnWkIseQw764fbdVVmprCKsRxvf9vufXu38lwFUd6TeYDOhNerL12SjuAYXLflw9jabRwL2fweOr1bVEJgPkZUJgS6jdufCNyjiNVtvHjdnDWgCQkWvg43WnUBQp5yFKr8QBUUpKCu7u7jzzzDN89NFHzJo1i6effhoPDw9OnTp16wsIIaqEWGtXus9OgdxU9XEFTJlF1onk5XYvW36BN/RueFtnpS4p80Jr8067RjUa8fFdaiqC+rr6ZBmymLZ1Gk/++yQxmTE33o0GULs9PPKH+lhjD2fWwuxw2Js/tWalabSQGm6Wxz/uPM8P28+V6XqieitxQNS/f3+ys7OvO378+HG6d+9ujT4JISpYrsFIQoa6TjBIZ6VpCPP6IVcfcHS7eVsb+ePMH5bHUztOva2zUpfU1Qutr/XWHW/Rv25/AHbG7KT/sv4M+m1QQdmPopI65uc04r75oKsNxjxY8SzMagaftbNqbbRHO9cBYPqKw2w9lWA5LkVgRUmUOCDy9vZm8ODBGAwGy7GjR4/SvXt3hgwZYtXOCSEqRlyamoPI0V6Lt6uDdS5awUVdz6SeYevlrRVy76rOTmvH+13f59u+3+Js54zepCfPmMeiAYtuPcLmUx+e2wMdxqjPUy9Cbgb0mwnNBpepX+at+GO61SMswB2TAs8s2sP5xCzZeSZKrMQB0a+//kpmZiYjR45EURQOHTpE9+7dGTFiBHPmzLFFH4UQ5Szmqi33N8tMXyIVnJTxx6M/AtA2oG2F3L8qunYarXVAa2b1mAVAjjGHD3d/WHgN0dWunkazd4R+78G9+bvUTHr49QlY8TzkphecU8J1Reat+AE6F8Z0b4CdVkNatoH+H2+i10ey80yUTIkDImdnZ1auXMnJkyd54IEH6NmzJw8//DAfffSRLfonhKgAlqKutshBVAE7zNLz0llxegUAwxoPq5ZZqUujqGm0Gs41ALXsx9rza5l/YH7RJxc1jebfRP0cfr/6ee+38FlHOLe1zOuK7m1Vkz+e7YKDVqPW4FOkCKwomWIFRGlpaYU+NBoNS5cuZefOnQwZMoRp06ZZXhNCVH1Wr3IPFZqU8fdTv5NlyKK+rj6RoZE3XCsjiu/J5k8C8FnUZ6w/v95y/LqdZ0Xp/Bx0+5+6zijtInzTHz5pXeZ1RU2DPZlxX3NALQK7dNeFUl9LVD/FCoi8vLzw9vYu9NGkSRMuXrzI3Llz8fb2trQRQlR9likzm+QgKt+yHSbFxI/H1OmykU1GWm8KsJoyT6MNDRtKuE84AK9seoUzKWdIzE7kxQ0v8kXUF4XOsQRJV0+j9ZgCz+4BF29AAX0WjPqtzOuKmgR5Wh5/uPoE647Flul6ovooVmLG9evX37qREOK2YZMpswpaQ7Tl0hbOp5/Hw8GDgfUGluu9b0fmaTRQM1y/uulVsgxZPPDHA4AagJ5PP8/QsKH4ufqRmJ3IOzveoYFXA/W8q2uj+dSDEUvg60g1Z9HKCfDYX2oyxzLqFx7I34diGL8kihXPdqGub8XsbBRVR7ECom7dutm6H0KISsTqI0R5mZCVqD4u5ymzRccWAXBvw3txdXAt13vf7vrX7U8jr0YMWzmMPFNBOacTySfo+XNPfFx8yMjLwF5rT6/avQqdqygKCgpa+/x/Y47ucGk3rHpFzXhdSuadZw+0rUVcWg57zqfw1He7WT7uDtyd7KUIrLihYk2ZHThwAFMJVv4fPny40LZ8IUTVYvU1ROYt9046cPGyzjVvwly37GzqWbZc2oIGDcMbDb/12hZRYg28G/Blny8BGNxgMO0D2+Ns54yCQkJ2AjnGHDL0GUzZPIXR/47mp+M/EZcZd31ttJ6vqZ93f12QwBFKvfOslrcrrWp742Sv5WRcBhN/iiI+PUe24osbKtYIUatWrYiJicHPr3iLEDt16kRUVBT16tUrU+eEEOXLZFJQFKVQpXur/EVdztNl5rplno7qepKOQR2ZvW92wbSNsCrn/FGe4Y2H09SnKQaTgVVnVzF502TaBLThcMJhcow5bL+yne1XtjNDMwNXB9fCtdGa3gNH/4Czm2DlixDQTB1N/PNF8G9aeKqtmFrV9uan3RfINZj453Asm04m4Oxgx8CIYGt/CcRtoFgBkaIoTJs2DVfX4g035+Xl3bqREKLSmb32JIcupWAwKWg1oNXA2EV7aRTgwYTejUp/4XLeYdbAqwFLji0hJTcFgAMJB3DUOkqZjnJir7Wnnk79g/jldi8T5h3GzpidTN86nSuZVzAqRjLyMjiWeIzuId1xyg92lDaPoVzYgdaYBwv7q0VhNdpSF4YdEBFEh3o1uPezLVxMzibXYOK3cXdIbiJRpGIFRF27duX48ePFvminTp1wcZGqw0JUNWEBHizcEg2Ag52WfnM2odFoyv4XdTnvMIusE8mVzCt8uPtDABp6NWR2j9lSqsNGrk3geC07rR2dgjsxu8dshq0cRofADuyI2cFXh77in7P/8HaXtwn1DOWdxC006Dmeses+AX22GhCN3Q7u/qXum6+7E5+OaMW9n2/FaFL4YsNpZg1rWerridtXsQKiDRs22LgbQojKYEBEEJl5Bl7+5QC5BhPhNXXMG9UGX/cy7vqpgB1mq6JXWR5P7jBZgiEbunrnmeXYTYKkF9u+yN9n/ubbI99yIeMCj656FFcHV3UUr+OrMOhjWPYUZCfDiVXQ+uEy9c/ermC57PJ9l7ivdU3ubCh5qERhJc5ULYS4vaVl6y2P3xjUrOzBEJR7HbMjiUc4nHgYe02x/uYTNnCzYrEAE9tN5I/Bf+Dl5IWCQpY+i6kdp6rTmr5hBQ3//h8kWGcR9N35Waun/naIHL3RKtcUtw8JiIQQhVxMzrbexUwmdZfQ1VNmJdw1VBq/nvgVgPaB7W16H1Ey144ahXqG8nnPzwFQUJi6ZSoH4g8UnBDcSk3Y+OuTYCj92lTzVvwJvRsR4OHEucQsPllXEGQpioLJpJT6+uL2IAGREKIQqwZEG9+DpQ9BRoz63MG1TPWqiiNLn8Wf0X8CMKTREKlbVokUNWpkp7UDoLlvc7IN2Tyz+hlOmLLVnWcDZ6M4e2G6EgXr3y64UCm34tfzc6dtXbUW29wNpzkek05CRq5sxReABERCiGtcTM4C4J6Wwfh7lHG6zL8JnN2sPtbYwTf9ylyv6lZWnV1Fpj6T2h616R3aW+qWVRGT2k4iwDWAdH06o7e8wvk2D5LoHcLEWrWZ66WDLXPgzIYyF4HtHx6Eg50GowLD52+j90cb2RGdJDvPRPEWVQshqgdFUbiUP0L03F0N8C9r6Y5mg0ExwS+Pg2IE34YwbBG42y5A+eXEL4A6OiR1yyo/8zRaiEcI41qO441tb5CUk8SQFUNwtHPETmMg0pg/nbV4GDi4lHkrfqiPK4M+3Uxylp5QH1d+HdPZOmvlRJVW7BGisWPHkpGRYXn+/fffF3qekpJC//79rds7IUS5Sss2kJ6rZpmv6WWlMhfZyQWP+820aTB0LOkYBxMOYq+1557699jsPsJ6rp5GG9xwMD/f/TOOWkdyjDkYTAZ+vGcZkY9vVrfgG3LAzhHG7ihTEdjwmjoe7VwHgNQsPU72MlkiShAQzZs3j6ysLMvzcePGERcXZ3mem5vLP//8Y93eCSHK1YX86TJfd0dcHO2sc9GkaOtcpxjMo0N3hdwl2+yrqIbeDZnZbSYAWYYsPtz9ISavEIicoTbIiIP0K2W+z90t1NxaKdl6Pll3qszXE1VfsQMiRVFu+lwIUfWZF1TX9LZiEdRyCoiyDdn8eUZdTH1/o/vL5Z7CNoLc1O3xdho7Vp9bzSf7PoGabfJfVeCvSWXeqehwVW6ib7ZEczo+4yatRXVQoeOE7777Lu3atcPDwwN/f3/uvffe6zJiK4rC66+/TnBwMC4uLnTv3p3Dhw8XapObm8tzzz2Hr68vbm5uDBo0iIsXLxZqk5yczKhRo9DpdOh0OkaNGkVKSoqt36IQVcqlFDUgquVtxUzzyfkBUfOhat0qKzMXcv3n7D9k6DOo5V6L9oHtpZDrbeDpFk8D8NXBr/jt8n/qQXtnlAs7MEUtLtO1zVvx72jgg96o8NbKI2XtrqjiKjQg2rhxI+PGjWP79u2sXr0ag8FAnz59yMzMtLSZOXMmH330EZ9++im7du0iMDCQ3r17k56ebmnzwgsvsHz5cpYsWcLmzZvJyMhg4MCBGI0FibdGjhxJVFQUq1atYtWqVURFRTFq1Khyfb9CVHbmHWZWC4gUBZLPqo+7v2KTgOiL/V8wceNEfjz2IwD96vbjpf9eKlxJXVQp5oXW9ze8n9b+rQF4/fDX7OrwGIntn2Sivy9zt71deH1aCZm34r99b3Mc7DRsOB7PumOx1noLogoq0S6z1157zVLgNS8vj3feeQedTgdQaH1Rca1atarQ82+++QZ/f3/27NlD165dURSF2bNn8+qrr3LffeqOgm+//ZaAgAAWL17M008/TWpqKgsWLOD777+nV69eAPzwww+EhISwZs0aIiMjOXr0KKtWrWL79u106NABgC+//JJOnTpx/PhxwsLCEEIUTJnVstaUWUasmlhPo7VZluoGXg1YfHQxaXlpaNCw9PhS7DR2Usi1Cru6FMiIxiM4mHAQvUnPUwkbcbFzwd7Flcj4eFg/A/p/oJ6kKOqHtmR/59f1deOxznWYvymat1YepUsDPxzttSiKkn852alYXRT7X465wOu+ffvYt28fnTt35syZM5bnx48fp2vXrmXqTGpqKgA1aqiJs6Kjo4mJiaFPnz6WNk5OTnTr1o2tW7cCsGfPHvR6faE2wcHBhIeHW9ps27YNnU5nCYYAOnbsiE6ns7QRQlwdEFlphMi8fkhXC+wdrXPNa0TWiWRAvQGAmu24nq4ey+9ZLgHRbaJv3b6sHLwSV3tXDCYD2YZsZrs1IzIrG3Z+CVcOlDk3kVarwdFOS3RCJt9siZZkjdVUsUeIbF3gVVEUXnzxRbp06UJ4eDgAMTFqdtuAgIBCbQMCAjh37pyljaOjI97e3te1MZ8fExODv//11ZL9/f0tba6Vm5tLbm6u5XlaWlop35kQVYd5yizEagHRGfWzd13rXK8IRpORdefXWZ5LIdfbT7B7MJ/c9QlP/PsEBsXAS4bzLHRyISQ3G77LT6+g0ZQ6N1Hzml442GnIM8LMf47z+YbT2Gk1DIwItuK7EJVdsUeIihMQjR079pZtbuTZZ5/lwIED/Pjjj9e9dm1yNUVRbplw7do2RbW/2XXeffddywJsnU5HSEj5VekWoiKkZutJz7FyDiLzguoa9axzvSLsit1FbFYsrvZW3BknKh13R3cAarnXIk6fzuN1w7hi7wDZSeDqgzJmO6ampcs9NSAiiPWTuuPmaIfRpKiL9F+4kwH5xWBF9VDsgOiee+5h7969N3x93LhxLFq0qFSdeO6551ixYgXr16+nVq1aluOBgeoCzGtHceLi4iyjRoGBgeTl5ZGcnHzTNrGx1y+Wi4+Pv270yWzy5MmkpqZaPi5cuFCq9yZEVXHRljmIathuhOiP038A0Dm4s83uISqPaZ2moXPUEZObxKO16xBnZ0eiYmDi7nfLtJDe39OZD+6PACAtx8C6Y3G3OEPcboodED355JP069ePEydOXPfas88+y3fffcfKlStLdHNFUXj22WdZtmwZ69ato27dwj8069atS2BgIKtXr7Ycy8vLY+PGjXTurP7wa9OmDQ4ODoXaXLlyhUOHDlnadOrUidTUVHbu3Glps2PHDlJTUy1truXk5ISnp2ehDyFuZzbJQWQeIbLRlFmWPovV59T/+4PqD5JCrrcx886zhl4Nea71c2jRclnJZVDNIAa557H78lbqe9Uv0z1q+7hZHr+18qglDYWoHoq9hujDDz8kOTmZ3r17s3XrVmrWrAnA888/z8KFC/nzzz+58847S3TzcePGsXjxYn7//Xc8PDwsI0E6nQ4XFxc0Gg0vvPACM2bMoGHDhjRs2JAZM2bg6urKyJEjLW2feOIJJk6ciI+PDzVq1GDSpEk0b97csuusSZMm9O3bl9GjRzNv3jwAnnrqKQYOHCg7zITIZ1lQ7WXFHEQ2HiFae34t2YZsarnXontId3rU7mGT+4iKd/XOs2Fhwwj3CWfUXw+RaWfAwaTwTZpCi9q9rXKvxoHuHIvJ4H+/HOD7J9pLTbxqokT7E7/66ivatGlDr169SEhIYMKECSxYsIA//viDbt26lfjmX3zxBampqXTv3p2goCDLx9KlSy1tXn75ZV544QXGjh1L27ZtuXTpEv/++y8eHgWViWfNmsW9997L0KFDueOOO3B1deWPP/7Azq5g2H/RokU0b96cPn360KdPHyIiIvj+++9L3GchbldWz0GUnaKu7wDwrmOda15j5Rl1VHpQ/UHyS6uaaebbjFkdpwOg12p43imDo3vKlnvKnKzxrXvCcXbQsvlUAj/sOG+N7ooqQKOUsAZHXl4eAwYMYP/+/WRmZrJixQp69uxpq/5VGmlpaeh0OlJTU2X6TNyWRn+3m9VHYnnrnmaM6lSn7Be8vA/mdwc3f3jJ+tuX47Li6P1Lb0yKib8G/0WIp2x8qG6OJB5h2Mph1LH34KwhHVcFPu3zFe2C1RQriqKgoKDVlDwH8YJNZ3jrz6O4OtqxanxXavu4Sm6iKqq4v7+LPWX28ccfWx53796dTZs2ERkZyeHDhwuV0nj++edL2WUhREWyelJGG0+X/XXmL0yKiVb+rSQYquZev3MGU1aP47K9lqdXP8UHPT6ipV9L3tnxDg28Glim2koiJVuPt6sDyVl6Jv2yn4+Ht+SNP47QKMCDCb0b3fRck0lBoym8u1mCqcqv2AHRrFmzCj0PCgriwIEDHDhwwHJMo9FIQCREFXXJ2lNmNl5QveLMCgAG1htok+uLys+80Lq2bzOeD+jCtIQt6DUmJqyfgJuDGw5aByJD+9z6QkVoHOiJKX/+ZGd0El3eX4+7s32xchPNXnuSk7HpvHlPMzycHcjINTB1+UEaBXjwYp+CdasSJFUuxQ6IoqPLp2K1EKL8pWbrSTPnILJ2lmorjhCZFBMaNJxIPsHJ5JM4aB3oE9oHk2Iq1bSIqNquXmg9wCmAdhdjGRLsR4qdHTmGbL52CKbJ2X1Qt2+Jrz0gIogO9WowfP52TsVlYDAp2AEBnoUzrhuNahFhO7uCf3+1vFz4atMZOsxYi0lRc0aiwK6zyWg1GtrWqUFDfzdeL+aIkygfxQ6I7rrrLpYtW4aXl5cNuyOEqAiX8qfLfNwccXUsUYnDGzMXdbXiCNEX+7/gdMppvJy8ADX30Jvb3yz1tIi4jQS3wl9jz8ex8TwcHIhBMfJ2xlEW+g7HoZSX9HV3Yvawlgz8ZDMh3i5cSM7mgbnbmRQZxtju9UnMzGP4vG2AhiVPdyQmNYcv/zvDHwcuW0aXQC2xBpCYmcfs/HIgGg042mloG1q4yoKMGlWcEpXuyMvLs2VfhBAVxOo7zKCgbIcVR4gaeDVgybElpOaqdQ93x+7GUesodcsENBsMgS1wmt8JAFcFDjjaMTPzBK9a4fIfDW3Ju38fZe/5FD745zjfbIkmNVuvZrYG2r29hqt3KAV7OTO4VS0+W3+KD4e2QKuBw5dS+XXvJZKz9CgK5BoU3vrzKH8cuMzIDqF0rFuDd/8+JqNGFUTGmIUQ1l9Qrc+BtMvqYyuW7YisE8mENhNQ8n/1NPBqIIVcRQGfetB0EADPZRoBWHJ8iSWbOagjMCbFVOJLuzja8euYzrzQsyEACRl56I0KJkUdATIHQ96uDnz1SFu2/O8u+oWr1RbCAjwY3KoWUwc24/sn1B1wU/o1JjxY3fEUdSGVl385QNcPNrDmaCygYLpqiElRCj8XtlGisfH09HScnZ1v2ka2pAtR9Vi9yn3KOUABRw9wtV6hVUVR+PXEr5bnUzpMkUKuohC/Jvcx5q9/iExPZ7V/R/ZmXeSNrW/QyLsRvi6+Jd55Zs5N5O/hpCYL7t2IUB9XJvy0n3E9GtC8pifero4kZuQydvE+vn+iA+E1dbe8bucGvjzVrT5Hr6TxxLe7uJySA4DeqDBn7Sl+3nOREe1qM6hlMO/JqFG5KFFA1KjRjb8Z5kKpRqOxzJ0SQpQvq0+ZWRZU18lfUWod++L2cSDhAPYaewyKwWrXFbcPP/cgxqaoU6rDYy9yQGdPrimXUX+PwlHriFajLdGIor+n83WBSMMANTFwv/BAS/Bz6FLq9edeFUzdSJMgT+aPasvATzbzwf3N+S3qMltOJXI5JYcPV5/gozUncHHQ0r95YKHzZK2R9ZUoIPrll1+oUaOGrfoihKggVp8ys9GW+4WHFwLQtVZX1l1YZ9Vri9uERyDcORH2fEe/hPM0bvEqQ84uIduQjZ2DHb8O+pUAt6KLeltbUcHUzYKkJkE6Hmhbm4tJWTy0YAdnE7NQFMjKM/Ha74fxcXOicwNfEjJymfbbIRk1srISBUR33HEH/v7+tuqLEKKC2G6EyHoBUXRqNBsubADg4WYPE1YjTAq5iut5BELP18AjCP6aRN2dX/POoPd4ees0MvQZzD8wn6kdp5ap1EtRQU1xRoOg6CDpWrVquPLpyNYM/GQzD3cKZcnOCyRn6Rn51Q5CfVxJzdKj1WqKlRNJFJ8sqhaimrNNDiLzDjPrLaj+7sh3KCh0D+lOm4A2jG05Fj9XCYjEDbR+GHQhkH6F0ItRlsM/nfiJH4/9WKZLm4Maf0/nmx4r9vVuEkwNbRvChpe606upOhhxLjGLrDwj797XnAERQaV/E+I6xQ6IQkNDCxVLvZUtW7aQm5tbqk4JIcqPbXIQWXfKLDE7kRWn1MzUjzZ71CrXFLc5eyd16gwgahEAI5uMBOC9ne+x9dJWS9PS7jyzllsFU8FeLnz1cDveuTccgDyjiae/38ObfxwmR1+wbld2o5VNsQOi6OhofHyKv5ujX79+XLp0qVSdEkKUH/N0mdVGh0xGSD6nPrbSlNmS40vIM+UR4RtBa//WVrmmqAZSL4KDK34ZCYzxaskTde6lEc4oKLy44UXOpJwhMTuRiRsnMnf/3IrubSFFjRq1CPECoE9TdQ3U11vO0nfOfxy4mEJCRi5jF+1lzlrrF1KuLqz05+D1FEWiVCGqgkspVt5yn3YJTHrQOoBnzVJfxlymI8eYw5JjSwB4uOnDKChokJ01ohgCw0Gjxc9oYuz+v+HYJh53dmCqlyuZhkyG/zm8VDvPysPN1ho937Mh9f3dmLvxDGcTsrjn0y24O9vjYKeVdUVlYLOASAhRNdisyr13KGiLP81+LXOZjmY+zUjJTSHQNZBVZ1dxOvW0lOkQxdNsMNTuBHNagiEb7BwZ8MQWGhvSGPrHULIN2dg72rP87uX4uVX+9WhXjxr9r28T7m8dwuDPt5CWYyDPYOKfF+4k2MtK/4+rIVlULUQ1Z/0dZtZZUN3AqwG7Y3bz8b6PAUjNS2VP7B7qe9Uvaw9FdeIRCJEz1McZcZB2ifpe9Xn7jrcBSM9LZ+mJpRXYweK7dq1RfX935o9qC0CuwcRPuy9WZPeqPAmIhKjmrJ6l2koLqiPrRDKlwxTLYteGXg2lTIconZrmdWcK/DkRTCZCdaGWl+cdmMf68+srpm9l5O5cMNHz+frTnIrLqMDeVG02C4jKkuNBCGF7JpOCoiiFpsysskvFijmINl3aZHn8asdXpUyHKBt7V7i0G/Z9ZznUr04/AKZsnsLZ1LMV1LGyaxPqRZ7RxKvLD8oa3lIqdkB0/vz5En2R5RsiROU2e+1JRn+3m9RsPQDO9lrr7FKx0ghRjiGHtefXlq0vQoA6bdbtFegyXn2+5nX8FHvGtBjDC21eoJVfKzL0Gbyw/gWy9OoUckVvxS8u87qiqQOa4uJgx47oJH6WqbNSKXZAVLduXeLj44t94fT0dOrVs15SNiGEdYUFeLDzbBIAjnZa7vlsCzuik2iUX6epxEwm9SPprPq8Rl21DLipdL9UNlzcQKY+U7JRi7LzCIQek+HOSeDfDLKT8dvyMWNbjiXYPZjmvuE4ah05nXqaaVumkZCVUCm34hfFvK6oVW1vXujVAIB3/jpKQoaaB1ByExVfsQMiGfER4vYyICKIV/s3AdREb/X83Pl3QtfSZ7/d+B4sGQF56YAGHFzhp4dh4/ulutxfZ/4CoHdob8a0GCOBkSg7O3sIbqU+3vc9nN8BGfFEnNqEU/7vuH/P/cuA5QPYHbO7yi3gT88x4uFsT2q2nrdWHpHcRCUk2+6FqMYupeRYHr8xqBm+7jevw3RT/k1gR/5f1PbOMK+rWuk+/L4SXyo1N9WyfuiBRg/QwLtB6fslxNUa9oJDP4MhFxY9AFo7IjUa2vZ5gxGnvudK5hVyjbn8OOBH6nlVrVmOJkGelgxdv0ddZt3ROBzsJTdRcZUoIPrqq69wd3e/aZvnn3++TB0SQpSf4zFp1rtYs8GQGA3r3lBzvgRFwLBF4F7ykZ3V51ZjMBlo5N1IgiFhXc0Gg19TmNsJclPBsxY8tQEfdz/+r2YLHvzrQYyKkQWHFvBOl3cqurclMiAiiA71atB39n8kZOSh1Wr4d0LXsv2hU42UKCCaO3fuTeuZaTQaCYiEqEJOxFp5i27S6YLH/WaWKhgC+PPMnwAMqDfAGr0SojD/MOg2Gda/DelXIPUCuPthry34lbji9AoG1R9Eh6AOFdjRkvN1d+Lte8N55oe9pGbrSc3WS0BUTCXadr97926io6Nv+HHmzBlb9VMIYWXZeUbOJmYCMPrOukVW2i6xK/vLfImYzBj2xO4BoH/d/mW+nhBFathL/awYYfnToM+2vNQ7tDcAb21/i1xj1StSfnXW+QWboyuwJ1VLsQMiySskxO3lVFwGiqJWuX91QNMbVtouNn0OxB8tc7/+jv4bBYU2AW0IdAss8/WEKFr+7zQXH0g4oW7Fd/FjTIsxPNfqOfxc/DiXdo75B+ZXbDfL6Nc9F0nKzKvoblQJsstMiGrqWP76obDAUm6zv1bcYTAZwMUbuv1P3epcCn9Fq7vLZLpM2JQ5N1G//F2QO+bid+UgY1uOpa6uLv9r/z8Avj70NadT1KngqpWbqAFhge7kGkws2n6uortUJRQ7IJo+ffotF1QLIaqO4zHpAKXPO3Qt83RZcCvoMaVUAdHplNMcSzqGvdaePqF9rNMvIYpizk0U8QC0eVw99ts4yE4G4GTySXxdfDGYDLyx7Q3is+KrWG6iMMZ2VzckfLvtHDl6YwX3qvIrdkD03HPPkZSUVOjY4cOHeeyxxxg6dCiLFy+2eueEELZzPFYNiBpba4TIHBAFtSjxqSbFhKIolsXUXWp2wdPRs0r8NS5uAy46cHCD9Mvw10uQEU/Do/+gz1VHUffF7WPg8oFVLjdR/+ZBBHo6k5CRy4r9lyu6O5VesQOicePG8dFHH1mex8XFceedd7Jr1y5yc3N59NFH+f77723SSSGE9VlGiCpBQPTF/i94ccOL/HH6DwC61exWZf4aF7eBoBZq0kaAgz/DnBZEXjzM781fIMhNTVSqN+n5ceCPVaq4sIOdlkfvqAPAgk3RsvTlFoodEG3fvp1BgwZZnn/33XfUqFGDqKgofv/9d2bMmMFnn31mk04KIawrOTOPuHR194xVpsyMeog9rD4Oalni0xt4NWD7le3EZMVgp7Fj9r7ZVe6vcVGFNRsMz+4BXS31uT4L7p2LT8tRfND1A/WQSW8ZwaxKhrULwcVBy/HYdDafSgCknMeNFDsgiomJoW7dgmKN69atY/Dgwdjbq1H1oEGDOHlS0oMLURWYp8tCarjg7mSFhPXxx8CYB0468K5T4tMj60TSJqANAEbFSF3Puiy/Z3mV+mtcVHHufjDsh/wnCvzxPKTH4mDnYGmy4OACErITKqZ/pfTNlrP4e6g7SL/cFC3lPG6i2AGRp6cnKSkpluc7d+6kY8eOlucajYbc3KqXr0GI6sg8XRZm7QXVQRFquY4Sis2MZfOlzZbnkztMxsfFxzp9E6LY8v/tetVREzb+9LA6+gnU19Uny5DF51GfV1z3SiEswIOUbPU9/Hcinrv+b0PZijjfxoodELVv356PP/4Yk8nEL7/8Qnp6OnfddZfl9RMnThASEmKTTgohrOuYOSCqBOuHAH45+QtGxUiTGk2s0x8hyiLyHXD0hAvb8ds+nzEtxvB8K7UKw68nf+VU8qkK7mDxDYgIYu3Ebni7qiNddvnlPEpdxPk2VuyA6K233uL333/HxcWFYcOG8fLLL+Pt7W15fcmSJXTr1s0mnRRCWNeJWHNA5GmdC1oCopYlPlVv1PPz8Z8BZIpMVCxzbqJabaGRmvbBb+8PjDW5c5dPc3ppdZgUEx/u+bCCO1oyvu5OvHVvOADJWXpi03JucUb1VOzFAy1btuTo0aNs3bqVwMBAOnQoXN9l+PDhNG3a1OodFEJYl6IonLDmlJnJCDEH1celGCFafW41iTmJ+Lv4069uP3KNufi5lK4GmhBlYs5NBNBkIBz/S11gveJ5cHBhvLMr633d2XxpM1svb6VzcGcURUFBQaspUSWsclfHx83yeNbqE3z1SLsK7E3lVKLvoJ+fH/fcc891wRDAgAEDCi26FkJUTpdSsknPNeBgp6Gur9utT7iVxFPqLw0HN/Ap+a6wJceXAHB/2P0EuwcztuVY/FwlIBIVrNlgGL8fXH0ABfRZ/OnlQ03XAAD+b/f/EZ8Zz8QNLzI36ouK7WsJaIA1R+OIupBS0V2pdIodEK1bt46mTZuSlpZ23Wupqak0a9aMTZs2WbVzQgjrM0+X1fN1x9HeCn/VmqfLApuD1q5Epx5NPMq+uH3Ya+y5v+H9Ze+LENbk7g8P/ao+tnehQeJ50tIvokHNZD1geX92n19P/UsHC5+nKGCqXElF1XIeDenXXF079NHqExXco8qn2D8NZ8+ezejRo/H0vH7NgU6n4+mnny6UuFEIUTlVpgXV5tGh3qG9ZVRIVFL5O88e+IZIt1B+u3iFYL26ayvPkM3CyzFEXjkBGfFqu4x4dXfaxvcrqL9FU8t5NOKVvo2x12r470Q8u84m3frEaqTYAdH+/fvp27fvDV/v06cPe/bssUqnhBC2c6KCAyJzmY7U3FT+OqMWch0WNkzKdIjKzSMIxmzDp/tUPohTAwmjRsN3Hi5waQ981Bg+bQ+ftILoTeAXVvj8SjJqVNvHlQfaqjvCP/z3eAX3pnIp9qLq2NhYHBwcbvi6vb098fHxVumUEMJ2jll1QbWpxAHRF/u/4HTKaRp4NSDHmEN9XX1+OPoDO2J2MLbl2LL3SQhrMu888wgErRa6TsTO3RMOfgzAMnc37kvPICI3DxKuCjB+HweHl0HoHaALgb3fq/9H7ppS0EZR1A9t+S7IHtejPr/svsD2M0lsPZVA5wa+6uJwBbTakucRu10U+7tQs2ZNDh48eMPXDxw4QFCQ5DUQojLTG02cjs8ArDRClBwNuWlg53T9X8Q30MCrAbtjdjNv/zwALmdeZk/sHinTISon884zj8CCY/nTu119W6JoNLzTuDPGLhPU13T5+fj0WXD0D1j1Cix9EE6ugi2z4Pv74L8P4MqBCpta+2n3RYK9XAD4cPUJ4tNzJHs1JQiI+vfvz2uvvUZOzvX5C7Kzs5k+fToDBw60aueEENZ1NiETvVHBzdGOWt4uZb+geXQooBnY3XgE+WqRdSJ5qd1LmFCnDxp6NZQyHaJK8XPyYkxyKs/XfwAPBw+OpJ3hV/P62mE/wP/Owv1fg2d+bTQ7R/WzMQ9Or4V1b8O8O+H43wWjRGblMLUWFuBBan726j3nkun+gWSvhhIERFOnTiUpKYlGjRoxc+ZMfv/9d1asWMH7779PWFgYSUlJvPrqq7bsqxCijI5dVeFeU4oSG9cp5YLq5aeWWx6/2vFVKdMhqhQ/38aMbTGGsJrtLdO8H5/6meT8qS/F2QtTs8EwfJF6whOr1SBp2CLwCi24kEkP/70Pn7aDPQshPaZcRo0GRASxZmI3gnRqjbMsvZFPR7Sq9tmrix0QBQQEsHXrVsLDw5k8eTKDBw/m3nvvZcqUKYSHh7NlyxYCAgJs2VchRBmZa5g1Lut0mcmk/iVrDoiCWxb7L9sjiUfYFbMLO0q2RV+ISuOqabTk3GTcHdxJ1Wcwp2k3Eh2cmLhxInP3zy18jou3muxx6Hfq8/u/gXrd1ceJJ+GP8TArHM5uBv/GNn8Lvu5OzH1ILaisKPDyrwdIzsyz+X0rsxKVuQ4NDeWvv/4iOTmZU6dOoSgKDRs2LFTCQwhReZmr3Jd5aHzjexB3FC7vU5971Vb/svVvWpDp9wa+P/I9AB2DO7Ll8pay9UOICtbIuxGa/K35v2ae5p/Vj+Ogdbj1FHCNevDw75BwCn4YDCnn1REjrWPBVJuZjRZf2+UvoA70dOZicjbP/biPhY+1w96ucmfdtpVSvWtvb2/atWtH+/btJRgSogowmRQURSmoch/ogaIomEzKLc68Af8mcHYT5KQAGvjlCTi35ZZ/2cZmxrIqehUADzV5iDEtxkiZDlGlRdaJ5I/Bf1DDuQYAepOeJQOXqAHR1TvUbsS3AQxV/0jAsxZkxsCC3rDpIzUIKoe8Rq8ObIKLg5bNpxKY+U/BTrky/YyogqpnGChENTN77Ume+n4P55OyAPBzdyrbrpJmg6G7eSRIAd+GMHaHevwmfjz2IwbFQJuANnSp1UXKdIjbgo+LDx90/QCAXGMuS46pCUeL3KF2syDpvvlQsw2gwNo3YHY4fNa+WH9slIY5e3WHOjXoHuYPwPz/zrBi/2USMnKr3c4zCYiEqAbCAjzYcSYRAEd7LUPnbSv7rpILOwoe95sJ7jcPbLL0Wfx04icAHm76cOnvK0Ql5O7obnn87ZFv2R+/v+iGRQVJZo5u8ORa6Jb/x0bqRTAZYPT6W/6xURrm7NX+ns4MjAjG2UENCV5Yso9uM9dXu51nEhAJUQ0MiAji8TvU4st5BhP1/Nz5d0LX0u8qMRnh9LoSnfL76d9Jz0untkdtuod0L919hajkutbqikkxMXXzVHIM16epKdLVo0YaDfR4BQbOUl/LTYPlT0PO9XVErWlARBAbX+qBp7M9JgUy84w0C/KkdW2vQu2KmkYzZ5+/rl0Vyz4vAZEQ1cTBS6mWx28Maoavu1PpL3ZpD2Qnw1V/Fd+M0WTkhyM/ADCq6Si0GvnRI24vfi5+jGkxhhfbvIivsy9n087y+f7PLa/fNEAoatQouLX62cEdzm+DbwdB1lW1x2yQryjA05lvH28PgL0WNp1K4M6Z65n/3xlMJoWEjFye+X4Xs1YXLvnxedTnTNjwIonZiSiKQmJ2YtE77Sq5Eu0yE0JUTQajiZ3RVizkeHK1+rnOnWoOohssGjUpJjRo2HhxI+fTz+Pp6Mnd9e7GpJgkKBK3FT9XP0tOoraBbVl1dhULDy2kV+1e1HSvyTs73qGBV4OSl6e5exaseA6u7INv+sOjK9Vg6M8Xi7Wrs6Qc8neYzRneig/+Oc7ZxCxm/HWULzacIkdvAu9VROUlcceFSexO+I/jSWfYdHEzRnJYd34tWo0WO40dTnZO9K7dp9C1FUVBQam0//clIBKiGjhyJY30XAOujnZk5RnLfsFT+QFRk4HQ6qEbNjPXLYvJjAHg7vp3M23rtNL9YhCiiugV2ot159eRZ8rjqdVPYa+1R4u2ZNnYzdNodbvCXVPh32kQfxQ+bgkaO9DaQfh9NnsPoT5urJ3Ync/Wn2L2mhMkZ6mZre2zA8jQbePJtQ+h0VwzTQYYFSNGxUieKY/XtrwNGoW+dfqSlJNU+qCwnEhAJEQ1sD1/QXWb2l60Dq2Bv0cZpssy4gvyDzXoddOmDbwasOjoItLz0tGg4Y/Tf2CnsZMyHeK2FlknkjDvMAb/PphMfSZ+Ln78fPfPJcvIbp5GA+j8HNTqAN8OgNx00NqrW/Ub97d63807z/w9nLDTani+Z0OaBXvyxLe76VC3BmcS2pGeeQgH3QEATHle5CbdiZLrj0abg0vIIkxZ9dG6nCbHlMrL/73MG9vewGAyYK+xp2tw10L3M5rUP9DstAWJWitqJEkCIiGqgW2n1YCoW5g/T95Zr2wXO71W/RzY/Ob5VVB/Mfx64le2XdmGgkI9XT1m95gtpTrEba+Org4T207k/V3vE58dz+HEw3St1fXWJ95I7fZqnbTFQ9WdZ0sfgv4fQLsnCtpYIYGjeefZ1QI81RIf0wY25WLeNl767wCgARQaMo40+0DOpmSiOF0CIDu2P4rRBY/A/8B9J5n6TAByyWXatmn8ePxH2ga2RavR8vup39FqtDwa/ig1nGsQ4hHC90e+r5CRJAmIhLjNGYwmdp1NBqBjPSsEIub1Qw1637Lp0cSjbLuyDQ0aFBQmd5gswZCoNloHtLY8nrZlGr8O+hVfF9/SX9A9vzxWvR5wZr26jujiLrj7Y8hJtdm6IrPEnFje3PkmAD2CB7D+8kqe6FKPu5u0J9dg5Jvdm/nsOIT6uBJ9yZu0C/eidYnArc58jOlNsXM5D/YZHEk6wpGkI4Wu/eHuDwFwsXfB2c65QkaRK+fKJiGE1Ry+nEZGrgFPZ3uaBHmW7WImY8EIUcNbB0RfHvwSgM7Bnct2XyGqsBCPEJJykpi6Zap1tqL3mg7h96uP9/8IHzWGz9rZNIHjc3fV48tj75Cel05z3+bcVXNQoTZO9nY0869FbnxP3hzQiY0vdeeZbvVw1KqjSzkJPck4ORXXuEn4O9cEwNupBr60o7ZTOzwd1Z9NQW5BLL9nuQREQgjr25a/fqhDPR9L7aJSu7RX3W7vpINa7W/a9FTyKVafU0eT7mtou8WfQlRW5q340ztNx1HryJZLW1h8dLHl9RLn6rHkKwqC+xfA4Pmg0UJWIhgN8NQGqyZwNOcX8vd0xitoK/sT9uJs58yMLjMI8w2hpftQGvgEW9p7O/mSl9AbbydfQn3ceKVfE2YOiQCgob87ChCb6Ev0UfXnQfLph0k9P4KxTd/hyz7qH0/v3vluhY0iS0AkxG3OvKDaKtNl5t1l9buD3c1n3OcfnA9A79DetPJvJXXLRLVj3orfIagD7YPUPyA+3P0hx5OOly5Xz7X5iloMU4MigLx0+OtlNTCyki/2f8HEjRPZenkrn+77FIB6unr8Ff0XTfxr8f2QaTTxLyhEe/WCbDNvZ19y43sytW8Hfht3B90aFfwMyNQb8XaxJyUrjxx9we7XiqqhJmuIhLiN6Y0mduXnH+pYr0bZL1jM9UNnU8/yz9l/AHgq4qlCOVqEqI7urX8vO67sQG/S88iqR0q3Fb8ovg3Vz3aOcOJvNWfRPZ+VaWG1WQOvBiw9tpT159djUAw4aB24knmF+l71i2xf1ILsq0eNwmvq/r+9Ow+P+dofOP6emWSyZyL7ZgmJSGKrUEspbRFaRZdbrlK63aqr6nJ1ob3o7xZtFd0sVVQXqi1aVde+L7GEEPu+FJGEyL7OnN8fk0wykhDEkuTzep55zJzv+Z45J4nkM2dl7ksPMjM6i8+PgF6n4URSJqN+24+Ty0W0gXD4Yipf/G839X1cSpR1p0kPkRBV2P7zKWTkGjE42BLme5vzhzKSyr3cfmbcTEzKRIfADjRwr/g5DUJUNlFBUfzy5C/YaG3IyMvARmNTsXNlOo4FtLB3Hqx637zaDG5rR+uoOlFM7zidfGXudQqpEXLTdS6t16hBwVyjKc+25+lmAWg1kJnlRE7iY4z89cw9O0NNAiIhqrDok+beoZZB7mhvd/7Q8TWAAp9G4FryDLTC+QZ/pf3Fnyf/BODVRq9WuvOMhLhT6rnV450H3wHgcvZltl/cfoM7yqFwXlHDpyG0qzlt25ewebJ5z7CfX4ANH91y8VsvbrU8H9169E3P7yl+gGyhwl6jQFcfJj3XlOh3H6Oeuz+5SZ0I8Qi4vXMWb4MEREJUYYUTqlvXu435QyaT+VPm8dXm1yEdS/3UWTjf4KvYrzAqI819mvPtwW8r3XlGQtxJjTwbWZ6P3TaWM6lnbq/A4vOKGv8NbB3N6WvGmne1vo2VZ0op/jjxx+3VrxTX9hp5u9ozpVdTAMY/3ej2zlm8DRIQCVFF5RlN7DpdOH/oNgKiDRPMnzILJ1QHtCj1U2ewWzDbL25n6cmlABy+cphd8bvKnG8gRHUW5h5GZn4m/97wb3KMORVTaMRT8OY+8yo0gLxM6D3/lleeHbpyiJMpJ7HV2lZM/QqU1mt0P5CASIgqat9fKWTmGqnhaEvo7YzHe4fByQ3m5fZaG1gyuNRPnZ1rd6aeoSj4CXYLvmf7iQhxvypcij+m9Rjc9G4cvnKYiTsnWq7f9FL8azl7Qa8fCwozwR9vQm7GLRVV+OHmIf+HqsUqUQmIhKiiCpfbtwzyuL35QxFPQb1Hzc9N+eZVLYO2l/jU+euxX9mTuAc7nbm7W3alFqKkwhWX4Z7htAkwb1j605GfWHVm1a0txS9N4blgDu7mA2H/HF40ybqc8k35LDu5DIBn6z/LoKaD8HK8cwFRaZOv7zYJiISoYkwmhVKq2P5D7re3r0fmFTiyrOh114/Nn0KLuZh+0bL1fu/Q3rf2PkJUM4/WehR7nXnY6K0Nb9H9t+4VO8z82H8AjXk3693fFaWXY+XZ9ovbuZx9GTc7N0vgdifdD8NoEhAJUcVMWXOMgT/EWPYfCvNzZdCPu/lszbFbK3DvfDDmgEdwqZeVUoyNHktGXgZNvZrSpU6XW626ENVKVJ0olj61FCcbJ/JVPiZlYmH3hbc/zFy48iy0KwS1M6f9+W+4uLfcK8/+OGmeTN2lTpcKn0N0v7qnAdHGjRt58skn8ff3R6PR8Ntvv1ldV0oxZswY/P39cXBwoEOHDhw4cMAqT05ODm+88Qaenp44OTnRvXt3/vrrL6s8ycnJ9OvXD4PBgMFgoF+/fly9evUOt06IeyPUx4WtJy6TnW9Cr9My8IeYW9/XQynYNdv8PLx7qVmWnFjClvNb0Gv1jH1oLD5OPtVivoEQFcHHyYfx7cYDkJ6XzsozK2+/0OIrzyJfBJ0tmHJhdtdynXmWmZfJ2rNrAXiy3pO3X59K4p4GRBkZGTRp0oQvv/yy1Osff/wxkyZN4ssvv2Tnzp34+vrSqVMn0tLSLHmGDh3K4sWL+emnn9i8eTPp6el069YNo7FoG/A+ffoQGxvL8uXLWb58ObGxsfTr1++Ot0+Ie+GJxn48GuoNQK7RRF0v51vf1+P0Jrh8HPTO0PT5gnOUfC17DiVmJvLRTvMnzdebvE4d1zqWORJ3cr6BEFWJj5OP5fnkmMmcSjlVcYU3fBoGbjXvZJ2XYT6g+bVN1115tubsGrLys6jtWttqm4Cq7p4e3dG1a1e6du1a6jWlFFOmTGHUqFE8/bT5ILi5c+fi4+PDvHnzeO2110hJSWHWrFl8//33dOxo3jn3hx9+oGbNmqxevZqoqCgOHTrE8uXLiY6OpmXLlgDMnDmT1q1bc+TIEUJDQ+9OY4W4SzJz81l96JLl9djuEbe+r0dh71DjXuYhs0feBWBa7FccTz5OVn4Wablp1K9Rn/2X9zN973Q5okOIW9TIsxFxSXG8t+U95naZi422gv5Ee9WHHlNh0SuQkwrrx0H3L0FTtNjCpExo0KDRaCyryx4PehyFQsNtbupaSdy3c4hOnTpFfHw8nTt3tqTZ2dnRvn17tm4175wZExNDXl6eVR5/f38aNmxoybNt2zYMBoMlGAJo1aoVBoPBkqc0OTk5pKamWj2EqAyWxcWTkWvEz3CbkxPTE+BQwaZszV+0uhTsFszWC1vZcmELGjRczLjI7ku7Zc8hIW5B4VL8US1H4WzjzL7EfXx74FvL9dteig9FZ56hgT0/wOZJVpcLN1Y9euUo0RejAdiXuK9abax63wZE8fHxAPj4+Fil+/j4WK7Fx8ej1+upUaPGdfN4e3uXKN/b29uSpzTjx4+3zDkyGAzUrFnzttojxN3y865zAHQK97lBzhvY/Z15mX3gg+Br3W0e5h6GKljGq1DUM9STPYeEuEWFw8wRnhG08GsBwJd7vuTIlSMVtxS/0ENvmv9d8wHsX2RJDjYEsyt+F33/1xeTMqHT6Dh4+WC1+pBz3wZEhTQa6646pVSJtGtdm6e0/Dcq59133yUlJcXyOHfu3E3WXIi773RSBjtOXUGrgb81D7z1fT1MRoiZa37e/CWrS3mmPN7Z9A5ZxizC3MMA2XNIiIryRNAT2GptMSojLy5/kZ6/96yYpfiFK89avQ4Bzc1pi16DczsgPZGoHT+wyKOD5YOOn5NftfuQc98GRL6+vgAlenESEhIsvUa+vr7k5uaSnJx83TyXLl3iWomJiSV6n4qzs7PD1dXV6iHE/e7XGPMKy4fre9EowO3m9/WwnFu2BlLOgr0bhPew2rNkWuw04pLicNG7MLjp4ApugRDVW1RQFAu6LUCn0ZGWl4a9jX3FBCbFV561HmSeZG3KhW+fgC+awZktJBh8yDZmAzC2zdhq9yHnvg2IgoKC8PX1ZdWqVZa03NxcNmzYQJs25k2iIiMjsbW1tcpz8eJF9u/fb8nTunVrUlJS2LFjhyXP9u3bSUlJseQRoiowmpQlIHqu+S0O8RaeWxY9zfw64ilY/Jplz5Kd8Tv5Ju4bwHzytaej523XWwhhLaRGiGVxQlJmEmm5aTe44yY1fAYG7wJbJzDmmidaN3+R30xXLVmc9c4V+56VwD0NiNLT04mNjSU2NhYwT6SOjY3l7NmzaDQahg4dyrhx41i8eDH79+9nwIABODo60qdPHwAMBgMvv/wyw4cPZ82aNezZs4e+ffvSqFEjy6qzsLAwunTpwquvvkp0dDTR0dG8+uqrdOvWTVaYiSpl07FE4lOzcXO05bGwkvPmysU7zLzU/qR5DxIOLMJ0ZgvKK5SUnBTe3fQuCkWPej3oVLuTZTKo7DkkRMV6yP8hAPJVPv8X/X+WoawKU6M29FlgeZmzaSJ/HvnZOk85drSuSu7psvtdu3bxyCOPWF4PGzYMgP79+/Ptt9/y1ltvkZWVxaBBg0hOTqZly5asXLkSF5eiDeYmT56MjY0Nzz33HFlZWTz22GN8++236HQ6S54ff/yRIUOGWFajde/evcy9j4SorH7ZZe4d6tk0ADsb3Q1ylyHiKTiwGA7+bn7t1YBpjTpxPHETeZfWcinzEgFOAaTmplqW2MsyeyEqXuEcV71Wz474Hfx+4nd6Bves2DexK/hb+vDbrNv9FamY8DSaeKZWFF4mrbm32Dvcst1GVadRFR52Vk2pqakYDAZSUlJkPpG47yRn5NJy3BpyjSaWDWlHuP8t/oxeiIWv2xe9/scGVuRe4v0t75OVn4UGDU62TthqbRnValS1mnApxN2UmJnIL0d/IV/lM3PfTAx2Bpb0XIK7vXvFvUnh//d/bGDglpFsyTzHq1dTGJKcAjZ25iG1bpOuu4ljZVDev9/37RwiIcSNFR7k+nvseXKNJiL8XQnzc7m1g1yVglXvm58Hd7QkO9g4kJ1vnmipUAS7BVe71SdC3G2FS/Ffb/I6IW4hpOSkMHHnRMv1CtmbqGDlWbxOy9bMgh5mo4P5Wn6OuQfJcM18xCo8jCYBkRCV2JQ1xxj0427mbT8LwOON/G79INdjq+DURvPqkxavAHA8/S/e2vgWCsWjNR8FZIm9EHeTrdbWcnzGHyf/YNuFbRW3N1HByrMll6JRKCJ9IqnVu2BekaMnXD0D3zwGK/8DxvxyHwxbWUlAJEQlFurjwpbjSRxNSEergZkbT97aQa4mI6z6j/l5y9fA/wGS2w5l8N7PyMjLoLlPc15u+HLFN0AIcUNtAtpgpzPvJ/bmujfp8XuPitmbCHNP02/HfwPgqeBiQ2PPzjFvygqw9TOYGAxfRN7wYNjKTAIiISqxJxr7EeZnHhM3KajnfYsHucb+CImHMNm7odoOI8/Rg3/ln+F8ZjwBTgFMbD8RG909XYMhRLUVVSeKRd0XYau1JSs/C71WX2HD1jGXYjiXdg5HG0c61e5UdMHeFV5ZBU9MBo0OspIhJwXqtIfAltaFVJFhNAmIhKjENh9LYvupK9gU/E++qYNcCzdhzM2AtR8CMC28A8O2/x/vbXmPmEsxONo4Usu1FguOLJAl9kLcQ7VcazEs0rwSOzErkdOppyuk3MLeoS5BXXC0dSza0drFvDkyLV6Cvxctz+fgIpjSEFaNhtzMKjWMJgGREJWUyaT4aPlhALo2vMkeISjahHHDx5AeD66B1E1LYtPZdSw7tQwwL/09fOUw9dzqWSZ5ejlKQCTEvdDMp5nl+Xub3yMzL/OWyjEpE0opMvIyWHlmJQA96vUwT9IuvqN1IeeC//M9poF7PVBG2DIFPq4LnzWGM5urxDCaBERCVFLL9l8k7nwKTnodvVrcws7UhZswbpkCQF72VdamnyQHoyVLiFuIrCgT4j7jae/JX+l/MSlm0o0zl6LwZPtFRxeRlZ9FTeeafH/w+xtP0vYJhzdizMNoOj3kZ0FeJijg8knITinKWwmH0SQgEqISyjOamLjiCACvPlyX+j4uN32Qq6nBkyhv8+GsmRoNg/18WW5vg06j481m5hOxZUWZEPePwmHrt1u+DcCCIwvYen7rTZcT7BbMzvidTIwxL+NPzEok5lJM2ZO0iw+jaTTmYbT+S83XnH0h6wqs/QAmRcCaDyA+rlIOo0lAJEQltGDnOU5fzsTDSc8r7eri7Wp/0we5Tlv2MsOzjnLazoFXfb3Zqs1Fq9HStU5X2vjLOX9C3G8Kh6271OlC79DeAPxn639IzU0Fyr83UVSdKFr7tbbkDalxg57g0obRbAo+fPX+EVq8ap54nZsGmz6F6W3hyP/Mq1eL7/18n/caSUAkRCVRuAljZm6+ZZ+hNx4NxtH2Fo7puLiP4CNr2GFvRw9/b/bZ26EBnGyd6FCrQ4XWWwhR8ZxsnXCwceBS5iU+3vHxTe1NtOzkMv53+n9oMB8P8l6r9269J1hrA09MhGGHwCOkKN2UB5s+gS+amQ+LvnQYFvSD9ROs77+PgiRZRytEJTFlzTGOXUqjjocjiWk5BLg5sO3kZZIz8/hXp/rlLyg3Exa+QkBuDlqdHlPBnKFQl9pM7zoXDwcPEjMTZUWZEPexMI8wbDTmP+G/n/idFadX4GDjcMP5fufSzvFB9AcAPB3yNAuPLby1Cly7Gs3FB575xnwUyLNzYPf35kOir5yE5e8U3KSF+H3Q6Fnwqm9eofbnsPvmvDTpIRKikgj1cWHbyctM33ASgKuZuew8nVy+TRgLl9gDrPoPKzPP8qK/D8kYqeUcAMDY5m9bPiXKijIh7m9RdaL446k/8HUyByTZxmxa+7fm0VqPlnlPnimPtze+TUZeBg94P8AzIc/cegVKG0Yr5F4XXlgMb+yBGnWKXTCZd7/+qgV8EgKfNTHvju8Van3/Peo1koBIiEriicZ+dAr3oXBEvoGva7k3YTStH49a0A8V+xPfHF3AcB8vsjUa2tr58UFb8x5EOMnkaSEqEw8HDz575DMANGhYdmoZg1YNIj033ZLHaDJiNJl7gb/a8xVxSXE42zozru04fJ1872xPsEdd+Ntc8/Pe8+Gx0eAVbn6dkQB5GZB9FZb+y7yvUXwcpCfcswnZEhAJUUkcvZTGot3nLa/H9ij/JozT8uN5M2UXb20ZyWfubgDUzDcR4V4fBxuHO1FdIcRd9FaLt7DR2BAdH02fZX2Iz4jnQvoFui3uRpeFXfgs5jNm758NQF1DXZacWFLxPcHXDqMV5+oP7YbBP7fBcz+Y07wjzP9mXzVv/zG9LUxpdM+OB5E5REJUAkop/m/pQYwmRasgd6JPXbmp+40uPmyws8FkbwtK4YCWdMcahNR/UnagFqIKaObTjMEPDOaz3Z9xKuUUnX/tjKJohdc3+78BQK/Vcy7tXIWcg1ZC4TDajbgV7JvWc6o5UIr71bxBbHayeYit/9KizSDvIukhEqISWH8kkU3HkrDVaXi5XdB18xbuQgtwOesyb68ZwsyjCzBpNNgqBRoNoW7BLO75O1F1omS+kBCVWPEPNC83epmfnvgJe529JRhytHGknqEeBr0BgAYeDe7uZqvX6zUCcPaG1oPghd/Mr3tMvSfBEEgPkRD3vTyjif/78yAALz0URJNAt+tuwjgtdirHrx6nmXckM/ZOIyUvDZSikVHDv5sMpv+Br3i3wQuy4aIQVUDhB5pC4Z7hzI6aTZ9lffim0zc86PcgGo2Gg5cP0mtpL0a1HHV3/++X1mt0vSBJewvbiFQQCYiEuM/9EH2Gk4kZeDjp+eejwbja21qW2ZuUCQ0aNBrzfiLZ+dkkHF/F2syTrD67BgCdUjgqRX/3SOydy/iUJoSoMnQFQYWLnYvld8N9pbxDa3eZBERC3GPXBjUAefn5aDSQlm1iymrzJoxDH6uHg431L7epsVM5cfUEbzZ7k1VnVvHdwe+4mnMVMAdCRo2Ghjm5fJaah8eDPTnoWPDJ0FF6h4QQ95EbDa3dBRIQCXGPTds7jRNXT1i6si9nXabHwufJMebT2O4l0lQ6Nf00fHbsU6ad1DGzyxTS8tLYfWk3C44sICUnhdVnV1vK06Khe1Y+TyUn0t/fl5EaTzxe/x2cvfAq3HDRs/KfTC2EKF1pCyXu+8UT90GvkUap4geNiLKkpqZiMBhISUnB1dX1XldHVCErTq9g7LaxZOdn42DjQFpumtXqkJtRy8Gb2adP4JOZzEH3QHoZtCxo+X+EN+hZsZUWQohKorx/v6WHSIi76NrhscNXDrPk+BLSctMAyMvNK8qsQKFFozGB0uJo64BJGckx5qBQGPQGngruyQPezdBqtbyx9g0+OXUYn6x0CGgOLV+EPZXrtGkhhLhXJCAS4i4qHB7rG96XeYfmseL0Csu1RwIfYd1f6xjfbjzbD7jz/ZYkXF0TUQFTGNdyJk+GPQhgWS3ydeevCd/3G5ycw0GPgn09jDng7AtB7fFyCeT15BS87NzufkOFEKKSkYBIiLso2C2Y7w9+z6ozqyxptlpbhkUOo5lPM9b9tY70VHd+2JoM6Oj9YC3mny+7PLzDYOsXcCIPAvxAqzefMu3XCC/PBgxq8jrIfCEhhLghCYiEuItcbF3Izs+2vA6tEcqMTjPwcPDg4GXzXkOTVh9FKS/+/mAtGgVkXT8gykiCvEy8dFpzb5BXGPT6qWhjs/twaasQQtyPZKdqIe6S2IRYhq4filEZae3XGoAPHvqAGnbuKKXwcvCijrYniVftqO3uwMiuDQj28Kep83MEe/hbyrGsFjmxEZb925zWuA+Drqbg1fXTe7bLqxBCVGYSEAlxhxQ/QuPIlSMMWjOIrPws2vi3YXDTwZZ8U9YcY9CPu1mxL4O4A63QGF3xd3Pgm82nCPMO5Ptn3ifMOxBMJlDKvDOtzhuvZe+YC2j5OrR45V40UQghqgwZMhPiDimcQD0gYgBD1g4hLTcNg95AuHs4Nrqi/3qhPi7M3Xqa5fvjAdDbaDlyKZ2+repYF7hhAiQcgpDO8MebgDIfhGjvCtyHu9EKIUQlIgGREHdIsFsw8w/PZ82ZNZgwodVo0Wg0NPBoYLVJmnddV1ztbUjJMi+5D/dz5esXmuPpfM1ZZd5hED0NDi0xv9bZQU6aOf0+2OVVCCEqMwmIhChgMik0GqyO0DAaTQDodEWjy0oplAKt9vq9Mu0D2zPLaRaHcg4BEOYexlePfWU5WHFQ00Gk5+Tz91nRnEvOwtvFjoS0HD7o0bBkMATm5fT5uUWv/ZpA73kygVoIISqABERCFJiy5hjHLqXxUts6GE2QmJbDh38eRAH/fKQeoT6ueLna8fH/DlPfx4VhnUMt914bJCml+M+W/3DoyiGcbZ1Jz0vnP63/Y3XKdG6+idd/iCHufAruTno+6BHBwB92l165i3th3nNgzIaareBcNDz+iUygFkKICiIBUTVU2mGiSikUCq3mzs2zL60Hpry9LXeDr6sd09Yf538Fc3mK+8/vB61ebz1xmYzcfHq3qEUNJz3v/7af+j4ullPoZ+ybwf9O/w8bjQ3Dmw9n7LaxQNHXQCkY8eteNh1LwsFWy6z+zbEt1guFyUTBFwuSjsH3T0NOKtRsDZ3GwOyoO/Z1EEKI6kgCompo2t5pHE8+Tvd63WkX2I6UnBQ+3P4hwW7BDGo6qELeo7TgZ8rqoxy9lM5/nzIPCSWl5/De4rgb9rbcCcXrZzIpftp5jnHLDpFnLDpDzMFWRz1vJ+xstBy9lE5adr7lWmp2PrM2n2b25tPobbQ46DU80cgPgOWnl/NV7FcAjGw5knCP8KKvwZpjHL2Uhoejnt9jL6DTQMMAA+uPJPJ8y1q8+VgI3i52RROo2w2Hn56HzCSwN0CtlmBjf8e+LkIIUV1JQFQNBbsFM2f/HFafXY2fkx9Z+Vlo0BBVp+J6HaasOcbBCylE+LuSnW8iM8fI8v0XuZKZx7ojCdTzcubslUzyjCbiU3N4oU0dS5B0bW/LnVA0PBbEJ8uPsOP0FQB8XOwY9UQ4Q37awy8DW9MwwADA/vMpdPtiMz/9oxUaYOfpK8zcdIqUrDxy8k3ovdbw7YnFuLr3473N7wFQy6UWlzIS6FCzA683eR1Pe0/y8i+z6uAljCZz4GVvq+NEYgYD2gTh7Wpf1GbvMNg+Aw4vBWUCjRY0OvBvKhOohRDiDpCAqBqqX6M++UZzb8fFjIvUdq3N3C5zrea33K46Ho5MXXec1YcSSlwzmhQHL6ZaXu89d5VW49bQwM+Vv5Iz0Wo0dGvsX+K+ihTq48Lszaeshscc9Tre6xZOkKdTmfc529nQMMBAy7oedAj1ptsXmwms4UB8uheHkzfzzzXbQKPQaWw4l3KZn7bk0ivEhQjHv/HirMMcuGBut1YDJgUN/FyZ0S/SehK1UpB1FfJzzMEQgG8TeP4XmUAthBB3iARE1dAnOz/BiNEy2TchM4FsY/aNbyyn3HwTv8VeIN+k0Gk1GE0KH1c7ukT44mRnQ3xqNot2n8fPYE98ajZKQb5Jsf98CjZaDa+0rUObeu5WZVb0MFp6Th7pOUVDYI0CXJnz4oN4Otux/3zKTZX15d8fYO42F1Ykr0NncxGA3Ew/ss71J9XoTMtxazAVjcTRMcybAW3q0HfWDsZ2j8DT0dYcBGk0kJ4AS96Ao8vNmf0egIt74MkpMoFaCCHuINmpuprZ+NdGNp3fhI3Whg/afABAVn4W7256l3xT/g3uvjGTSfHvX/ay8WgiDrY6JjzTCIBZ/VswtkdD3urSgJceCgJg5gvNOTA2iiWDH+Klh+oA5sBoxsYTRP53FW//uo8LV7NISs9h0A8xfLb6yG3XD2D25lO8vTAOgC4NzcNO459ubOml8XaxK5rLU6C0tEI6rQY7/4XoHC6CyTy/x5T4FBqTs/m5KipjxdCH+aZ/C9wc9UUFbJgAP78AsfNgauuCYEgDdR+FbpMrpM1CCCGuT3qIqpE8Yx6f7PwEgL5hfQlwCQDA3saePQl7mBU3i9eavHbT5RZOUAYY+8cBluw1Txb+6vkH8Ha5/gRgR70NjQPd0Go0zN5ymuGd6uO09WN8c0/z/q4X+SXmHD66ND6wmUVtm+bAeMu9ymQ+GkOr05W7fl+uPc6nq44C8ErbOvRoGmDZIbqQ1Vye66UVBEnr4n/mz5N/otPoGBD+BrMOf8InzzYhKqQ5Z69ksOlYEmP/OMjsAS0I9XYy9wZZGqHAmAdHlhVtuKjRgZ0LRL4Amnu/+k4IIaoDCYiqkfmH53M69TTu9u78o/E/yM7P5vUmr2OwMzBhxwSmxk6ltX9rGns1Bsq/FL9wgnItd0fmbjsDmFdO7T2XQqfwkgGRVW9L4fLyAo808CbCoQnGNX/QSTuYRGWghiYNnTJx8eh5Luzvin9EOy4nnOfM3IHkuIfS+pVPiwpXioKxNav6HY1Pw9vVju8K6hfs7YyTnY3VKrjyKty2wNvVnsiweAav+RKAt1q8hQvBlnx6Gy3B3i5k55mKbi5YPebz8Id80fQcob93hcTCJf0aQEFAM+g93zxElhYvE6iFEOIukICoiiv8430l+wrT904HYEiTwTjZOOKid2FQ00Eopfj1yK8cTznOiA0jWNRjEdn52eVeil84QblwTo6jXse55Czq+7iUPvxUvLdl3ThIOERAeF9+Dl5Jg19Gobl6yvyDqQF/zRXLfbXVefj1SbJ/1eOMIgJFpt1VSE80Bw/piailw1DeYWgfHWluv0mRmZPHqoPxFK6od7DVcSUjl/o+rtcdCitL4RllfcP68tbGt1AoApwDSM5OpqbBg5zEx3Cz8ygR7AHgVhu2fonX4T95UhmL0kOi4MFX4cdn4fGJRfOFXHxlArUQQtwFEhBVcdNip3Li6gnsbOxJy0sjxC2Ezftmc2nXdAa1nwABzdBkXqF/Rg6jgQsZF/jbH38jLTet3Evxn2jsx1frj3OwYAVV2DUrp667fN4jBLZMocahJTxYPN23MYneD+G1bxrJdZ8kNyudrItHqaUuYK8pOr7CNjkO06RwtP5NMCUdJzU7n89PhTKweTa7zyYzccURTiQWruzSYVIQ7u/KjL7N8HTSg1Z708v7g92C+enwT6w5uwaTMqHT6MjIyyC4RjCh7gG83mQQoZ4BRb1B7cfx35Ym6m0eBocXQ/FAyNUf+v4G3qFwIfam6iGEEKLiSEBUxQVf2M+PSdGkYf4jfDH1DEl52XRJugyzOpo3+1OKnjpbnFq/xLCTCziXdo7QGqHM6DSjXEvx9/11lYMXUrHRasg3KfPKqdLO4rpWeiLsmmVeXl7IIxj6/Awe9UjZuxmvfdO43GQgwU3akm808dvS33l6zwBWGJvTTHsUL00qmHLhr52gIEO587esnxg0wZHdphBqkMZK/f/hbGfD+R6/8uwPx/lvJx88/3wFvMOhoCcJKHW4rTQdanZg7oG5xCWZJ2aHuYfx5SOf4+HoCRpNUYDl6AHHV+N1ZBl9i09Yd/GDhn+DbZ+bh8a8Q0t5FyGEEHeTBERVXFSdKDadWcfvTuZVTcGZaUy5lIiHqWBeS3bBEvOm/ejU/A0ezDzFjvgd6HV63O1qmId9bhAgfF8wL6dtsAfrjyaVnbH4cRQX98L8PpD6F+hd4NFRsPwdeGYWeNQDwL1gJVbhvzY6LY3reMEe8HriPZbkBrJv22oeTlvKMzab0WogQHOFAK7wq36seXWXRmM+piRfg8+itqzT16D2/BRzL82l/eZgJLij+dDUP4eZg6TiQ1TF64x5CHLUppHEJcXhYONAVn4W77d+H4/tX5t3ln7wVTi7DQ78BomHrNvv4A5Pfg5h3czt3/a59XXZcFEIIe4ZCYiquoinOBAzDpR5n6F3U7PwaPUGNHwaMq/AD0+Z88V+D3E/07fta+wA4pLi2PBTTzr4tb7uHJbkjFyW7L0AwBON/a8fEBUeR1HvUVj+LuRngd4JmvSGWq1LZHf3rQXt3zH/ew1XB1tebhUCD4ewfUsorNrM5uaf408iNc4sxy1xJ+Yti5T5oUCrjARpL0HhiFXyKfj1JfMu0Fob0OkhrIf1G60fD4mH4YlJ4OzFpG3/ZcWZldgo+HfQ0/zfsR9h40Q4tAJy0+HwH9b3ezWA4M7m4KffYvNO02WR+UJCCHHPSEBUxSVnXua4Krbp4pNfQIOe5ueFc1Ye/xQ2fgzpl/DZNg0CzGdyTcw6wUOe/bC9Tvm/xJwjJ99EhL8rD4d4Xn+CsncYbP2yaHm51hZ0dlDnodLzlxIguHnVZFvNVwnxqmlJ83A29yD51qxH3Sb9gX9zfM96gn/vwblHvqBmYCBkJJFxehdOu2eQFdwNB70NpF+C87vBmAPGXPPj90FwZpN5blNWMqbYeWgyEtAcW8mP7p7MdTL3lo1NTCL4wkTz1+rQEsjNM1dGozXvLu1eF3r/VDQ3SHqDhBDiviYBURW3++giAALz8vnLtoxvd2BzeHMfrP0vXjum8vLVFBa5OHPGRsc8TTr9i+ctNs/GZFL8EH0WgH6tauNjcLj+BGWTEfIyil77PwC9593U8nJP/9p4vjzRKi3f0Zsp+U/TxdG7KFFrbmuOW12o1xYAJ8/6sHsGDo+OKOqpubAHvu4ADZ+BowW9PDHfWoqZ5mbghKcbD2dk8pGjedgsODeXv2xtaZ2Ty+vJKXjZu8NjwyCoA+RlwsxH4Nk5158bJL1BQghxX5Gdqqu4XadWAdDMxtX8x9vOrehi8V4KW3uI+i9eT81iaHIK/7pyFYDpOz/l8tmt5vzpieYdlTd8BMCGo4mcvZKJq70NPZoGXL8iJ9bB4oHm5+EFw3SPf1Jyefkt9Ji4+9RCtX8Xd5+iobVr5x+VrWBZfJsh8PZpeGoGOBXUycmLYL8WbHN25X1vT5RGg16j47KTB/W6TsbrpdUMupqCV++foeVA8G5g7iG6lvQGCSHEfU8CoiouJuU4AG19HmRQk9fx8mxQdLG0IMTdfKxGjwa9aJCTS7pWw1e/Pw/fdISvHkSd2YLJy9wL9N220wD8rXlNHPTX7BZtMhXtyHxhDyzoC6Y88xydh4ZUaBsL9zXydi3aBLLU+Uc3Ckx0tub5TM//an79/K+kPtCbTFW0sWKYaxCLn/qDqNBnsARTN3IbwZ4QQoi7Q4bMqrC0lHMcIRfQENn0JfNBoeWkbdafULtcDl/YyK8uTvS6EIunvgYfNn6M4PxLdLucyfqjiQD0bVW7ZAGFE6jbDIH5vc1DUY6e4BlSei9KRSttSKqstIIgqXATSw1gAqYc+4k5p/8E4GHPpmxMimVkg/7X34pAeoOEEKJSkh6iKmzP/nmYNBpqmTR4lzcYKvYHvV1Id2y1tiiNhn5+PnR3t2XXhS3U0zjww/YzKAUP1/eijrsDJqPRuhzvMDi1EWZ3hswk8/lcygS+De+voKFY7820vdMYvmE4F7KSGOHlYQmGmvs0Z1CjV835HT2s7722HdIbJIQQlZL0EFVhMec2ABDpVPMGOYsp1osS5eJLXUNdei99jizywaSYf+48AaeHsZMBQEd6NbBjz8Tu1meKZV2FczshOw1zXwvg18S84WLhnKF7PKHY0htU7GiNuq51+eHgDzx+di1GZycAHG0c6d2gNxonT3Mmp2sCIpkYLYQQVYIERFWVUsSknwFbLZGB7W65mJAaIXzSaDBv7p1CllbDf/wC+fqvM7zHbIbazcN2pRENigx9snml2JH/wZoPIKvgDLKareBcNHSbXBQM3QcKzyMb1XIUSVlJ/Hr0VxYfX0yOsWjX7GC3YL7p/A0eDh4kZibyepPX8XK4f9oghBCi4khAVEVlXYjhgI259yMy7G+3VZavu3kStb3Ojhhy+JtnBL8mHsBZY97fSAGGq/thUph5WAzMQ2QPvQnhPeDr9rf1/ndCsFswPxz8gUd/eRRTsUnT9jb2POT/EGvOruHDth9a5gt5OXrd8JBbIYQQlZfMIaqi9h5YQL5Ggy82BLjVvb3CCoaJHvZ8GaU0nHRJo09QFxRwqk4nhgfUYrqboSgYcqsN/9oPHUff3vveIZl5mcRciiE9L90SDBnsDPzfQ//Hlt5b+Efjf9zjGgohhLjbpIeoioq5sBU0EOlaz2qezM0wmRQaDXg5ePGwZx8WbXbD1v0h7Dw2c1hzkHa1AsjWnUKvd6FzqxGQlgorR8Fz32F09gGTEV2xicdKKRQK7d1YZVa8HcXmC8UmxDJq8yjOppk3lOxYqyOrz67m605fE+4RflfrJYQQ4v4hAVFVlJ1KTE4i2NsRWeexWy5myppjHLuURqu67izb3BilwM/4HHVd6rAr7QdSdDow5ZJjyuWDuOkc9H+YUCdHNBe38tnWt9FqtHzx6BfU7fA2ydnJ/Hf9MILdgvnnA/+0vMfdCJKm7Z3G0StH8Xb0ZsGRBSgUdjo7OtXqRL+Ifqw+u9oqv5eDl8wXEkKIakYCoioo98Qa9unNJ5BF1o0q1z2FvUHFe5Oc7XSsPZzA//bHA2BnoyUtO59ng1/gWYJ5Z88YQl2COZp2grS8NOac+RO8PWH/NEsZTy15ytI7o5Qi5lIMOcYcHvR7kNqutZm0a9IdDZKUUqTnprPu3DoU5o0i9Vo9DjYOdKjVodR7ZL6QEEJUPxIQVUH7jywmR6vFXaMnyDWoXPcU9gaNfjKc3Wev8s2mk+w+e9UqT0N/AzNeiMTT2Y6Dl8NgD3zQ/kN8nXxZemIpM/fN4GpuKg46ewx2bqTlpZGRl4FCoQp2rU7OSWbOgTnMOTAHAA0aYhNjqe1am1b+rdCg4b/R/y0RJBlN5n2OdFpdudMOXj7IRzs+YnfCbgBstbbkmfII9whnyiNTZPWYEEIICwmIqhKTeYJwzKUYcNQS6R6GMinQmNAUDxqM5nw6XVEPjJuDLeuOJLD8QLzlxA2AB2q58bfImoxcHMfYHhF4Opc8yd7d3p0XIl6guW9zei3txbdd51rm4+xL3Mfzy55ndOvR2OnsOJ58nMUnFpOcnQyAQpGUlcS7m837+WgKjsOITYzFzc6N4BrB6DQ6xmwdg06rY3bUbDwcPLicdZmXVrwEwOyo2bjZuXHkyhGGrh9KnimPSO9IVp5Zae5pQkukTyRDmg2h3//68W7Ld2X1mBBCCCsSEFUlGybAuR3EkAM4EOnfiuNTn+GoKZBWL0/E09mOpPQces/YBmiY2T+S7SevMH/HWfb+lWJVlJ/Bnm/6NyfC38D+8ykl3qq882xsCk6dD/cItwRJUUFR9Frai6mPTeVqzlW2nN/C6rOryTHmWIa1krKSmLBzQonyHvn5Eex0dlb7BT3686PmXiiKIrkVZ1YA5l4hRxtHejXohV53o4NehRBCVFcSEFUhJq9QjFu/YI+/OwDN102mVo6RGblNGD5hLQYHW65k5GIymUOHRyZusLo/wt+Vbo39+Gj5EWa+YA6GylIRPSseDh60C2zHk/We5ODlg/Ra2otxbceRa8xlX+I+Vp5ZSXpeOjqNDqMyD4UpFNnGbKtyigdCAU4B+Dn7cSrlFJezLxPhEWEZHjt4+eBt1VcIIUTVJQFRFTIt/Ri7PV3J1GpxMZpwd6vNm74hbD+RRs4FEwlpOaXeF+DmwDf9mxPm58r+8yl8tPyI1XVvFzvefCwEb5eSw2XFldZrdLMrtuq51SPcI5xn6j9Drwa96LW0F/OemEeIWwiXsy8TcymGdza9w4cPfUgdQx1MysTp1NO8v+V95j8+n4ZeDQEsAZbV8JisHhNCCFEGCYiqkOCTW/hWb/6W2isTPe0zSb20l6y0HtR2d+TMlUze6dqAhv4GDA62JKRl8/LcXczoF0mYn2uZ5Xq72vOvTvVv+P6l9RqVmnYLgYmtzhZfJ1+CDOZJ4sE1gi1DcIVDYVrt9VelyXwhIYQQZZGdqquKoyvxORNNXsGy+UQbG/IzXEk/8S/qObbhw6fMPSdtgz1pG+JJo0ADPq72JYopb2/Q7SgMTLwcb70nSQghhKhI0kNUFeRmcuV/w/m3tydGjYZGTqHEZRzB9UJrMm3caB/qjZtj+SYUl7c3qKKVtyfpdtKEEEKIsmiUKr7IWpQlNTUVg8FASkoKrq5lDy/dC8bVYxl4/HuiHRzQ5Hpie6EzuXXmYX9qAHnaRnzYsxEt6tTgx+1neb5lLbwLeoYSUrNLpAkhhBBVSXn/fksPUWVkMlGwrTQkHGbagTlEu7lgiw0p5/qhtPk4AR7efvzwQnvL3kHX9vzcq94gIYQQ4n5TreYQTZ06laCgIOzt7YmMjGTTpk33uko3ZjLBNZ14xrXjMC3oC+kJbPzzdWa4uQDQOj4QY64PD9asTU7iY4zq0rLUjRSFEEIIYa3aBEQLFixg6NChjBo1ij179tCuXTu6du3K2bNn70l98vPzMBmNVmm5Odnk5ljvsZO35r/kz38e0hPNCekJfHLkZ/6RvIMfZ0TyrroEgH+ekXN6e2a+0Jz3urQiN6kTNew870pbhBBCiMqu2gyZTZo0iZdffplXXnkFgClTprBixQqmTZvG+PHj73p93v/+WeJz4/lHmw+ws3Xk1MX9TDvxJQDPevbEwdaBSylnWJq1DQ0mnpzdknSdPUds8jngpEdp7NnuaJ73o1WKC1pnnm38FJ3CfUrdWVoIIYQQZasWAVFubi4xMTG88847VumdO3dm69atpd6Tk5NDTk7RRoapqakVWqdabuFsSj7O4F3DydUWnDBva+6w+yplSVFGGw2gY67BqSDBfCaZzgRapSVPZ8Ix35fven5PiKcfcHeWzgshhBBVSbUYMktKSsJoNOLj42OV7uPjQ3x8fKn3jB8/HoPBYHnUrFmzQuv0+lPj+bbjAlxM5vlBOqWokW/CO8+EXcEhrU5GEw2zbXkg2wnnfPO3yjXPgTdCxrP2uZ2MD3sbgDERL1mCISiaLC0rx4QQQojyqRYBUSFNwaaFhZRSJdIKvfvuu6SkpFge586dq/D6BNdqyLvhowD4KHQkG18+wJpXDvBhA3Pa2LBRzH9tN9+9Fs2YcHPw85+IofyjTTfcnSTYEUIIISpKtRgy8/T0RKfTlegNSkhIKNFrVMjOzg47u/t7yMlo7271rxBCCCFuTbXoIdLr9URGRrJq1Sqr9FWrVtGmTZt7VKubU1rwExTQkKbOzxEU0PBeVUsIIYSoEqpFDxHAsGHD6NevH82bN6d169Z8/fXXnD17loEDB97TetXxDaPb4XrU8Q27blpQQEOanrQOfsK8A/n+mffvan2FEEKIqqhaHd0xdepUPv74Yy5evEjDhg2ZPHkyDz/8cLnuvZ+P7hBCCCFE6cr797taBUS3QwIiIYQQovIp79/vajGHSAghhBDieiQgEkIIIUS1JwGREEIIIao9CYiEEEIIUe1JQCSEEEKIak8CIiGEEEJUexIQCSGEEKLak4BICCGEENWeBERCCCGEqPaqzVlmt6twQ+/U1NR7XBMhhBBClFfh3+0bHcwhAVE5paWlAVCzZs17XBMhhBBC3Ky0tDQMBkOZ1+Uss3IymUxcuHABFxcXNBpNhZWbmppKzZo1OXfuXLU5I626tVnaW7VJe6s2aW/lp5QiLS0Nf39/tNqyZwpJD1E5abVaAgMD71j5rq6uVeaHr7yqW5ulvVWbtLdqk/ZWbtfrGSokk6qFEEIIUe1JQCSEEEKIak8ConvMzs6O0aNHY2dnd6+rctdUtzZLe6s2aW/VJu2tPmRStRBCCCGqPekhEkIIIUS1JwGREEIIIao9CYiEEEIIUe1JQCSEEEKIak8CotuwceNGnnzySfz9/dFoNPz222+Wa3l5ebz99ts0atQIJycn/P39eeGFF7hw4cINy42Li6N9+/Y4ODgQEBDABx98UOIMlg0bNhAZGYm9vT1169Zl+vTpFd28Eq7X3mu99tpraDQapkyZcsNyK3N7Dx06RPfu3TEYDLi4uNCqVSvOnj173XIra3vT09MZPHgwgYGBODg4EBYWxrRp025Y7v3a3vHjx9OiRQtcXFzw9vamZ8+eHDlyxCqPUooxY8bg7++Pg4MDHTp04MCBAzcs+35s843aW5V+Z23duhWdTkeXLl3u2HsALFy4kPDwcOzs7AgPD2fx4sVW18eMGYNGo7F6+Pr63tE6TZ06laCgIOzt7YmMjGTTpk1A1fr+3jFK3LJly5apUaNGqYULFypALV682HLt6tWrqmPHjmrBggXq8OHDatu2baply5YqMjLyumWmpKQoHx8f1bt3bxUXF6cWLlyoXFxc1MSJEy15Tp48qRwdHdWbb76pDh48qGbOnKlsbW3Vr7/+eqeaqpS6fnuLW7x4sWrSpIny9/dXkydPvm6Zlbm9x48fV+7u7mrEiBFq9+7d6sSJE2rp0qXq0qVLZZZZmdv7yiuvqHr16ql169apU6dOqRkzZiidTqd+++23Msu8n9sbFRWl5syZo/bv369iY2PVE088oWrVqqXS09MteSZMmKBcXFzUwoULVVxcnOrVq5fy8/NTqampla7NN2pvVfqd9fLLL6s333xTOTk5qTNnztxWWfn5+cpoNJZI37p1q9LpdGrcuHHq0KFDaty4ccrGxkZFR0db8owePVpFRESoixcvWh4JCQm3VZ/r+emnn5Stra2aOXOmOnjwoNXXoCp9f+8UCYgqyPUChEI7duxQwHX/g06dOlUZDAaVnZ1tSRs/frzy9/dXJpNJKaXUW2+9pRo0aGB132uvvaZatWp16w24SWW196+//lIBAQFq//79qnbt2jcMiCpze3v16qX69u17U+VU5vZGRESoDz74wCqtWbNm6r333iuznMrSXqWUSkhIUIDasGGDUkopk8mkfH191YQJEyx5srOzlcFgUNOnTy+znMrS5mvbW5rK+DsrPT1dubi4qMOHD6tevXqpsWPHWq6tW7dOAWrp0qWqcePGys7OTj344INq3759ljxz5sxRBoNB/fHHHyosLEzpdDp18uTJEu/z3HPPqS5dulilRUVFqd69e1tejx49WjVp0qTC21iWBx98UA0cONAqrUGDBuqdd94pNX9l/P7eSTJkdhelpKSg0Whwc3OzpA0YMIAOHTpYXm/bto327dtbbYoVFRXFhQsXOH36tCVP586drcqOiopi165d5OXl3ckmXJfJZKJfv36MGDGCiIiIUvNUlfaaTCb+/PNP6tevT1RUFN7e3rRs2bLEMFNVaS9A27ZtWbJkCefPn0cpxbp16zh69ChRUVGWPJW5vSkpKQC4u7sDcOrUKeLj463qZmdnR/v27dm6daslrbK2+dr2lpWnsv3OWrBgAaGhoYSGhtK3b1/mzJlTYnhnxIgRTJw4kZ07d+Lt7U337t2t6pGZmcn48eP55ptvOHDgAN7e3iXep6w2Ff/ZADh27Bj+/v4EBQXRu3dvTp48WYGtLZKbm0tMTEyJOnXu3LlEnQpVxu/vnSQB0V2SnZ3NO++8Q58+fawOzPPz86NWrVqW1/Hx8fj4+FjdW/g6Pj7+unny8/NJSkq6U024oY8++ggbGxuGDBlSZp6q0t6EhATS09OZMGECXbp0YeXKlTz11FM8/fTTbNiwwZKvqrQX4PPPPyc8PJzAwED0ej1dunRh6tSptG3b1pKnsrZXKcWwYcNo27YtDRs2tKpfaXUrvAaVs82ltfdalfV31qxZs+jbty8AXbp0IT09nTVr1ljlGT16NJ06daJRo0bMnTuXS5cuWc3/ycvLY+rUqbRp04bQ0FCcnJxKvE9ZbSr+s9GyZUu+++47VqxYwcyZM4mPj6dNmzZcvny5IpsMQFJSEkaj8YZ1KlRZv793kpx2fxfk5eXRu3dvTCYTU6dOtbo2fvz4Evk1Go3V68JPN8XTy5PnboqJieGzzz5j9+7d161DVWmvyWQCoEePHvzrX/8CoGnTpmzdupXp06fTvn17oOq0F8wBUXR0NEuWLKF27dps3LiRQYMG4efnR8eOHYHK297Bgwezb98+Nm/eXOJaaXUrnlYZ23y99kLl/Z115MgRduzYwaJFiwCwsbGhV69ezJ492/IzCtC6dWvLc3d3d0JDQzl06JAlTa/X07hxYwDOnj1LeHi45drIkSMZOXJkmW0qnta1a1fL80aNGtG6dWvq1avH3LlzGTZsWEU0uYQb1Qkq7/f3TpOA6A7Ly8vjueee49SpU6xdu9YqEi+Nr69viWg+ISEBKIrKy8pjY2ODh4dHBda+/DZt2kRCQoLVJwuj0cjw4cOZMmWKpWv1WpW1vZ6entjY2Fj9ogQICwsr848MVN72ZmVlMXLkSBYvXswTTzwBQOPGjYmNjWXixIlWf2yKqwztfeONN1iyZAkbN24kMDDQkl64Gig+Ph4/Pz+rul37abi4+73NZbW3UGX+nTVr1izy8/MJCAiwpCmlsLW1JTk5+br3Fv/D7eDgYHnt7+9PbGys5VrhEGNZbbrez4aTkxONGjXi2LFj5W5TeXl6eqLT6W5Yp8r8/b3TZMjsDir8wTt27BirV68u1w9G69at2bhxI7m5uZa0lStX4u/vT506dSx5Vq1aZXXfypUrad68Oba2thXahvLq168f+/btIzY21vLw9/dnxIgRrFixosz7Kmt79Xo9LVq0KLFM++jRo9SuXbvM+ypre/Py8sjLy0Ortf6VodPpLL1lpbmf26uUYvDgwSxatIi1a9cSFBRkdT0oKAhfX1+ruuXm5rJhwwbatGlTZrn3a5tv1F6o3L+z8vPz+e677/j000+tfg/t3buX2rVr8+OPP1ryRkdHW54nJydz9OhRGjRoUGq5NjY2BAcHWx6FAVFZbbrez0ZOTg6HDh2yCrAril6vJzIyskSdVq1aZalTZf7+3hV3b/521ZOWlqb27Nmj9uzZowA1adIktWfPHnXmzBmVl5enunfvrgIDA1VsbKzVssucnBxLGe+8847q16+f5fXVq1eVj4+P+vvf/67i4uLUokWLlKura6lLHP/1r3+pgwcPqlmzZt2VJY7Xa29pSltlVpXau2jRImVra6u+/vprdezYMfXFF18onU6nNm3aVCXb2759exUREaHWrVunTp48qebMmaPs7e3V1KlTK2V7X3/9dWUwGNT69eut/n9mZmZa8kyYMEEZDAa1aNEiFRcXp/7+97+XWHZfWdp8o/ZW9t9ZixcvVnq9Xl29erXEtZEjR6qmTZtaVplFRESo1atXq7i4ONW9e3dVq1YtSxsLV5ndyJYtW5ROp1MTJkxQhw4dUhMmTCix7H748OFq/fr16uTJkyo6Olp169ZNubi4qNOnT1dYu4srXHY/a9YsdfDgQTV06FDl5OSkTp8+Xem/v3eDBES3ofA/17WP/v37q1OnTpV6DVDr1q2zlNG/f3/Vvn17q3L37dun2rVrp+zs7JSvr68aM2aMZXljofXr16sHHnhA6fV6VadOHTVt2rR72t7SlBYQVbX2zpo1SwUHByt7e3vVpEmTEnvyVKX2Xrx4UQ0YMED5+/sre3t7FRoaqj799FOrulem9pb1/3POnDmWPCaTSY0ePVr5+voqOzs79fDDD6u4uDircipLm2/U3sr+O6tbt27q8ccfL/VaTEyMAtSnn36qAPXHH3+oiIgIpdfrVYsWLVRsbKwlb3kDIqWU+uWXX1RoaKiytbVVDRo0UAsXLrS6Xrhvla2trfL391dPP/20OnDgwC23sTy++uorVbt2baXX61WzZs0s2ypU9u/v3aBR6pr1iEIIIUQVtH79eh555BGSk5OtlpoLATKHSAghhBBCAiIhhBBCCBkyE0IIIUS1Jz1EQgghhKj2JCASQgghRLUnAZEQQgghqj0JiIQQQghR7UlAJISoFsaMGUPTpk3vdTWEuKHx48fTokULXFxc8Pb2pmfPniWOCVJKMWbMGPz9/XFwcKBDhw4cOHDAKs/XX39Nhw4dcHV1RaPRcPXq1RLvtXv3bjp16oSbmxseHh784x//ID09vVz1/Ouvv9Dr9WUee1KRNmzYQGRkJPb29tStW5fp06dbXf/222/RaDQlHtnZ2eV+DwmIhBCVXmm/CIs/BgwYwL///W/WrFlzr6sqxA1t2LCBf/7zn0RHR7Nq1Sry8/Pp3LkzGRkZljwff/wxkyZN4ssvv2Tnzp34+vrSqVMn0tLSLHkyMzPp0qULI0eOLPV9Lly4QMeOHQkODmb79u0sX76cAwcOMGDAgHLV89tvv+W5554jMzOTLVu23FabwRzk5efnl0g/deoUjz/+OO3atWPPnj2MHDmSIUOGsHDhQqt8rq6uXLx40ephb29/UxUQQohKrfi5TFOmTFGurq5WaaWdbyVEZZGQkKAAyzEcJpNJ+fr6qgkTJljyZGdnK4PBoKZPn17i/sJjeZKTk63SZ8yYoby9vZXRaLSkFZ5leOzYsevWyWQyqbp166rly5ert99+W7344otW1wuPCpk/f75q3bq1srOzU+Hh4VbHhBTWa/ny5SoyMlLZ2tqqtWvXlnivt956SzVo0MAq7bXXXlOtWrWyvL6ZI1fKIj1EQohKz9fX1/IwGAxoNJoSadcOmQ0YMICePXsybtw4fHx8cHNzY+zYseTn5zNixAjc3d0JDAxk9uzZVu91/vx5evXqRY0aNfDw8KBHjx6cPn367jZYVCspKSkAuLu7A+Yek/j4eDp37mzJY2dnR/v27dm6dWu5y83JyUGv16PVFoUCDg4OAGzevPm6965bt47MzEw6duxIv379+Pnnn616pwqNGDGC4cOHs2fPHtq0aUP37t25fPmyVZ633nqL8ePHc+jQIRo3blyijG3btlm1FSAqKopdu3aRl5dnSUtPT6d27doEBgbSrVs39uzZc+MvQjESEAkhqq21a9dy4cIFNm7cyKRJkxgzZgzdunWjRo0abN++nYEDBzJw4EDOnTsHmIcgHnnkEZydndm4cSObN2/G2dmZLl26kJube49bI6oipRTDhg2jbdu2NGzYEID4+HgAfHx8rPL6+PhYrpXHo48+Snx8PJ988gm5ubkkJydbhtcuXrx43XtnzZpF79690el0REREEBwczIIFC0rkGzx4MM888wxhYWFMmzYNg8HArFmzrPJ88MEHdOrUiXr16uHh4VGijPj4+FLbmp+fT1JSEgANGjTg22+/ZcmSJcyfPx97e3seeughjh07Vu6vhwREQohqy93dnc8//5zQ0FBeeuklQkNDyczMZOTIkYSEhPDuu++i1+st8yN++ukntFot33zzDY0aNSIsLIw5c+Zw9uxZ1q9ff28bI6qkwYMHs2/fPubPn1/imkajsXqtlCqRdj0RERHMnTuXTz/9FEdHR3x9falbty4+Pj7odDpLHmdnZ5ydnenatSsAV69eZdGiRfTt29dSVt++fUv0pgK0bt3a8tzGxobmzZtz6NAhqzzNmze3PC98L2dnZwYOHHjdthZPb9WqFX379qVJkya0a9eOn3/+mfr16/PFF1+U++thU+6cQghRxURERFgNF/j4+Fg+hQPodDo8PDxISEgAICYmhuPHj+Pi4mJVTnZ2NidOnLg7lRbVxhtvvMGSJUvYuHEjgYGBlnRfX1/A3HPi5+dnSU9ISCjRk3Ijffr0oU+fPly6dAknJyc0Gg2TJk0iKCgIgGXLllmGpQqH0+bNm0d2djYtW7a0lKOUwmQycfDgQcLDw6/7ntcGN05OTpbnsbGxlueurq6W9l7b85WQkICNjU2pPUoAWq2WFi1a3FQPkQREQohqy9bW1uq1RqMpNc1kMgFgMpmIjIzkxx9/LFGWl5fXnauoqFaUUrzxxhssXryY9evXW4KTQkFBQfj6+rJq1SoeeOABAHJzc9mwYQMfffTRLb1nYSA1e/Zs7O3t6dSpEwC1a9cukXfWrFkMHz68xGq0IUOGMHv2bCZOnGhJi46O5uGHHwYgPz+fmJgYBg8eXGY9goODS6S1bt2aP/74wypt5cqVNG/evMT/10JKKWJjY2nUqFGZ73UtCYiEEKKcmjVrxoIFC/D29rZ8ehWiov3zn/9k3rx5/P7777i4uFh6RwwGAw4ODmg0GoYOHcq4ceMICQkhJCSEcePG4ejoSJ8+fSzlxMfHEx8fz/HjxwGIi4vDxcWFWrVqWSZof/nll7Rp0wZnZ2dWrVrFiBEjmDBhAm5ubqXWLTY2lt27d/Pjjz+W2H/o73//O6NGjWL8+PGWtK+++oqQkBDCwsKYPHkyycnJvPTSSzf19Rg4cCBffvklw4YN49VXX2Xbtm3MmjXLahhx7NixtGrVipCQEFJTU/n888+JjY3lq6++Kv8b3dYaNSGEuM+Utfx29OjRqkmTJpbX/fv3Vz169LDK0759e/Xmm29apdWuXVtNnjxZKaVURkaGCgkJUR06dFAbN25UJ0+eVOvXr1dDhgxR586dq9iGiGoLKPUxZ84cSx6TyaRGjx6tfH19lZ2dnXr44YdVXFycVTmjR4++YTn9+vVT7u7uSq/Xq8aNG6vvvvvuunUbPHiwCg8PL/VaQkKC0ul0auHChZZl9/PmzVMtW7ZUer1ehYWFqTVr1ljyl7UdQGnWr1+vHnjgAaXX61WdOnXUtGnTrK4PHTpU1apVS+n1euXl5aU6d+6stm7desNyi5MeIiGEKCdHR0c2btzI22+/zdNPP01aWhoBAQE89thj0mMkKowqmDB8PRqNhjFjxjBmzJgy89zoOsB33313U3W73iRlLy8vy8aKhVtRhIWFER0dXWr+Dh06lKutAO3bt2f37t1lXp88eTKTJ08uV1ll0ajy1kYIIYQQohxOnz5NUFAQe/bsqTRH5siyeyGEEEJUe9JDJIQQQohqT3qIhBBCCFHtSUAkhBBCiGpPAiIhhBBCVHsSEAkhhBCi2pOASAghhBDVngREQgghhKj2JCASQgghRLUnAZEQQgghqj0JiIQQQghR7UlAJIQQQohq7/8BSLRdywL5pt4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compare TKE with three simulations\n",
+    "fig, ax = plt.subplots(ncols=1)\n",
+    "\n",
+    "ds_stat6[\"CST_TKE\"].plot(ax=ax, marker=\"1\", label=\"Sim6-75\")\n",
+    "ds_stat7[\"CST_TKE\"].plot(ax=ax, marker=\"1\", label=\"Sim7-150\")\n",
+    "ds_stat8[\"CST_TKE\"].plot(ax=ax, marker=\"1\", label=\"Sim8-300\")\n",
+    "\n",
+    "ax.legend()\n",
+    "ax.set_title(\"Comparisons of TKE\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83beedb6-b9bc-4efb-a87b-cbb8b66c67d7",
+   "metadata": {},
+   "source": [
+    "# We can see that simulation 6 has the biggest TKE value and it reaches its peak at the earliest time (around 18:00 UTC) among the three simulations.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "64fbddf3-bcb8-4c95-97c7-a277223c5a35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 24 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# To further investigate the impact of large-scale forcing scales,\n",
+    "# I looked at each component of TKE and specific humidity (water vapor mixing ratio) variance\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(4, 3, figsize=(20, 10))\n",
+    "\n",
+    "ds_stat6[\"CSP_U2\"].isel(z= slice(0, 150)).plot(ax=ax[0,0], y =\"z\") #one plot\n",
+    "ds_stat7[\"CSP_U2\"].isel(z= slice(0, 150)).plot(ax=ax[0,1], y = \"z\")\n",
+    "ds_stat8[\"CSP_U2\"].isel(z= slice(0, 150)).plot(ax=ax[0,2], y = \"z\")\n",
+    "\n",
+    "ds_stat6[\"CSP_V2\"].isel(z= slice(0, 150)).plot(ax=ax[1,0], y =\"z\") #one plot\n",
+    "ds_stat7[\"CSP_V2\"].isel(z= slice(0, 150)).plot(ax=ax[1,1], y = \"z\")\n",
+    "ds_stat8[\"CSP_V2\"].isel(z= slice(0, 150)).plot(ax=ax[1,2], y = \"z\")\n",
+    "\n",
+    "ds_stat6[\"CSP_W2\"].isel(z_stag= slice(0, 150)).plot(ax=ax[2,0], y =\"z_stag\") #one plot\n",
+    "ds_stat7[\"CSP_W2\"].isel(z_stag= slice(0, 150)).plot(ax=ax[2,1], y = \"z_stag\")\n",
+    "ds_stat8[\"CSP_W2\"].isel(z_stag= slice(0, 150)).plot(ax=ax[2,2], y = \"z_stag\")\n",
+    "\n",
+    "ds_stat6[\"CSP_QV2\"].isel(z= slice(0, 150)).plot(ax=ax[3,0], y =\"z\") #one plot\n",
+    "ds_stat7[\"CSP_QV2\"].isel(z= slice(0, 150)).plot(ax=ax[3,1], y = \"z\")\n",
+    "ds_stat8[\"CSP_QV2\"].isel(z= slice(0, 150)).plot(ax=ax[3,2], y = \"z\")\n",
+    "\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# U wind variance, V wind variance, W wind variance, the total water vapor mixing ratio from top to bottom.\n",
+    "# Simulation 6, 7, and 8 from the left to the right.\n",
+    "\n",
+    "# If you find something from the below figure, please let me know!\n",
+    "# lim\n",
+    "# What I can see is the variances rapidly increase in Simulation 6. \n",
+    "# I guess that is the atmospheric boundary layer (ABL) growth. \n",
+    "# That is, the ABL grows at the fastest rate in the smallest Large Scale Forcing Scale. \n",
+    "\n",
+    "# However, the water vapor mixing ratio (the bottom row) is the lowest in Simulation 6. \n",
+    "# Is it because the strong turbulence in Simulation 6 makes a well-mixed layer??\n",
+    "# If you can interpret this, please let me know.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2db57492-4e4a-4a34-85e5-f7c355d8ee07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f3f178e1790>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 24 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(20, 10))\n",
+    "\n",
+    "# Create a plot for temperature\n",
+    "ax = fig.add_subplot(4, 3, 1)\n",
+    "\n",
+    "ds_stat6[\"CSP_U2\"].isel(z= slice(0, 150)).plot( y =\"z\") #one plot\n",
+    "\n",
+    "#ax.set_title('Temperature')\n",
+    "#ax.set_xlim(110, 130)\n",
+    "#ax.set_ylim(290, 315)\n",
+    "\n",
+    "# Create a plot for dewpoint\n",
+    "ax2 = fig.add_subplot(4, 3, 2)\n",
+    "ds_stat7[\"CSP_U2\"].isel(z= slice(0, 150)).plot( y = \"z\")\n",
+    "\n",
+    "ax3 = fig.add_subplot(4, 3, 3)\n",
+    "ds_stat8[\"CSP_U2\"].isel(z= slice(0, 150)).plot( y = \"z\")\n",
+    "\n",
+    "ax4 = fig.add_subplot(4, 3, 4)\n",
+    "ds_stat6[\"CSP_V2\"].isel(z= slice(0, 150)).plot( y =\"z\") #one plot\n",
+    "\n",
+    "ax5 = fig.add_subplot(4, 3, 5)\n",
+    "ds_stat7[\"CSP_V2\"].isel(z= slice(0, 150)).plot( y = \"z\")\n",
+    "\n",
+    "ax6 = fig.add_subplot(4, 3, 6)\n",
+    "ds_stat8[\"CSP_V2\"].isel(z= slice(0, 150)).plot( y = \"z\")\n",
+    "\n",
+    "ax7 = fig.add_subplot(4, 3, 7)\n",
+    "ds_stat6[\"CSP_W2\"].isel(z_stag= slice(0, 150)).plot( y =\"z_stag\") #one plot\n",
+    "\n",
+    "ax8 = fig.add_subplot(4, 3, 8)\n",
+    "ds_stat7[\"CSP_W2\"].isel(z_stag= slice(0, 150)).plot( y = \"z_stag\")\n",
+    "\n",
+    "ax9 = fig.add_subplot(4, 3, 9)\n",
+    "ds_stat8[\"CSP_W2\"].isel(z_stag= slice(0, 150)).plot( y = \"z_stag\")\n",
+    "\n",
+    "ax10 = fig.add_subplot(4, 3, 10)\n",
+    "ds_stat6[\"CSP_QV2\"].isel(z= slice(0, 150)).plot( y =\"z\") #one plot\n",
+    "#ax10.set_ylim(0, 4)\n",
+    "\n",
+    "ax11 = fig.add_subplot(4, 3, 11)\n",
+    "ds_stat7[\"CSP_QV2\"].isel(z= slice(0, 150)).plot( y = \"z\")\n",
+    "#ax11.set_ylim(0, 4)\n",
+    "\n",
+    "ax12 = fig.add_subplot(4, 3, 12)\n",
+    "ds_stat8[\"CSP_QV2\"].isel(z= slice(0, 150)).plot( y = \"z\")\n",
+    "#ax12.set_ylim(0, 4)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54dadfeb-c6af-4ecd-996b-302eeeb450c1",
+   "metadata": {},
+   "source": [
+    "# Conclusion\n",
+    "The large-scale forcing scales impact cloud core fraction due to different TKEs.\n",
+    "As Girish suggests today (our assumption), the model should carefully choose the large-forcing length scale to represent the environment appropriately on a given day.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/LassoARMClouds.ipynb b/LassoARMClouds.ipynb
new file mode 100644
index 0000000..13883d9
--- /dev/null
+++ b/LassoARMClouds.ipynb
@@ -0,0 +1,2024 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "53504431-c50f-4028-bc9c-430f628f8fe2",
+   "metadata": {},
+   "source": [
+    "## Simulation vs Observational Data of Shallow Cumulus Clouds over the Southern Great Plains on April 4th, 2019"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02d4b6b0-dd31-4cb5-baa0-174412b92da1",
+   "metadata": {},
+   "source": [
+    "# Import prerequisites"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "91516d5b-84c6-4a7c-8fba-dd394f908569",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lasso Simulation Data\n",
+    "\n",
+    "# import dask\n",
+    "from datetime import datetime\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import xwrf\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba9d2623-2b85-49cb-adfa-098cc0e091e2",
+   "metadata": {},
+   "source": [
+    "# Load Data and View Variables, Dimensions, and Data Details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "21ba9de9-b8fb-42c6-973d-2868ebb130f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR 1: PROJ: proj_create_from_database: Open of /opt/conda/share/proj failed\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 72GB\n",
+       "Dimensions:         (Time: 91, bottom_top: 226, bottom_top_stag: 227,\n",
+       "                     south_north: 250, west_east: 250, west_east_stag: 251,\n",
+       "                     south_north_stag: 251)\n",
+       "Coordinates:\n",
+       "    XTIME           (Time) datetime64[ns] 728B ...\n",
+       "Dimensions without coordinates: Time, bottom_top, bottom_top_stag, south_north,\n",
+       "                                west_east, west_east_stag, south_north_stag\n",
+       "Data variables: (12/179)\n",
+       "    Times           (Time) |S19 2kB ...\n",
+       "    CST_CLDLOW      (Time) float32 364B ...\n",
+       "    CST_CLDTOT      (Time) float32 364B ...\n",
+       "    CST_LWP         (Time) float32 364B ...\n",
+       "    CST_IWP         (Time) float32 364B ...\n",
+       "    CST_PRECW       (Time) float32 364B ...\n",
+       "    ...              ...\n",
+       "    CSV_IWC         (Time, bottom_top, south_north, west_east) float32 5GB ...\n",
+       "    CSV_CLDFRAC     (Time, bottom_top, south_north, west_east) float32 5GB ...\n",
+       "    CSS_LWP         (Time, south_north, west_east) float32 23MB ...\n",
+       "    CSS_IWP         (Time, south_north, west_east) float32 23MB ...\n",
+       "    CSS_CLDTOT      (Time, south_north, west_east) float32 23MB ...\n",
+       "    CSS_CLDLOW      (Time, south_north, west_east) float32 23MB ...\n",
+       "Attributes: (12/96)\n",
+       "    TITLE:                                  OUTPUT FROM WRF V3.8.1 MODEL\n",
+       "    START_DATE:                            2019-04-04_12:00:00\n",
+       "    WEST-EAST_GRID_DIMENSION:              251\n",
+       "    SOUTH-NORTH_GRID_DIMENSION:            251\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:             227\n",
+       "    DX:                                    100.0\n",
+       "    ...                                    ...\n",
+       "    config_aerosol:                        NA\n",
+       "    config_forecast_time:                  15.0 h\n",
+       "    config_boundary_method:                Periodic\n",
+       "    config_microphysics:                   Thompson (mp_physics=8)\n",
+       "    config_nickname:                       runlas20190404v1addhm\n",
+       "    simulation_origin_host:                cumulus-login2.ccs.ornl.gov</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8c4b0be4-fb75-4f56-9c96-bc647373abd6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8c4b0be4-fb75-4f56-9c96-bc647373abd6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>Time</span>: 91</li><li><span>bottom_top</span>: 226</li><li><span>bottom_top_stag</span>: 227</li><li><span>south_north</span>: 250</li><li><span>west_east</span>: 250</li><li><span>west_east_stag</span>: 251</li><li><span>south_north_stag</span>: 251</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-66ce33fa-1de3-4151-b64d-4218d379f1da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66ce33fa-1de3-4151-b64d-4218d379f1da' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>XTIME</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-46fc5f8c-6dcd-42dc-8b58-4b2b1ee39c8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46fc5f8c-6dcd-42dc-8b58-4b2b1ee39c8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d636a461-906b-4034-aadb-e06df78dd04b' class='xr-var-data-in' type='checkbox'><label for='data-d636a461-906b-4034-aadb-e06df78dd04b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>minutes since 2019-04-04 12:00:00</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8fc6c36-11b4-471b-bfd5-00046d022046' class='xr-section-summary-in' type='checkbox'  ><label for='section-d8fc6c36-11b4-471b-bfd5-00046d022046' class='xr-section-summary' >Data variables: <span>(179)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Times</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>|S19</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-55b3d46a-77f1-4997-ad3a-ee363d0162ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55b3d46a-77f1-4997-ad3a-ee363d0162ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d81546a5-d37b-48d9-9782-5bcf394e561b' class='xr-var-data-in' type='checkbox'><label for='data-d81546a5-d37b-48d9-9782-5bcf394e561b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[91 values with dtype=|S19]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_CLDLOW</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-87d5ce09-084a-4fa8-b89c-72af5fd1019a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87d5ce09-084a-4fa8-b89c-72af5fd1019a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-496f5096-9c63-473a-8356-951632a5f36d' class='xr-var-data-in' type='checkbox'><label for='data-496f5096-9c63-473a-8356-951632a5f36d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Fractional low-cloud cover (&lt;5 km)</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_CLDTOT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-16cd32fd-4040-4240-9c61-5d7f74f4f9bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16cd32fd-4040-4240-9c61-5d7f74f4f9bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ead25d8e-73d3-49bb-9f1f-0db9846243bf' class='xr-var-data-in' type='checkbox'><label for='data-ead25d8e-73d3-49bb-9f1f-0db9846243bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Fractional cloud cover</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_LWP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc422650-663a-4cf6-bd2d-02d061683163' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc422650-663a-4cf6-bd2d-02d061683163' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74cd0a2b-d6c7-4236-8061-c0004248e83c' class='xr-var-data-in' type='checkbox'><label for='data-74cd0a2b-d6c7-4236-8061-c0004248e83c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Vertical integrated liquid water path (based on ql)</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_IWP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b902a1f9-dd93-4db4-b446-f7bf18d819b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b902a1f9-dd93-4db4-b446-f7bf18d819b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3055540c-d6cb-4bd6-9afe-7aa910d4ef65' class='xr-var-data-in' type='checkbox'><label for='data-3055540c-d6cb-4bd6-9afe-7aa910d4ef65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Vertical integrated ice water path (based on qf)</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_PRECW</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3fd321d3-21b2-47a1-97ba-bdcf1506940c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fd321d3-21b2-47a1-97ba-bdcf1506940c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d080d42-4e32-449f-a74e-894507c4e403' class='xr-var-data-in' type='checkbox'><label for='data-4d080d42-4e32-449f-a74e-894507c4e403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Vertical integrated water vapor</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_TKE</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a59d0fa9-d2ef-4ca4-801f-3e2b9cb665d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a59d0fa9-d2ef-4ca4-801f-3e2b9cb665d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4a7cc8a-4b48-46bf-8112-f055c38c001a' class='xr-var-data-in' type='checkbox'><label for='data-c4a7cc8a-4b48-46bf-8112-f055c38c001a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Vertical integrated TKE</dd><dt><span>units :</span></dt><dd>kg/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_TSAIR</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4a6df12a-c853-4b94-8e01-ead1937a6d32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a6df12a-c853-4b94-8e01-ead1937a6d32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2b379bb-35ad-480b-895a-3a7ef389b7c2' class='xr-var-data-in' type='checkbox'><label for='data-f2b379bb-35ad-480b-895a-3a7ef389b7c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_PS</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-451dd912-ca9e-4b66-a17a-fc45ef2a881e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-451dd912-ca9e-4b66-a17a-fc45ef2a881e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b8d4c90-a458-4309-a28e-9ae03de0b12a' class='xr-var-data-in' type='checkbox'><label for='data-4b8d4c90-a458-4309-a28e-9ae03de0b12a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_PRECT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-abc25d97-33cd-4c1c-ba78-45805d7ccf2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abc25d97-33cd-4c1c-ba78-45805d7ccf2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24239ecb-4ec3-484f-9df3-8ebd76df2f1a' class='xr-var-data-in' type='checkbox'><label for='data-24239ecb-4ec3-484f-9df3-8ebd76df2f1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Total precipitation at surface</dd><dt><span>units :</span></dt><dd>mm/sec</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_SH</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aa918319-6507-4e6c-af4e-9d0010cf7b07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa918319-6507-4e6c-af4e-9d0010cf7b07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15ed3ca8-a63a-4d48-8398-669e18a663bf' class='xr-var-data-in' type='checkbox'><label for='data-15ed3ca8-a63a-4d48-8398-669e18a663bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface sensible heat flux</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_LH</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-efacb933-8fa4-4a35-8d33-4c3e01939b91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efacb933-8fa4-4a35-8d33-4c3e01939b91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81a639a6-0c9a-4ba9-a968-10b71287fb6f' class='xr-var-data-in' type='checkbox'><label for='data-81a639a6-0c9a-4ba9-a968-10b71287fb6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface latent heat flux</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FSNTC</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b0612483-bef4-40b9-ada1-a05e86de0269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0612483-bef4-40b9-ada1-a05e86de0269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd8ff217-2134-4eb9-a9ed-1376c5d5721d' class='xr-var-data-in' type='checkbox'><label for='data-fd8ff217-2134-4eb9-a9ed-1376c5d5721d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TOA SW net upward clear-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FSNT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f965d8d4-3ef4-4dad-826b-e64497513216' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f965d8d4-3ef4-4dad-826b-e64497513216' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0769bf7d-18e9-4d23-8bc0-53edf73c0831' class='xr-var-data-in' type='checkbox'><label for='data-0769bf7d-18e9-4d23-8bc0-53edf73c0831' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TOA SW net upward total-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FLNTC</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8a118076-849a-4bef-964c-fc92b0618c2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a118076-849a-4bef-964c-fc92b0618c2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6bd02c9-5392-48f4-81b5-8c952a009597' class='xr-var-data-in' type='checkbox'><label for='data-d6bd02c9-5392-48f4-81b5-8c952a009597' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TOA LW (net) upward clear-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FLNT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f6cc8a4b-8f44-4e6c-9d61-1c1f912bafe5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6cc8a4b-8f44-4e6c-9d61-1c1f912bafe5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2500fdb7-139d-4fc7-9314-d6b5128f6e6b' class='xr-var-data-in' type='checkbox'><label for='data-2500fdb7-139d-4fc7-9314-d6b5128f6e6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TOA LW (net) upward total-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FSNSC</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-63c90ca8-33a3-46e1-b724-3d0f9a4d4ea2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63c90ca8-33a3-46e1-b724-3d0f9a4d4ea2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2e436f2-a0de-4264-adba-5b7354fa6470' class='xr-var-data-in' type='checkbox'><label for='data-d2e436f2-a0de-4264-adba-5b7354fa6470' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface SW net upward clear-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FSNS</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-24621d48-575e-4cc0-8dc8-a07484d2b1c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24621d48-575e-4cc0-8dc8-a07484d2b1c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5b7e277-9707-4c14-bf17-2ce4ffe46ca9' class='xr-var-data-in' type='checkbox'><label for='data-e5b7e277-9707-4c14-bf17-2ce4ffe46ca9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface SW net upward total-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FLNSC</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a0e68b96-7195-4c71-862d-e4e0dbc78086' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0e68b96-7195-4c71-862d-e4e0dbc78086' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49c43b99-c33b-49f7-9c25-1184e1589f23' class='xr-var-data-in' type='checkbox'><label for='data-49c43b99-c33b-49f7-9c25-1184e1589f23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface LW net upward clear-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_FLNS</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-518e65b2-96e0-4505-a3ff-043ab2a4432b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-518e65b2-96e0-4505-a3ff-043ab2a4432b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc432213-a4a0-4939-8d03-923306826081' class='xr-var-data-in' type='checkbox'><label for='data-cc432213-a4a0-4939-8d03-923306826081' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface LW net upward total-sky radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_SWINC</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2adb86be-d6ed-4e49-9aed-06c28a8a5967' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2adb86be-d6ed-4e49-9aed-06c28a8a5967' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b248494a-69aa-4ae2-aa40-7c6fb56ef228' class='xr-var-data-in' type='checkbox'><label for='data-b248494a-69aa-4ae2-aa40-7c6fb56ef228' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TOA solar insolation</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_TSK</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9fd3325b-9010-4de9-b2a6-fda2a537545c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fd3325b-9010-4de9-b2a6-fda2a537545c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea2f2996-316e-4a81-88ba-c19e7eb5cbce' class='xr-var-data-in' type='checkbox'><label for='data-ea2f2996-316e-4a81-88ba-c19e7eb5cbce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface skin temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CST_UST</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0bcffe99-bdd1-47bc-a555-e2abb4d2b56a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bcffe99-bdd1-47bc-a555-e2abb4d2b56a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7e70bf0-bdb3-431d-ab7f-30ab325061cd' class='xr-var-data-in' type='checkbox'><label for='data-f7e70bf0-bdb3-431d-ab7f-30ab325061cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Surface friction velocity</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[91 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_Z</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-60d29317-82ef-4426-9633-21c9527b3787' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60d29317-82ef-4426-9633-21c9527b3787' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfb85037-a428-461a-b0cd-d745cdf09d28' class='xr-var-data-in' type='checkbox'><label for='data-cfb85037-a428-461a-b0cd-d745cdf09d28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Half level height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_Z8W</span></div><div class='xr-var-dims'>(Time, bottom_top_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6171ddf4-c0d2-4ab3-9b8b-1ff0cc2d598d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6171ddf4-c0d2-4ab3-9b8b-1ff0cc2d598d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed6c8326-5338-46a0-af73-b49b787e98fe' class='xr-var-data-in' type='checkbox'><label for='data-ed6c8326-5338-46a0-af73-b49b787e98fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Full level height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[20657 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_DZ8W</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a652874f-c2c9-4ec2-970d-d31acadda282' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a652874f-c2c9-4ec2-970d-d31acadda282' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3291d3ca-3656-460b-974b-f9896ad6a36c' class='xr-var-data-in' type='checkbox'><label for='data-3291d3ca-3656-460b-974b-f9896ad6a36c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dz at full level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_U</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-88677059-1254-4263-86da-13f067e0b1cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88677059-1254-4263-86da-13f067e0b1cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-410d7f20-f7d8-4657-81f2-c2134ec9201d' class='xr-var-data-in' type='checkbox'><label for='data-410d7f20-f7d8-4657-81f2-c2134ec9201d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Zonal wind</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_V</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-79b95fcd-948f-407b-84ac-b4c6cbfc0e2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79b95fcd-948f-407b-84ac-b4c6cbfc0e2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76e7fe11-d5e6-4105-a721-5423ae950cb2' class='xr-var-data-in' type='checkbox'><label for='data-76e7fe11-d5e6-4105-a721-5423ae950cb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Meridional wind</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W</span></div><div class='xr-var-dims'>(Time, bottom_top_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-25773226-f193-4dcf-9800-462bccc78848' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25773226-f193-4dcf-9800-462bccc78848' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bc56ee3-50cc-437f-8069-8b7911cdef81' class='xr-var-data-in' type='checkbox'><label for='data-6bc56ee3-50cc-437f-8069-8b7911cdef81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Vertical motion</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[20657 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_P</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b992466a-3e76-4ff5-840d-76af4ca4b633' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b992466a-3e76-4ff5-840d-76af4ca4b633' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2cc018f-296e-4b25-92ba-77c4392f4766' class='xr-var-data-in' type='checkbox'><label for='data-f2cc018f-296e-4b25-92ba-77c4392f4766' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TH</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-962e7717-4f7a-4b66-b54d-c49474546a17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-962e7717-4f7a-4b66-b54d-c49474546a17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6ecf64d-3f5a-4906-9598-cab8c5d17485' class='xr-var-data-in' type='checkbox'><label for='data-e6ecf64d-3f5a-4906-9598-cab8c5d17485' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Potential temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THV</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45826667-03f1-47ab-8a0b-3ccb8b8ada83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45826667-03f1-47ab-8a0b-3ccb8b8ada83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-805b226c-c5dd-4f9e-8d5b-f4cca95fd148' class='xr-var-data-in' type='checkbox'><label for='data-805b226c-c5dd-4f9e-8d5b-f4cca95fd148' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Virtual potential temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THL</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b26af5ab-a0f6-40d4-b28e-85eb63982ae2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b26af5ab-a0f6-40d4-b28e-85eb63982ae2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c760f46-13c3-4098-ba18-c54cbe37d1da' class='xr-var-data-in' type='checkbox'><label for='data-9c760f46-13c3-4098-ba18-c54cbe37d1da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water potential temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QV</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a85ac4c3-a0db-45e3-8209-700e58c1e324' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a85ac4c3-a0db-45e3-8209-700e58c1e324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a006465-8aca-4f8e-be59-6976be1b081b' class='xr-var-data-in' type='checkbox'><label for='data-7a006465-8aca-4f8e-be59-6976be1b081b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Water vapor mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a041689c-64fc-48cb-8ac3-79c0d08a5b35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a041689c-64fc-48cb-8ac3-79c0d08a5b35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e9506e2-321e-4be5-8815-a92a6bc6630c' class='xr-var-data-in' type='checkbox'><label for='data-8e9506e2-321e-4be5-8815-a92a6bc6630c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud water mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QI</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c17ca506-763d-411b-b679-3490727f6910' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c17ca506-763d-411b-b679-3490727f6910' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b56f110-bfd6-4041-ad77-55bb7c884167' class='xr-var-data-in' type='checkbox'><label for='data-1b56f110-bfd6-4041-ad77-55bb7c884167' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Ice crystal (cloud ice) mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QL</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-82bc9a7b-8ea7-414e-b022-3e4bf843079d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82bc9a7b-8ea7-414e-b022-3e4bf843079d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fca2cacd-716f-435c-ba34-f31e89b4bb4f' class='xr-var-data-in' type='checkbox'><label for='data-fca2cacd-716f-435c-ba34-f31e89b4bb4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QF</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-753a4e11-83cc-4881-8ea1-dc1b972f3a50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-753a4e11-83cc-4881-8ea1-dc1b972f3a50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4732aa4b-d93a-4ee2-ace2-b84dbc2b4981' class='xr-var-data-in' type='checkbox'><label for='data-4732aa4b-d93a-4ee2-ace2-b84dbc2b4981' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Frozen water mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QT</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-19d7016c-139b-4cd2-a57b-a162280ee83a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19d7016c-139b-4cd2-a57b-a162280ee83a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e536a872-e9a2-49dc-b396-e30886e22fc7' class='xr-var-data-in' type='checkbox'><label for='data-e536a872-e9a2-49dc-b396-e30886e22fc7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Total (vapor+liquid+frozen) water mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_LWC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-be9bf07f-472f-4e11-9ccb-02f4de160dc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be9bf07f-472f-4e11-9ccb-02f4de160dc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c5fd89c-ca7e-4c52-bd68-dde7d67c2d63' class='xr-var-data-in' type='checkbox'><label for='data-6c5fd89c-ca7e-4c52-bd68-dde7d67c2d63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water content (based on ql)</dd><dt><span>units :</span></dt><dd>kg/m^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_IWC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5dfb4593-7018-4212-bf6f-e502f73c3b9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dfb4593-7018-4212-bf6f-e502f73c3b9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f69caea-b929-42a2-bd88-80827b0b736a' class='xr-var-data-in' type='checkbox'><label for='data-6f69caea-b929-42a2-bd88-80827b0b736a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Ice water content (based on qf)</dd><dt><span>units :</span></dt><dd>kg/m^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SPEQV</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d7d9720-6cd4-46eb-be5b-094c269150ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d7d9720-6cd4-46eb-be5b-094c269150ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daa1bbbb-9bbc-49a3-9960-8204af1172ab' class='xr-var-data-in' type='checkbox'><label for='data-daa1bbbb-9bbc-49a3-9960-8204af1172ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Specific humidity</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_A_CL</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-990e6bd7-89f9-4d08-909d-6938b5c54822' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-990e6bd7-89f9-4d08-909d-6938b5c54822' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a151d9ce-0333-4077-a07d-5e253e563375' class='xr-var-data-in' type='checkbox'><label for='data-a151d9ce-0333-4077-a07d-5e253e563375' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Fraction of cloudy grid points</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_RHO</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a91e5103-f9b0-45de-83d1-59c52c5d4dfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a91e5103-f9b0-45de-83d1-59c52c5d4dfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63aad69e-cae6-4dc2-8340-a5fa7ba05ed3' class='xr-var-data-in' type='checkbox'><label for='data-63aad69e-cae6-4dc2-8340-a5fa7ba05ed3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Density</dd><dt><span>units :</span></dt><dd>kg/m^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_U2</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-afca97b5-0c58-406f-8338-6f6c17ba539c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afca97b5-0c58-406f-8338-6f6c17ba539c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b7d1daf-21a9-440b-8753-d6d4ff2592aa' class='xr-var-data-in' type='checkbox'><label for='data-4b7d1daf-21a9-440b-8753-d6d4ff2592aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u_p^2</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_V2</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9520a8ef-2982-4fe0-80c0-145a17d5d048' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9520a8ef-2982-4fe0-80c0-145a17d5d048' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c10457b-c988-43ae-9cbb-8b41cd2c426e' class='xr-var-data-in' type='checkbox'><label for='data-1c10457b-c988-43ae-9cbb-8b41cd2c426e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>v_p^2</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_U2V2</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-642c8de4-8284-49b1-a73a-e7f8b865a8cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-642c8de4-8284-49b1-a73a-e7f8b865a8cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-124fbb04-499b-4531-bec8-685d22465812' class='xr-var-data-in' type='checkbox'><label for='data-124fbb04-499b-4531-bec8-685d22465812' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u_p^2+v_p^2</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W2</span></div><div class='xr-var-dims'>(Time, bottom_top_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b35513f0-5c8d-45a2-b6ea-535d70a8c443' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b35513f0-5c8d-45a2-b6ea-535d70a8c443' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9323622f-5e68-49e4-a32b-311941352b9f' class='xr-var-data-in' type='checkbox'><label for='data-9323622f-5e68-49e4-a32b-311941352b9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>w_p^2</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[20657 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W3</span></div><div class='xr-var-dims'>(Time, bottom_top_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f839668c-9dc3-4c8d-a668-812bd2eab44e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f839668c-9dc3-4c8d-a668-812bd2eab44e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faac4aa5-96d9-4bdf-8d69-4ca9c29ed4ea' class='xr-var-data-in' type='checkbox'><label for='data-faac4aa5-96d9-4bdf-8d69-4ca9c29ed4ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>w_p^3</dd><dt><span>units :</span></dt><dd>m^3/s^3</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[20657 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WSKEW</span></div><div class='xr-var-dims'>(Time, bottom_top_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b99925e8-4dfb-43c0-87a1-a9a1e80c89c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b99925e8-4dfb-43c0-87a1-a9a1e80c89c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adbb7284-38ac-4151-af50-ead24d1b1895' class='xr-var-data-in' type='checkbox'><label for='data-adbb7284-38ac-4151-af50-ead24d1b1895' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Skewness &lt;w3&gt;/&lt;w2&gt;^(3/2)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[20657 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_UW</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b2ca5ecb-26ff-4da9-b95c-fc2223526396' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2ca5ecb-26ff-4da9-b95c-fc2223526396' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23188b03-75f4-484e-beba-179e890b32c8' class='xr-var-data-in' type='checkbox'><label for='data-23188b03-75f4-484e-beba-179e890b32c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>x-momentum flux uw (rs+sgs)</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_VW</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2a591b7d-92f8-4a7b-913e-7873de466165' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a591b7d-92f8-4a7b-913e-7873de466165' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d484962-1cf4-49cb-8a0f-16126d86f3c4' class='xr-var-data-in' type='checkbox'><label for='data-7d484962-1cf4-49cb-8a0f-16126d86f3c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>y-momentum flux vw (rs+sgs)</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTH</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-69c36d9b-db00-43bd-815d-16cddce6e11c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69c36d9b-db00-43bd-815d-16cddce6e11c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96a67d26-84de-45cd-aebf-c0894986f93d' class='xr-var-data-in' type='checkbox'><label for='data-96a67d26-84de-45cd-aebf-c0894986f93d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Potential temperature flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTHV</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cdf7198b-ae2e-4a05-b5b0-21ff9c78d7b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdf7198b-ae2e-4a05-b5b0-21ff9c78d7b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29a39a65-068f-4b0e-ba89-e4158bbfc099' class='xr-var-data-in' type='checkbox'><label for='data-29a39a65-068f-4b0e-ba89-e4158bbfc099' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Virtual potential temperature flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTHL</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2ae163a5-b968-4a5f-84ac-c2ba5e021ef8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ae163a5-b968-4a5f-84ac-c2ba5e021ef8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76fd4e7d-a416-43e8-b21c-05252d033724' class='xr-var-data-in' type='checkbox'><label for='data-76fd4e7d-a416-43e8-b21c-05252d033724' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water potential temperature flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQV</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-525a4409-3498-438a-9ea9-b209c2983bf3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-525a4409-3498-438a-9ea9-b209c2983bf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa20d13-dd70-40a4-ab43-ab298e3bfd0f' class='xr-var-data-in' type='checkbox'><label for='data-caa20d13-dd70-40a4-ab43-ab298e3bfd0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Water vapor flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4ede36bd-fe0d-4e19-8f7f-4eba7e97317b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ede36bd-fe0d-4e19-8f7f-4eba7e97317b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f9a2a87-fba0-490a-8a08-2659fef7cc54' class='xr-var-data-in' type='checkbox'><label for='data-6f9a2a87-fba0-490a-8a08-2659fef7cc54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud water flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQI</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d92b0dae-094c-43b5-ac9a-3ac34d713c07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d92b0dae-094c-43b5-ac9a-3ac34d713c07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4429a437-190b-4dbc-8863-07409d406c10' class='xr-var-data-in' type='checkbox'><label for='data-4429a437-190b-4dbc-8863-07409d406c10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Ice crystal (cloud ice) flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQL</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1b903deb-336e-4bae-8174-134da4c8d24b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b903deb-336e-4bae-8174-134da4c8d24b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee4f0501-8907-4ad8-9d93-9198b4e25c9f' class='xr-var-data-in' type='checkbox'><label for='data-ee4f0501-8907-4ad8-9d93-9198b4e25c9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQF</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-24f26254-548d-4cee-8e51-1c48e494cdd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24f26254-548d-4cee-8e51-1c48e494cdd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd1b374c-b690-4889-8d88-a77dabd5a93c' class='xr-var-data-in' type='checkbox'><label for='data-fd1b374c-b690-4889-8d88-a77dabd5a93c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Frozen water flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQT</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2dc0cd5e-5afa-47e8-8ba1-44a574d4fe73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2dc0cd5e-5afa-47e8-8ba1-44a574d4fe73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b3c7a8e-982f-4370-957d-72dc97b1bf0e' class='xr-var-data-in' type='checkbox'><label for='data-2b3c7a8e-982f-4370-957d-72dc97b1bf0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Total water flux (rs+sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_UW_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9e5b7092-dd85-440a-bdcd-d1e92736062d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e5b7092-dd85-440a-bdcd-d1e92736062d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2329c41d-a88e-4be5-9b94-8974125a1ccc' class='xr-var-data-in' type='checkbox'><label for='data-2329c41d-a88e-4be5-9b94-8974125a1ccc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>x-momentum flux uw (sgs)</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_VW_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2db746ef-3cad-4813-8307-9d2a0c82e44a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2db746ef-3cad-4813-8307-9d2a0c82e44a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd5e42cd-6b1a-4c9c-a506-15fade5da729' class='xr-var-data-in' type='checkbox'><label for='data-dd5e42cd-6b1a-4c9c-a506-15fade5da729' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>y-momentum flux vw (sgs)</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTH_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-88609568-e52f-4b6d-b8cf-ddedc022400a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88609568-e52f-4b6d-b8cf-ddedc022400a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d6397f0-b5fd-4eb6-9dcc-53e8f75c266f' class='xr-var-data-in' type='checkbox'><label for='data-1d6397f0-b5fd-4eb6-9dcc-53e8f75c266f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Potential temperature flux (sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTHV_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b4f47ed-ca8a-4fb8-ac9e-3f23bca56786' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b4f47ed-ca8a-4fb8-ac9e-3f23bca56786' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31974b93-4806-4545-ada1-4d18da46aaf7' class='xr-var-data-in' type='checkbox'><label for='data-31974b93-4806-4545-ada1-4d18da46aaf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Virtual potential temperature flux (sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WTHL_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6cdef3af-7517-40f3-86ea-6ee52f446250' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6cdef3af-7517-40f3-86ea-6ee52f446250' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eee4df3c-4945-4a4e-94be-734b322a6180' class='xr-var-data-in' type='checkbox'><label for='data-eee4df3c-4945-4a4e-94be-734b322a6180' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water potential temperature flux (sgs)</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQV_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-46cc87ee-7bfc-4ea7-bec3-26e580e9575f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46cc87ee-7bfc-4ea7-bec3-26e580e9575f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e00d6f96-27b1-4b39-a202-d8f7716b0062' class='xr-var-data-in' type='checkbox'><label for='data-e00d6f96-27b1-4b39-a202-d8f7716b0062' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Water vapor flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQC_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ecb986ea-b928-44a0-b959-e6d1443a37dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ecb986ea-b928-44a0-b959-e6d1443a37dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c81e64e9-5940-4cf9-9cdf-1dd64f6c2f3e' class='xr-var-data-in' type='checkbox'><label for='data-c81e64e9-5940-4cf9-9cdf-1dd64f6c2f3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud water flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQI_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cb1c0dd9-1d50-47de-8fb3-6078792e11a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb1c0dd9-1d50-47de-8fb3-6078792e11a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8257b2a-468d-44e4-92b9-0932773a1250' class='xr-var-data-in' type='checkbox'><label for='data-f8257b2a-468d-44e4-92b9-0932773a1250' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Ice crystal (cloud ice) flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQL_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe665552-2d8f-4588-9d4e-ad9aba0f578f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe665552-2d8f-4588-9d4e-ad9aba0f578f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc870667-9fb8-4f06-be3c-7042d83a3797' class='xr-var-data-in' type='checkbox'><label for='data-bc870667-9fb8-4f06-be3c-7042d83a3797' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Liquid water flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQF_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8baaf8db-4f44-4258-a18c-820e2b3680ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8baaf8db-4f44-4258-a18c-820e2b3680ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a663a28c-6f28-4cf7-9977-a894208d15a5' class='xr-var-data-in' type='checkbox'><label for='data-a663a28c-6f28-4cf7-9977-a894208d15a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Frozen water flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WQT_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-78cb12fd-2df5-4276-861a-c3f1b5236026' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78cb12fd-2df5-4276-861a-c3f1b5236026' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a318ddc-4061-4a44-bac7-e031e2482e24' class='xr-var-data-in' type='checkbox'><label for='data-9a318ddc-4061-4a44-bac7-e031e2482e24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Total water flux (sgs)</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SEDFQC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-902961af-3655-44b0-962a-c2c21442716d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-902961af-3655-44b0-962a-c2c21442716d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50a4ae1b-4cc4-46f4-a4c9-6687b93e9d54' class='xr-var-data-in' type='checkbox'><label for='data-50a4ae1b-4cc4-46f4-a4c9-6687b93e9d54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Sedimentation flux of qc</dd><dt><span>units :</span></dt><dd>kg /m^2/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SEDFQR</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-334e8eea-ac25-4829-afb8-5798085ac52b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-334e8eea-ac25-4829-afb8-5798085ac52b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff6aab33-3fa3-4cfd-83f5-c06bdb436116' class='xr-var-data-in' type='checkbox'><label for='data-ff6aab33-3fa3-4cfd-83f5-c06bdb436116' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Sedimentation (Precipitation) flux of qr</dd><dt><span>units :</span></dt><dd>kg /m^2/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_COND</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e7e940ab-cada-4efa-964b-843758e50b72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7e940ab-cada-4efa-964b-843758e50b72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f26d9734-6b87-4da1-8cfc-ed37ea9e11e7' class='xr-var-data-in' type='checkbox'><label for='data-f26d9734-6b87-4da1-8cfc-ed37ea9e11e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dth/dt due to net condensation</dd><dt><span>units :</span></dt><dd>K/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_LW</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8b690ccd-60d6-4873-84d5-27e8df94bfa0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b690ccd-60d6-4873-84d5-27e8df94bfa0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87670f56-2522-4262-8055-017ea030fbe1' class='xr-var-data-in' type='checkbox'><label for='data-87670f56-2522-4262-8055-017ea030fbe1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dth/dt due to LW radiation</dd><dt><span>units :</span></dt><dd>K/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_SW</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2ec20810-e4e7-46cc-8d93-eaa997aa0328' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ec20810-e4e7-46cc-8d93-eaa997aa0328' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9626eb55-b034-47d2-8e10-3af05098c42f' class='xr-var-data-in' type='checkbox'><label for='data-9626eb55-b034-47d2-8e10-3af05098c42f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dth/dt due to SW radiation</dd><dt><span>units :</span></dt><dd>K/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_LS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d5a8e08-7ddf-4c95-b205-00e12fc421b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d5a8e08-7ddf-4c95-b205-00e12fc421b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37581560-98b4-41f2-84ee-06eae9f08231' class='xr-var-data-in' type='checkbox'><label for='data-37581560-98b4-41f2-84ee-06eae9f08231' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dth/dt due to large-scale forcing</dd><dt><span>units :</span></dt><dd>K/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_PR</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9d13628-d77c-4a44-bced-81c5f00388e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d13628-d77c-4a44-bced-81c5f00388e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6aadf8c-51ef-4fae-bae6-987cf2da5313' class='xr-var-data-in' type='checkbox'><label for='data-d6aadf8c-51ef-4fae-bae6-987cf2da5313' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqv/dt due to conversion to precipitation</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_COND</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7c02b09a-c6bb-4fae-a007-71814a8fbcca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c02b09a-c6bb-4fae-a007-71814a8fbcca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d92e39c-b494-48a3-99c6-6ad223110f5b' class='xr-var-data-in' type='checkbox'><label for='data-5d92e39c-b494-48a3-99c6-6ad223110f5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqv/dt due to net condensation</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_LS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-860ebdc5-baee-47d3-9542-d5bc47e3ff19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-860ebdc5-baee-47d3-9542-d5bc47e3ff19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bb77aa1-eb8c-4060-a053-1270be68b123' class='xr-var-data-in' type='checkbox'><label for='data-9bb77aa1-eb8c-4060-a053-1270be68b123' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqv/dt due to large-scale forcing</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QCDT_PR</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1ca44823-09db-403b-8bc2-d8a2baf1b78d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ca44823-09db-403b-8bc2-d8a2baf1b78d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15f2bb69-1a3a-4db0-a100-397bc0eef713' class='xr-var-data-in' type='checkbox'><label for='data-15f2bb69-1a3a-4db0-a100-397bc0eef713' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqc/dt due to conversion to precipitation</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QCDT_SED</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e7a2ed8f-ff41-4252-b16f-7c7071d5227e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7a2ed8f-ff41-4252-b16f-7c7071d5227e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7baa571-1007-44ac-ba56-7a509acb12d8' class='xr-var-data-in' type='checkbox'><label for='data-c7baa571-1007-44ac-ba56-7a509acb12d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqc/dt due to sedimentation</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QRDT_SED</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1b557e13-6341-4bcd-b315-bb3ec1839b53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b557e13-6341-4bcd-b315-bb3ec1839b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14751f57-4bfc-440a-b4bc-5a661783d8f4' class='xr-var-data-in' type='checkbox'><label for='data-14751f57-4bfc-440a-b4bc-5a661783d8f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>dqr/dt due to sedimentation</dd><dt><span>units :</span></dt><dd>kg/kg/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_LSHOR</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-24c61e2a-9545-42bf-b232-185d580f9f33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24c61e2a-9545-42bf-b232-185d580f9f33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-650d2a41-a4ec-4cdd-92ee-8be59ce3d19f' class='xr-var-data-in' type='checkbox'><label for='data-650d2a41-a4ec-4cdd-92ee-8be59ce3d19f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>th tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_LSHOR</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c3549aa1-07a0-427d-8e37-a85c2f9a73b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3549aa1-07a0-427d-8e37-a85c2f9a73b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72996fe1-c75b-47a6-ad20-8925b26a943f' class='xr-var-data-in' type='checkbox'><label for='data-72996fe1-c75b-47a6-ad20-8925b26a943f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_LSVER</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-53186290-159a-41d4-ab93-1eb8c7346a61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53186290-159a-41d4-ab93-1eb8c7346a61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-552c1642-86cf-4635-a05d-db4e33bc8da5' class='xr-var-data-in' type='checkbox'><label for='data-552c1642-86cf-4635-a05d-db4e33bc8da5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>th tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_LSVER</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-11aefbed-2985-430d-8e2b-e45f81bad826' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11aefbed-2985-430d-8e2b-e45f81bad826' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e23d0b62-d347-476c-91d5-25aa2ab6d5cd' class='xr-var-data-in' type='checkbox'><label for='data-e23d0b62-d347-476c-91d5-25aa2ab6d5cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THDT_LSRLX</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b627de1f-465a-4c5b-873a-0341a6798ab9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b627de1f-465a-4c5b-873a-0341a6798ab9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82e4c8bd-8477-417a-92d6-54c3a173ca5c' class='xr-var-data-in' type='checkbox'><label for='data-82e4c8bd-8477-417a-92d6-54c3a173ca5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>th tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QVDT_LSRLX</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-073f8c00-c7f0-4ee3-ba0f-9b4962a4853b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-073f8c00-c7f0-4ee3-ba0f-9b4962a4853b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3052b6a-23bf-4e56-b4fb-b5b2a32a59d6' class='xr-var-data-in' type='checkbox'><label for='data-e3052b6a-23bf-4e56-b4fb-b5b2a32a59d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_UDT_LS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5fde544-95ab-415b-bdf5-cebc098dde16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5fde544-95ab-415b-bdf5-cebc098dde16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-765cc30d-0131-47d2-a85e-8309cb774412' class='xr-var-data-in' type='checkbox'><label for='data-765cc30d-0131-47d2-a85e-8309cb774412' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_VDT_LS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-680f6baa-6c35-41e8-b5a2-9520fcaecf76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-680f6baa-6c35-41e8-b5a2-9520fcaecf76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77193653-b050-4a4f-958c-2f07a2fa09b1' class='xr-var-data-in' type='checkbox'><label for='data-77193653-b050-4a4f-958c-2f07a2fa09b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>v tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_UDT_LSVER</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-88679243-2dad-451d-9e6a-e5d2e96a456e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88679243-2dad-451d-9e6a-e5d2e96a456e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68c75799-cdd9-4050-be2a-f6325a72906e' class='xr-var-data-in' type='checkbox'><label for='data-68c75799-cdd9-4050-be2a-f6325a72906e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_VDT_LSVER</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fb1d87ed-a075-40dc-840e-302704ca9d1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb1d87ed-a075-40dc-840e-302704ca9d1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f065fc6f-80f5-4de4-83b3-1f4f4f33200e' class='xr-var-data-in' type='checkbox'><label for='data-f065fc6f-80f5-4de4-83b3-1f4f4f33200e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>v tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_UDT_LSRLX</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-47e7216b-1d95-4ce4-bb6c-8285c414242a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47e7216b-1d95-4ce4-bb6c-8285c414242a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e725ad2-a3c4-4736-8e7f-40580a918525' class='xr-var-data-in' type='checkbox'><label for='data-6e725ad2-a3c4-4736-8e7f-40580a918525' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_VDT_LSRLX</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9a43da48-d43a-4d39-b33a-381261ebaa38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a43da48-d43a-4d39-b33a-381261ebaa38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f417e19-4270-4670-abb1-2c29d29c9b04' class='xr-var-data-in' type='checkbox'><label for='data-9f417e19-4270-4670-abb1-2c29d29c9b04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>v tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SWUPF</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4368afcc-a2d8-4b18-8637-f91102aad8eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4368afcc-a2d8-4b18-8637-f91102aad8eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d504d234-2c5d-44c3-9a03-ae85d9d79bf8' class='xr-var-data-in' type='checkbox'><label for='data-d504d234-2c5d-44c3-9a03-ae85d9d79bf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>SW flux upward</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SWDNF</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-934a7451-c1e9-40df-896c-b691d0216b22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-934a7451-c1e9-40df-896c-b691d0216b22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00ecb357-0cd6-495d-840a-e548dfe3686a' class='xr-var-data-in' type='checkbox'><label for='data-00ecb357-0cd6-495d-840a-e548dfe3686a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>SW flux downward</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_LWUPF</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f90bb379-8802-4f2e-8ad6-c0dbcc30c9e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f90bb379-8802-4f2e-8ad6-c0dbcc30c9e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb2f6e31-a186-43bd-ba88-f074ccf877fe' class='xr-var-data-in' type='checkbox'><label for='data-eb2f6e31-a186-43bd-ba88-f074ccf877fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>LW flux upward</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_LWDNF</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8ae66008-1401-44f3-951b-8fe8b03c61cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ae66008-1401-44f3-951b-8fe8b03c61cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-805efe25-136a-4239-9ed1-7a9fc318fb0e' class='xr-var-data-in' type='checkbox'><label for='data-805efe25-136a-4239-9ed1-7a9fc318fb0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>LW flux downward</dd><dt><span>units :</span></dt><dd>W/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_RS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a25c8ec7-b3be-4305-9af3-fe0faf17c96a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a25c8ec7-b3be-4305-9af3-fe0faf17c96a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3823f7cd-2cb5-4c02-8e0d-1234fcb5525e' class='xr-var-data-in' type='checkbox'><label for='data-3823f7cd-2cb5-4c02-8e0d-1234fcb5525e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>RS TKE</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_SH</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-02ff5c86-2967-4a83-a5e2-3726cf726809' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02ff5c86-2967-4a83-a5e2-3726cf726809' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff522a81-3de9-487b-918f-bc36b20fc1ac' class='xr-var-data-in' type='checkbox'><label for='data-ff522a81-3de9-487b-918f-bc36b20fc1ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>RS TKE shear production</dd><dt><span>units :</span></dt><dd>m^2/s^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_BU</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9c2cb8d1-9b21-4651-9c78-8a77a9d1f87a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c2cb8d1-9b21-4651-9c78-8a77a9d1f87a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6ab5851-ad5f-4305-8ba8-ac0e764b7366' class='xr-var-data-in' type='checkbox'><label for='data-c6ab5851-ad5f-4305-8ba8-ac0e764b7366' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>RS TKE buoyancy production</dd><dt><span>units :</span></dt><dd>m^2/s^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_TR</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-051b84a4-e675-43f7-b1de-3a03a45281bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-051b84a4-e675-43f7-b1de-3a03a45281bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b6d93ed-97c1-4411-8167-f85817fc7ddf' class='xr-var-data-in' type='checkbox'><label for='data-8b6d93ed-97c1-4411-8167-f85817fc7ddf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>RS TKE turbulent + pressure transport</dd><dt><span>units :</span></dt><dd>m^2/s^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_DI</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-553b4e77-bd7f-4007-9b34-21465bc454d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-553b4e77-bd7f-4007-9b34-21465bc454d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-045d5fa4-175c-453b-b2de-6294c108ad8b' class='xr-var-data-in' type='checkbox'><label for='data-045d5fa4-175c-453b-b2de-6294c108ad8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>TKE dissipation</dd><dt><span>units :</span></dt><dd>m^2/s^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TKE_SGS</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8147fc82-048f-4b41-956e-8dc9e6684d9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8147fc82-048f-4b41-956e-8dc9e6684d9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-368222c1-969b-4742-a12c-db412bc30443' class='xr-var-data-in' type='checkbox'><label for='data-368222c1-969b-4742-a12c-db412bc30443' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>SGS TKE</dd><dt><span>units :</span></dt><dd>m^2/s^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a33f56da-e24d-413c-8f9c-f451cc165616' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a33f56da-e24d-413c-8f9c-f451cc165616' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a99dd5c-a989-4958-9a16-f001b130f3ec' class='xr-var-data-in' type='checkbox'><label for='data-7a99dd5c-a989-4958-9a16-f001b130f3ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of w</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THL_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e165d055-b39f-46df-a771-8b032dd9b6b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e165d055-b39f-46df-a771-8b032dd9b6b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a23c65d3-1304-432a-af53-482afdd8abaa' class='xr-var-data-in' type='checkbox'><label for='data-a23c65d3-1304-432a-af53-482afdd8abaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of thl</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QT_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7cebc348-3ef9-4f07-86f3-de6bf5dd360b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cebc348-3ef9-4f07-86f3-de6bf5dd360b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58a2d127-6b04-49fc-b52d-c0809885d0b3' class='xr-var-data-in' type='checkbox'><label for='data-58a2d127-6b04-49fc-b52d-c0809885d0b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qt</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QV_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36a22706-8b2f-40e6-8585-4bccbcd4ba49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36a22706-8b2f-40e6-8585-4bccbcd4ba49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d77f2fd5-2445-4264-ae64-67be82ace2e6' class='xr-var-data-in' type='checkbox'><label for='data-d77f2fd5-2445-4264-ae64-67be82ace2e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qv</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QL_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-856a2168-ca16-4dd8-89c2-92e883c251b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-856a2168-ca16-4dd8-89c2-92e883c251b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a458f8db-368e-4409-93d5-c1d8840650af' class='xr-var-data-in' type='checkbox'><label for='data-a458f8db-368e-4409-93d5-c1d8840650af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of ql</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QF_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4157540a-00c9-48d8-b60e-66b26b87cb9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4157540a-00c9-48d8-b60e-66b26b87cb9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac0b49c8-ba2b-4e5f-81d5-2820373f0944' class='xr-var-data-in' type='checkbox'><label for='data-ac0b49c8-ba2b-4e5f-81d5-2820373f0944' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qf</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QC_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e0edee8f-1e39-4673-9f48-16f5950ef632' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0edee8f-1e39-4673-9f48-16f5950ef632' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b2e7f31-3339-45c0-a0f9-f4cba8227863' class='xr-var-data-in' type='checkbox'><label for='data-9b2e7f31-3339-45c0-a0f9-f4cba8227863' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qc</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QI_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f13ed360-8c8d-4770-9a16-0223e94cc46a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f13ed360-8c8d-4770-9a16-0223e94cc46a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26797fdf-d6ea-49e2-9547-411dfa0cd819' class='xr-var-data-in' type='checkbox'><label for='data-26797fdf-d6ea-49e2-9547-411dfa0cd819' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qi</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QNC_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5ecf952-daf6-4ecd-bcb1-83c25bb9ef83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5ecf952-daf6-4ecd-bcb1-83c25bb9ef83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-127dbf24-9e53-4313-85d7-aaced09cdcd9' class='xr-var-data-in' type='checkbox'><label for='data-127dbf24-9e53-4313-85d7-aaced09cdcd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of qnc</dd><dt><span>units :</span></dt><dd>cm-3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THV_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-737a2716-ea65-4a65-a055-64a57b14e54a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-737a2716-ea65-4a65-a055-64a57b14e54a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-204b071b-dcf9-406d-a4fe-29bf152a9a7f' class='xr-var-data-in' type='checkbox'><label for='data-204b071b-dcf9-406d-a4fe-29bf152a9a7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of thv</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W2_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a8d9d72a-4705-4721-ba24-12ca26a68286' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8d9d72a-4705-4721-ba24-12ca26a68286' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-844ffe51-01e3-4208-b8f5-2fb48d73e42a' class='xr-var-data-in' type='checkbox'><label for='data-844ffe51-01e3-4208-b8f5-2fb48d73e42a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudy grid points of w variance</dd><dt><span>units :</span></dt><dd>(m/s)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AW_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6922e72e-2eda-4b84-8bb3-48390759a718' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6922e72e-2eda-4b84-8bb3-48390759a718' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47e7a5f3-3110-4fd1-afc3-a8f03c671400' class='xr-var-data-in' type='checkbox'><label for='data-47e7a5f3-3110-4fd1-afc3-a8f03c671400' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of w</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWTHL_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49d61d25-e8aa-4ccf-b433-9f7e1986bfc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49d61d25-e8aa-4ccf-b433-9f7e1986bfc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc1f70ac-2459-4364-b360-cd5761eb51ad' class='xr-var-data-in' type='checkbox'><label for='data-bc1f70ac-2459-4364-b360-cd5761eb51ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wthl</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQT_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe61a56b-3cdd-4aeb-ad6b-a3a76caf3b06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe61a56b-3cdd-4aeb-ad6b-a3a76caf3b06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a91dbe82-0436-46f3-aa68-5a347edee2fb' class='xr-var-data-in' type='checkbox'><label for='data-a91dbe82-0436-46f3-aa68-5a347edee2fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wqt</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQV_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2e527a7e-bdf3-40ee-8a0a-8dfbdbeab7ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e527a7e-bdf3-40ee-8a0a-8dfbdbeab7ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cc44327-1a1d-4b1e-b3ee-b889612a030b' class='xr-var-data-in' type='checkbox'><label for='data-6cc44327-1a1d-4b1e-b3ee-b889612a030b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wqv</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQL_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0f99e4a-47e8-4a12-8c0d-029cfb7cc3f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0f99e4a-47e8-4a12-8c0d-029cfb7cc3f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b3d0d6a-e171-434b-bbbd-68b22a06e4dd' class='xr-var-data-in' type='checkbox'><label for='data-9b3d0d6a-e171-434b-bbbd-68b22a06e4dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wql</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQF_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-48dcc39a-552e-4fe1-92f8-22a103c797eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48dcc39a-552e-4fe1-92f8-22a103c797eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66f30936-69df-4643-afcb-f5a189312fe9' class='xr-var-data-in' type='checkbox'><label for='data-66f30936-69df-4643-afcb-f5a189312fe9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wqf</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQC_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-75a0783f-3b35-4737-81bb-f4bcab87776c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75a0783f-3b35-4737-81bb-f4bcab87776c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b5238f9-c17d-4e8b-a974-a0ce60a572a7' class='xr-var-data-in' type='checkbox'><label for='data-2b5238f9-c17d-4e8b-a974-a0ce60a572a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wqc</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQI_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2afe8c7d-29cd-4fc6-8d8e-bebd34d52030' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2afe8c7d-29cd-4fc6-8d8e-bebd34d52030' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bd948b8-203a-48d5-b810-78fac2c50787' class='xr-var-data-in' type='checkbox'><label for='data-7bd948b8-203a-48d5-b810-78fac2c50787' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wqi</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWTHV_C</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7722d4c5-f910-4d1c-a760-f89fafdf0224' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7722d4c5-f910-4d1c-a760-f89fafdf0224' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59618c87-84ae-49bf-a0c0-6250710058bf' class='xr-var-data-in' type='checkbox'><label for='data-59618c87-84ae-49bf-a0c0-6250710058bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloud fraction * average over all cloudy grid points of wthv</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_A_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f2ff6b52-4207-4f5c-9db3-2a9eda2b0cf3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2ff6b52-4207-4f5c-9db3-2a9eda2b0cf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69850ede-438e-4386-90f9-61082c7a1803' class='xr-var-data-in' type='checkbox'><label for='data-69850ede-438e-4386-90f9-61082c7a1803' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Fraction of cloudcore grid points</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6f2866b2-56a6-4452-8b58-df535d21c43a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f2866b2-56a6-4452-8b58-df535d21c43a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-199d92e4-2a89-4aae-9164-7f56acf81a37' class='xr-var-data-in' type='checkbox'><label for='data-199d92e4-2a89-4aae-9164-7f56acf81a37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of w</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THL_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c5eedf9-1efa-4c7f-a9b0-bfe57166ed87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c5eedf9-1efa-4c7f-a9b0-bfe57166ed87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f2250ae-5a48-41f6-858b-4b116544390b' class='xr-var-data-in' type='checkbox'><label for='data-3f2250ae-5a48-41f6-858b-4b116544390b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of thl</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QT_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6be9b4d6-0e02-4411-82d6-b2d7a55859bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6be9b4d6-0e02-4411-82d6-b2d7a55859bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77486d26-337c-4958-87ed-5ee85dfefd58' class='xr-var-data-in' type='checkbox'><label for='data-77486d26-337c-4958-87ed-5ee85dfefd58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of qt</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QV_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1c6b2a1d-e946-41db-afa3-27f8cffc1e7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c6b2a1d-e946-41db-afa3-27f8cffc1e7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1b82825-948b-49fd-b80c-a7b0d8d2775d' class='xr-var-data-in' type='checkbox'><label for='data-a1b82825-948b-49fd-b80c-a7b0d8d2775d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of qv</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QL_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7e93ecb7-3394-4ba9-86a1-119b6f1c6df4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e93ecb7-3394-4ba9-86a1-119b6f1c6df4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21e50466-36fb-4966-af97-b2a155397f44' class='xr-var-data-in' type='checkbox'><label for='data-21e50466-36fb-4966-af97-b2a155397f44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of ql</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QF_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b6b020da-4d69-452f-bc26-ce40dc05116a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6b020da-4d69-452f-bc26-ce40dc05116a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab15984d-82bc-44a7-a24b-ec4fd29ca0ed' class='xr-var-data-in' type='checkbox'><label for='data-ab15984d-82bc-44a7-a24b-ec4fd29ca0ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of qf</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QC_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0c62e5f2-1a08-41ab-9e59-0be8859b12a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c62e5f2-1a08-41ab-9e59-0be8859b12a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2d6b988-45da-452d-b304-631ec2fd15f9' class='xr-var-data-in' type='checkbox'><label for='data-e2d6b988-45da-452d-b304-631ec2fd15f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of qc</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QI_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aab068af-db61-49db-ae56-e7dfb130eb1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aab068af-db61-49db-ae56-e7dfb130eb1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff3df730-bfd1-4299-b5f2-8820239d5803' class='xr-var-data-in' type='checkbox'><label for='data-ff3df730-bfd1-4299-b5f2-8820239d5803' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of qi</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THV_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e0354f6c-309e-43ca-a96a-398eda1422c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0354f6c-309e-43ca-a96a-398eda1422c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-add0b73f-16b2-41fe-88d6-a1e759c00596' class='xr-var-data-in' type='checkbox'><label for='data-add0b73f-16b2-41fe-88d6-a1e759c00596' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of thv</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_W2_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4d773cfd-1df4-4292-bd20-4740d9c02440' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d773cfd-1df4-4292-bd20-4740d9c02440' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c754aa26-09ce-46ee-a6dc-96b4b2c13999' class='xr-var-data-in' type='checkbox'><label for='data-c754aa26-09ce-46ee-a6dc-96b4b2c13999' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Average over all cloudcore grid points of w variance</dd><dt><span>units :</span></dt><dd>(m/s)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AW_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71403d91-7ee5-4b20-b254-a79c9f5a9ad2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71403d91-7ee5-4b20-b254-a79c9f5a9ad2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49c365e1-96de-496f-9abe-122cb8f0137b' class='xr-var-data-in' type='checkbox'><label for='data-49c365e1-96de-496f-9abe-122cb8f0137b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of w</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWTHL_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-51cd987b-a09a-426a-81aa-1c4dbe16ffca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51cd987b-a09a-426a-81aa-1c4dbe16ffca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3d0f682-81b1-49bf-b645-d73132037ff8' class='xr-var-data-in' type='checkbox'><label for='data-d3d0f682-81b1-49bf-b645-d73132037ff8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wthl</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQT_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6437f1c6-141a-424d-900f-e0432c39e36f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6437f1c6-141a-424d-900f-e0432c39e36f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10525604-c6e8-4457-bd65-3ec18e302180' class='xr-var-data-in' type='checkbox'><label for='data-10525604-c6e8-4457-bd65-3ec18e302180' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wqt</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQV_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32aeaa14-34c6-4fa8-8832-5f954a0b92d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32aeaa14-34c6-4fa8-8832-5f954a0b92d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbe42d50-722d-4579-ba2d-1c543c7c9bc2' class='xr-var-data-in' type='checkbox'><label for='data-cbe42d50-722d-4579-ba2d-1c543c7c9bc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wqv</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQL_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d3d535d-a1d4-4aa0-b2bd-00b63436a379' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d3d535d-a1d4-4aa0-b2bd-00b63436a379' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94c20d09-ac93-4234-a809-643a9cb2492d' class='xr-var-data-in' type='checkbox'><label for='data-94c20d09-ac93-4234-a809-643a9cb2492d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wql</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQF_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed6050bd-40d5-4147-89e3-21face3f4959' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed6050bd-40d5-4147-89e3-21face3f4959' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b356f79-cc25-49b0-9dec-2344902cd162' class='xr-var-data-in' type='checkbox'><label for='data-5b356f79-cc25-49b0-9dec-2344902cd162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wqf</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQC_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c7d0d201-59f6-428e-bf43-22afb440b87c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7d0d201-59f6-428e-bf43-22afb440b87c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d7cd723-5be8-41e3-bdc0-db58e25034ca' class='xr-var-data-in' type='checkbox'><label for='data-5d7cd723-5be8-41e3-bdc0-db58e25034ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wqc</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWQI_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36f6a832-642c-4cc4-9b6a-87f094c55296' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36f6a832-642c-4cc4-9b6a-87f094c55296' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-165469ce-1967-4536-9c08-8fdee3f6695a' class='xr-var-data-in' type='checkbox'><label for='data-165469ce-1967-4536-9c08-8fdee3f6695a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wqi</dd><dt><span>units :</span></dt><dd>kg/kg m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_AWTHV_CC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e1df2570-9533-4d1e-be60-6adf10440c0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1df2570-9533-4d1e-be60-6adf10440c0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29b7a996-445c-4657-bc43-3a6e699c3b4f' class='xr-var-data-in' type='checkbox'><label for='data-29b7a996-445c-4657-bc43-3a6e699c3b4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Cloudcore fraction * average over all cloudcore grid points of wthv</dd><dt><span>units :</span></dt><dd>K m/s</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_THL</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b83b67fd-3e27-43a5-b500-61c669667340' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b83b67fd-3e27-43a5-b500-61c669667340' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a8b954e-5c9f-4f91-9f25-dbc55c05ea8e' class='xr-var-data-in' type='checkbox'><label for='data-8a8b954e-5c9f-4f91-9f25-dbc55c05ea8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of thl</dd><dt><span>units :</span></dt><dd>K^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_QT</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bc8a79a3-7c9f-46e0-a92b-581f28f2ea43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc8a79a3-7c9f-46e0-a92b-581f28f2ea43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcb24781-60fd-4ce0-9dff-ac7f8fa38a5d' class='xr-var-data-in' type='checkbox'><label for='data-bcb24781-60fd-4ce0-9dff-ac7f8fa38a5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of qt</dd><dt><span>units :</span></dt><dd>(kg/kg)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_QL</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5fee9c43-c6f9-43c8-a5c8-bc50ca1ac30a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5fee9c43-c6f9-43c8-a5c8-bc50ca1ac30a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84329cab-3e81-44e6-aca4-7eddcd033c3c' class='xr-var-data-in' type='checkbox'><label for='data-84329cab-3e81-44e6-aca4-7eddcd033c3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of ql</dd><dt><span>units :</span></dt><dd>(kg/kg)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_QF</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3b1616dd-cbac-4421-8808-6216ad8f2199' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b1616dd-cbac-4421-8808-6216ad8f2199' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b03896e-f504-4b98-a19a-3a1390744073' class='xr-var-data-in' type='checkbox'><label for='data-0b03896e-f504-4b98-a19a-3a1390744073' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of qf</dd><dt><span>units :</span></dt><dd>(kg/kg)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_QC</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-111e9169-4b34-4735-b1f1-aad79913abd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-111e9169-4b34-4735-b1f1-aad79913abd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10866c25-a863-4e40-b2d5-294663faca67' class='xr-var-data-in' type='checkbox'><label for='data-10866c25-a863-4e40-b2d5-294663faca67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of qc</dd><dt><span>units :</span></dt><dd>(kg/kg)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_QI</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-40f7ef88-6167-4324-8865-ee5690cb274a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40f7ef88-6167-4324-8865-ee5690cb274a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2fffa67-f868-4ff4-8a88-29e8a0fe7f30' class='xr-var-data-in' type='checkbox'><label for='data-f2fffa67-f868-4ff4-8a88-29e8a0fe7f30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of qi</dd><dt><span>units :</span></dt><dd>(kg/kg)^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SIGC_THV</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0e39471e-039c-4ae8-9a4f-889ed3e95b76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e39471e-039c-4ae8-9a4f-889ed3e95b76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebd9f12b-a6ff-4e3f-b605-38bee7b3c4b2' class='xr-var-data-in' type='checkbox'><label for='data-ebd9f12b-a6ff-4e3f-b605-38bee7b3c4b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Incloud variance of thv</dd><dt><span>units :</span></dt><dd>K^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_TH2</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7e20e1c6-e53b-4aa5-b774-d4bf2dc66b1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e20e1c6-e53b-4aa5-b774-d4bf2dc66b1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eac5c05-8a54-45ae-9c70-3c07d1934b11' class='xr-var-data-in' type='checkbox'><label for='data-5eac5c05-8a54-45ae-9c70-3c07d1934b11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Variance of th</dd><dt><span>units :</span></dt><dd>K^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THV2</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ba1610ab-2700-4ae8-8c2d-f35942adf850' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba1610ab-2700-4ae8-8c2d-f35942adf850' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e3a66d7-682d-4eea-aef2-8300d7c729ad' class='xr-var-data-in' type='checkbox'><label for='data-4e3a66d7-682d-4eea-aef2-8300d7c729ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Variance of thv</dd><dt><span>units :</span></dt><dd>K^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_THL2</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d5f8e51d-8077-4b6f-9198-0b30e9e97c1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5f8e51d-8077-4b6f-9198-0b30e9e97c1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa220501-5bd1-446e-bf14-d85a3ba8858e' class='xr-var-data-in' type='checkbox'><label for='data-aa220501-5bd1-446e-bf14-d85a3ba8858e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Variance of thl</dd><dt><span>units :</span></dt><dd>K^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_QV2</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e8e7f4d1-6bc8-4bc4-922e-6ff258c9417e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8e7f4d1-6bc8-4bc4-922e-6ff258c9417e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ac0010c-6ed1-4d8c-906d-398b439880a8' class='xr-var-data-in' type='checkbox'><label for='data-5ac0010c-6ed1-4d8c-906d-398b439880a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Variance of qv</dd><dt><span>units :</span></dt><dd>kg^2/kg^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_SMAXACTMAX</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e3bf7652-410a-4e4d-ac62-27afccc14282' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3bf7652-410a-4e4d-ac62-27afccc14282' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be7b6946-900e-42eb-806e-868b04c977ed' class='xr-var-data-in' type='checkbox'><label for='data-be7b6946-900e-42eb-806e-868b04c977ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Max of max supersat in Morrison microphysics</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_RMINACTMIN</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f35b6c66-c7fc-4468-a01d-d1372776b848' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f35b6c66-c7fc-4468-a01d-d1372776b848' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dccb071-083d-427d-9655-f4e62e7ff0ea' class='xr-var-data-in' type='checkbox'><label for='data-2dccb071-083d-427d-9655-f4e62e7ff0ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Min of min activated radius in Morrison microphysics</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WMAX</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3adfa6cb-67f1-4a86-95e3-f180cebdf990' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3adfa6cb-67f1-4a86-95e3-f180cebdf990' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-179d1a3f-8a10-470b-bb69-5a68b9cc9279' class='xr-var-data-in' type='checkbox'><label for='data-179d1a3f-8a10-470b-bb69-5a68b9cc9279' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Max value of vertical motion</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSP_WMIN</span></div><div class='xr-var-dims'>(Time, bottom_top)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c37fda47-e6c2-4450-aa25-0ebfea17ceb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c37fda47-e6c2-4450-aa25-0ebfea17ceb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6e2e6bf-7cd2-436d-a479-d1310fbd98d4' class='xr-var-data-in' type='checkbox'><label for='data-f6e2e6bf-7cd2-436d-a479-d1310fbd98d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Min value of vertical motion</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[20566 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_TH</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-433d8be4-ac27-4070-a1a8-4e65410116c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-433d8be4-ac27-4070-a1a8-4e65410116c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcd6479f-ca2c-4418-af1c-3324c522b6b3' class='xr-var-data-in' type='checkbox'><label for='data-dcd6479f-ca2c-4418-af1c-3324c522b6b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged potential temperature</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_U</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north, west_east_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6544e608-d8b5-4c5a-832a-fcafe4c16e11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6544e608-d8b5-4c5a-832a-fcafe4c16e11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59ffcf7f-841a-48e4-a7b3-8be1c2c41250' class='xr-var-data-in' type='checkbox'><label for='data-59ffcf7f-841a-48e4-a7b3-8be1c2c41250' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged meridional wind speed</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>[1290516500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_V</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north_stag, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c287d150-f306-4431-9682-8aafdbf97ab1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c287d150-f306-4431-9682-8aafdbf97ab1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70b80be9-f260-43be-b30f-ad0f93473122' class='xr-var-data-in' type='checkbox'><label for='data-70b80be9-f260-43be-b30f-ad0f93473122' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged zonal wind speed</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>[1290516500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_W</span></div><div class='xr-var-dims'>(Time, bottom_top_stag, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f82c07d7-36e3-45c6-b3ca-dff5fb355e5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f82c07d7-36e3-45c6-b3ca-dff5fb355e5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de7ebe82-ddb4-434f-9418-1d3f579f5342' class='xr-var-data-in' type='checkbox'><label for='data-de7ebe82-ddb4-434f-9418-1d3f579f5342' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged vertical wind speed</dd><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[1291062500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_W2</span></div><div class='xr-var-dims'>(Time, bottom_top_stag, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad0a8f06-6cec-4234-a3ce-8c6335e317c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad0a8f06-6cec-4234-a3ce-8c6335e317c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-628991ef-d92a-4562-a13b-2319799ee3af' class='xr-var-data-in' type='checkbox'><label for='data-628991ef-d92a-4562-a13b-2319799ee3af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged vertical wind speed variance</dd><dt><span>units :</span></dt><dd>m^2 s^-2</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>[1291062500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QV</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bdcb9b07-b73f-4abf-8176-249246d8cad7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdcb9b07-b73f-4abf-8176-249246d8cad7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc83b9bc-02a7-40e1-a07a-14cd632dcbc3' class='xr-var-data-in' type='checkbox'><label for='data-dc83b9bc-02a7-40e1-a07a-14cd632dcbc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged water vapor mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QC</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c6572bb-4292-4ac4-b03d-eda84c00eb2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c6572bb-4292-4ac4-b03d-eda84c00eb2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-101a8584-90b0-4ecd-a3e1-82a4df1ac7ac' class='xr-var-data-in' type='checkbox'><label for='data-101a8584-90b0-4ecd-a3e1-82a4df1ac7ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged cloud droplet mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QR</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-40afd2cb-24a1-4e98-b0ff-6fb10675abd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40afd2cb-24a1-4e98-b0ff-6fb10675abd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f45e3ffc-508d-4514-9287-6c7580356080' class='xr-var-data-in' type='checkbox'><label for='data-f45e3ffc-508d-4514-9287-6c7580356080' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged rain droplet mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QI</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e91325c9-06b1-43c3-a1bb-75823bc10b4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e91325c9-06b1-43c3-a1bb-75823bc10b4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2a5665e-81fc-46d1-96d3-097ddc52372d' class='xr-var-data-in' type='checkbox'><label for='data-c2a5665e-81fc-46d1-96d3-097ddc52372d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged cloud ice mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QS</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad93c646-87a5-44f4-adbb-1c5effec6aba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad93c646-87a5-44f4-adbb-1c5effec6aba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ca586d1-fbda-4665-8a83-2e85f0f8371d' class='xr-var-data-in' type='checkbox'><label for='data-0ca586d1-fbda-4665-8a83-2e85f0f8371d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged snow mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_QG</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-781d3361-167d-426c-b0db-25cdf24317e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-781d3361-167d-426c-b0db-25cdf24317e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c063bb14-b904-46d0-831b-6682e3a5dd64' class='xr-var-data-in' type='checkbox'><label for='data-c063bb14-b904-46d0-831b-6682e3a5dd64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged graupel mixing ratio</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_LWC</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ab5cd2fc-754e-4960-b42f-acbccf0fbd21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab5cd2fc-754e-4960-b42f-acbccf0fbd21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d7c413e-5aed-4d02-8217-1d1cf8e9f52b' class='xr-var-data-in' type='checkbox'><label for='data-8d7c413e-5aed-4d02-8217-1d1cf8e9f52b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged liquid water content (based on ql)</dd><dt><span>units :</span></dt><dd>kg/m^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_IWC</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed2136bd-77ec-44db-aeca-a1791c180004' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed2136bd-77ec-44db-aeca-a1791c180004' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-993bc69a-9bae-4fb0-a252-881e81e3cc80' class='xr-var-data-in' type='checkbox'><label for='data-993bc69a-9bae-4fb0-a252-881e81e3cc80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged ice water content (based on qf)</dd><dt><span>units :</span></dt><dd>kg/m^3</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSV_CLDFRAC</span></div><div class='xr-var-dims'>(Time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea7cc51b-6d61-4c93-a515-26ec9209e3be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea7cc51b-6d61-4c93-a515-26ec9209e3be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e63c54f4-8ae9-419f-896e-fb00e7c6eb3f' class='xr-var-data-in' type='checkbox'><label for='data-e63c54f4-8ae9-419f-896e-fb00e7c6eb3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Time-averaged cloud fraction</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1285375000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSS_LWP</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3991bb10-cc3f-4862-9bd0-a785c3a2c133' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3991bb10-cc3f-4862-9bd0-a785c3a2c133' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55323c9c-b23a-4a48-a9ea-25b6a0872956' class='xr-var-data-in' type='checkbox'><label for='data-55323c9c-b23a-4a48-a9ea-25b6a0872956' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Time-averaged liquid water path (based on ql)</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[5687500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSS_IWP</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9652714f-8475-4fd8-a79a-033ccc9e07bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9652714f-8475-4fd8-a79a-033ccc9e07bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba802dea-1767-4adb-b491-ce7660f73b15' class='xr-var-data-in' type='checkbox'><label for='data-ba802dea-1767-4adb-b491-ce7660f73b15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Time-averaged ice water path (based on qf)</dd><dt><span>units :</span></dt><dd>kg/m^2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[5687500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSS_CLDTOT</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cfeec107-dfb7-4d7b-9a38-3e52caede178' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfeec107-dfb7-4d7b-9a38-3e52caede178' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3615d168-4559-4b67-b449-68144acf8a7c' class='xr-var-data-in' type='checkbox'><label for='data-3615d168-4559-4b67-b449-68144acf8a7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Time-averaged fractional cloud cover</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[5687500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CSS_CLDLOW</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-82e290f8-1a1c-4000-abd6-eaadc5ad3072' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82e290f8-1a1c-4000-abd6-eaadc5ad3072' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cf63252-d404-46b4-8e97-40a21d95fa4d' class='xr-var-data-in' type='checkbox'><label for='data-0cf63252-d404-46b4-8e97-40a21d95fa4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Time-averaged fractional low-cloud cover (&lt;5 km)</dd><dt><span>units :</span></dt><dd>(0-1)</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[5687500 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2bb85bb7-a1af-4086-a4d0-234a17b05cb5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2bb85bb7-a1af-4086-a4d0-234a17b05cb5' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-77a2bb85-ccb6-497f-81fd-f85282eaaa10' class='xr-section-summary-in' type='checkbox'  ><label for='section-77a2bb85-ccb6-497f-81fd-f85282eaaa10' class='xr-section-summary' >Attributes: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TITLE :</span></dt><dd> OUTPUT FROM WRF V3.8.1 MODEL</dd><dt><span>START_DATE :</span></dt><dd>2019-04-04_12:00:00</dd><dt><span>WEST-EAST_GRID_DIMENSION :</span></dt><dd>251</dd><dt><span>SOUTH-NORTH_GRID_DIMENSION :</span></dt><dd>251</dd><dt><span>BOTTOM-TOP_GRID_DIMENSION :</span></dt><dd>227</dd><dt><span>DX :</span></dt><dd>100.0</dd><dt><span>DY :</span></dt><dd>100.0</dd><dt><span>GRIDTYPE :</span></dt><dd>C</dd><dt><span>DIFF_OPT :</span></dt><dd>2</dd><dt><span>KM_OPT :</span></dt><dd>2</dd><dt><span>DAMP_OPT :</span></dt><dd>3</dd><dt><span>DAMPCOEF :</span></dt><dd>0.2</dd><dt><span>KHDIF :</span></dt><dd>1.0</dd><dt><span>KVDIF :</span></dt><dd>1.0</dd><dt><span>MP_PHYSICS :</span></dt><dd>8</dd><dt><span>RA_LW_PHYSICS :</span></dt><dd>4</dd><dt><span>RA_SW_PHYSICS :</span></dt><dd>4</dd><dt><span>SF_SFCLAY_PHYSICS :</span></dt><dd>1</dd><dt><span>SF_SURFACE_PHYSICS :</span></dt><dd>1</dd><dt><span>BL_PBL_PHYSICS :</span></dt><dd>0</dd><dt><span>CU_PHYSICS :</span></dt><dd>0</dd><dt><span>SF_LAKE_PHYSICS :</span></dt><dd>0</dd><dt><span>SURFACE_INPUT_SOURCE :</span></dt><dd>3</dd><dt><span>SST_UPDATE :</span></dt><dd>0</dd><dt><span>GRID_FDDA :</span></dt><dd>0</dd><dt><span>GFDDA_INTERVAL_M :</span></dt><dd>0</dd><dt><span>GFDDA_END_H :</span></dt><dd>0</dd><dt><span>GRID_SFDDA :</span></dt><dd>0</dd><dt><span>SGFDDA_INTERVAL_M :</span></dt><dd>0</dd><dt><span>SGFDDA_END_H :</span></dt><dd>0</dd><dt><span>HYPSOMETRIC_OPT :</span></dt><dd>1</dd><dt><span>USE_THETA_M :</span></dt><dd>1</dd><dt><span>WEST-EAST_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>WEST-EAST_PATCH_END_UNSTAG :</span></dt><dd>250</dd><dt><span>WEST-EAST_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>WEST-EAST_PATCH_END_STAG :</span></dt><dd>251</dd><dt><span>SOUTH-NORTH_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>SOUTH-NORTH_PATCH_END_UNSTAG :</span></dt><dd>250</dd><dt><span>SOUTH-NORTH_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>SOUTH-NORTH_PATCH_END_STAG :</span></dt><dd>251</dd><dt><span>BOTTOM-TOP_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>BOTTOM-TOP_PATCH_END_UNSTAG :</span></dt><dd>226</dd><dt><span>BOTTOM-TOP_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>BOTTOM-TOP_PATCH_END_STAG :</span></dt><dd>227</dd><dt><span>GRID_ID :</span></dt><dd>1</dd><dt><span>PARENT_ID :</span></dt><dd>0</dd><dt><span>I_PARENT_START :</span></dt><dd>0</dd><dt><span>J_PARENT_START :</span></dt><dd>0</dd><dt><span>PARENT_GRID_RATIO :</span></dt><dd>1</dd><dt><span>DT :</span></dt><dd>0.5</dd><dt><span>CEN_LAT :</span></dt><dd>0.0</dd><dt><span>CEN_LON :</span></dt><dd>0.0</dd><dt><span>TRUELAT1 :</span></dt><dd>0.0</dd><dt><span>TRUELAT2 :</span></dt><dd>0.0</dd><dt><span>MOAD_CEN_LAT :</span></dt><dd>0.0</dd><dt><span>STAND_LON :</span></dt><dd>0.0</dd><dt><span>POLE_LAT :</span></dt><dd>0.0</dd><dt><span>POLE_LON :</span></dt><dd>0.0</dd><dt><span>GMT :</span></dt><dd>0.0</dd><dt><span>JULYR :</span></dt><dd>0</dd><dt><span>JULDAY :</span></dt><dd>1</dd><dt><span>MAP_PROJ :</span></dt><dd>0</dd><dt><span>MAP_PROJ_CHAR :</span></dt><dd>Cartesian</dd><dt><span>MMINLU :</span></dt><dd></dd><dt><span>NUM_LAND_CAT :</span></dt><dd>21</dd><dt><span>ISWATER :</span></dt><dd>16</dd><dt><span>ISLAKE :</span></dt><dd>0</dd><dt><span>ISICE :</span></dt><dd>0</dd><dt><span>ISURBAN :</span></dt><dd>0</dd><dt><span>ISOILWATER :</span></dt><dd>0</dd><dt><span>doi :</span></dt><dd>10.5439/1342961</dd><dt><span>contacts :</span></dt><dd>lasso@arm.gov, LASSO PI: William Gustafson (William.Gustafson@pnnl.gov), LASSO Co-PI: Andrew Vogelmann (vogelmann@bnl.gov)</dd><dt><span>site_id :</span></dt><dd>sgp</dd><dt><span>facility_id :</span></dt><dd>C1</dd><dt><span>location_description :</span></dt><dd>Southern Great Plains (SGP), Lamont, Oklahoma</dd><dt><span>date :</span></dt><dd>20190404</dd><dt><span>simulation_id_number :</span></dt><dd>4</dd><dt><span>model_type :</span></dt><dd>WRF</dd><dt><span>model_version :</span></dt><dd>3.8.1</dd><dt><span>model_github_hash :</span></dt><dd>b6b6a5cc4229eec1ea9b005746b5ebef2205fb07</dd><dt><span>output_domain_size :</span></dt><dd>25.0 km</dd><dt><span>output_number_of_levels :</span></dt><dd>226</dd><dt><span>output_horizontal_grid_spacing :</span></dt><dd>100 m</dd><dt><span>config_large_scale_forcing :</span></dt><dd>ECMWF</dd><dt><span>config_large_scale_forcing_scale :</span></dt><dd>114 km</dd><dt><span>config_large_scale_forcing_specifics :</span></dt><dd>sgpecmwfvarX1.c1,sgpecmwftenX1.c1,sgpecmwfsfc1lX1.c1,sgpecmwfsfceX1.c1 (v20191206)</dd><dt><span>config_surface_treatment :</span></dt><dd>VARANAL</dd><dt><span>config_surface_treatment_specifics :</span></dt><dd>sgp60varanarapC1.c1 (v20191126)</dd><dt><span>config_initial_condition :</span></dt><dd>Sounding</dd><dt><span>config_initial_condition_specifics :</span></dt><dd>sgpsondewnpnC1</dd><dt><span>config_aerosol :</span></dt><dd>NA</dd><dt><span>config_forecast_time :</span></dt><dd>15.0 h</dd><dt><span>config_boundary_method :</span></dt><dd>Periodic</dd><dt><span>config_microphysics :</span></dt><dd>Thompson (mp_physics=8)</dd><dt><span>config_nickname :</span></dt><dd>runlas20190404v1addhm</dd><dt><span>simulation_origin_host :</span></dt><dd>cumulus-login2.ccs.ornl.gov</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 72GB\n",
+       "Dimensions:         (Time: 91, bottom_top: 226, bottom_top_stag: 227,\n",
+       "                     south_north: 250, west_east: 250, west_east_stag: 251,\n",
+       "                     south_north_stag: 251)\n",
+       "Coordinates:\n",
+       "    XTIME           (Time) datetime64[ns] 728B ...\n",
+       "Dimensions without coordinates: Time, bottom_top, bottom_top_stag, south_north,\n",
+       "                                west_east, west_east_stag, south_north_stag\n",
+       "Data variables: (12/179)\n",
+       "    Times           (Time) |S19 2kB ...\n",
+       "    CST_CLDLOW      (Time) float32 364B ...\n",
+       "    CST_CLDTOT      (Time) float32 364B ...\n",
+       "    CST_LWP         (Time) float32 364B ...\n",
+       "    CST_IWP         (Time) float32 364B ...\n",
+       "    CST_PRECW       (Time) float32 364B ...\n",
+       "    ...              ...\n",
+       "    CSV_IWC         (Time, bottom_top, south_north, west_east) float32 5GB ...\n",
+       "    CSV_CLDFRAC     (Time, bottom_top, south_north, west_east) float32 5GB ...\n",
+       "    CSS_LWP         (Time, south_north, west_east) float32 23MB ...\n",
+       "    CSS_IWP         (Time, south_north, west_east) float32 23MB ...\n",
+       "    CSS_CLDTOT      (Time, south_north, west_east) float32 23MB ...\n",
+       "    CSS_CLDLOW      (Time, south_north, west_east) float32 23MB ...\n",
+       "Attributes: (12/96)\n",
+       "    TITLE:                                  OUTPUT FROM WRF V3.8.1 MODEL\n",
+       "    START_DATE:                            2019-04-04_12:00:00\n",
+       "    WEST-EAST_GRID_DIMENSION:              251\n",
+       "    SOUTH-NORTH_GRID_DIMENSION:            251\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:             227\n",
+       "    DX:                                    100.0\n",
+       "    ...                                    ...\n",
+       "    config_aerosol:                        NA\n",
+       "    config_forecast_time:                  15.0 h\n",
+       "    config_boundary_method:                Periodic\n",
+       "    config_microphysics:                   Thompson (mp_physics=8)\n",
+       "    config_nickname:                       runlas20190404v1addhm\n",
+       "    simulation_origin_host:                cumulus-login2.ccs.ornl.gov"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Plotting wrfstat variables...\n",
+    "\n",
+    "# path_shcu_root = \"/gpfs/wolf2/arm/atm124/world-shared/arm-summer-school-2024/lasso_tutorial/ShCu/untar/\"  # on cumulus\n",
+    "path_shcu_root = \"/data/project/ARM_Summer_School_2024_Data/lasso_tutorial/ShCu/untar\"  # on Jupyter\n",
+    "\n",
+    "case_date = datetime(2019, 4, 4)\n",
+    "sim_id = 4\n",
+    "\n",
+    "ds_stat = xr.open_dataset(f\"{path_shcu_root}/{case_date:%Y%m%d}/sim{sim_id:04d}/raw_model/wrfstat_d01_{case_date:%Y-%m-%d_12:00:00}.nc\")\n",
+    "ds_stat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be8778ae-33e6-4491-a298-849e990f84de",
+   "metadata": {},
+   "source": [
+    "# Plot Variables and Modify as Desired"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "045f44de-ec20-4788-ad88-8d4860c7fae1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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wpS99CW3b4sQTT8zu736/j+OOOw4XX3zxzR73kEMOwT/+4z/uUhvvete77tJ6ZH/zN3+Dk08+GU95ylPwkpe8ZOb32/oavPjFL8ZnP/tZnHjiiXjb296GhYUF/Mmf/Am+8Y1vAEhjgfd+JkNURg53pV1t2+I5z3kOnva0p+FRj3rUzbbrthh3zj77bHz/+9/f7Yy2zvZf2+8doLe97W34+7//e3z84x/Haaedhuc85zm47LLL9trL6KSTTsKv//qv401vehOuu+46PPvZz77Z9fv9/m7jzmRHH330LoW0dxV/B4Af/vCH+I3f+A0URYGvfOUrMzOpAw44AJdddtnMduvr66jreu7M65Zs27Zt+OIXv4hnPetZeOADH8jHOfvss3HSSScxH+DKK6/EUUcdlW178cUX4/jjj9/ldn3rW9/Cu971Lnz605/GeDxmqMQ5h7ZtsX37dgwGA/R6PTzsYQ/LZszPetazcN555+GAAw4AMH82euONN2Z9QDO++973vti+fTsA8DFXVlbQ6/WwtLS02312a+zcc88FgLnw187sgAMO2Ol5AmlWfUt9opTC5s2b95tj7Q2bvverqrrZ5XQf/PSnPwUA3O9+95u731t6fquqwr3vfe9dauPuwL1f+tKX8KQnPQmPeMQj8JGPfGTmRb+r12t37GEPexg+8IEP4NRTT8Wd7nQnAGGc+8M//EO8+tWv5rHggx/84AzvhsbCXW3XOeecg//6r//CX/7lX/KzSVG58XiM7du3Y2lpCcaYGXj+Ax/4AJ797GfjgAMOwHg8xsbGBobD4czxjj32WADB2X3961+PP/qjP0JVVXw8oihs374dvV7vZz5J6mz3bL92gK644gq8/vWvx4knnoinPe1pOOKII/DgBz8Yr3nNa3D22WfvlWM++MEPxl3velecccYZeMQjHoHDDjtsrxwHCNGjH/7wh7e43mmnnbZTnRhpP/zhD3H88cfDe49LLrkEd7jDHWbWuec974mPf/zjuOaaazK+DektSU2L3bH73e9+uOKKK3DllVdifX0dd77znXHppZcCAB760IcCmD/TpRntrrbriiuugPceJ5xwwkwbrr76amzZsgVvf/vbccopp+DP//zPsbq6yr9v27Yt29d3vvOdmVD2d77znawPLr/8clx++eU4//zzZ453pzvdCfe6173mOm63tdV1jb/4i7/Ascceu8svSyD068c+9jG0bZvNsKf79U53uhMGg8Fc3a3vfOc7+JVf+ZW5Uaef1bH2J6P76pOf/CSOOOKI3d5+3sRgZ0YThluyL33pS3jiE5+I4447Dp/61KfYaZN2z3vec6fXALj1Y8GznvUsPOMZz8D3v/99lGWJX/mVX8Fb3vIWKKXwkIc8BADw+Mc/fqdRr11t13e/+13s2LEDd77znWfWfd3rXofXve51+Od//mfc+973njkW9Tdxf77zne/gAQ94AP9+zTXX4Prrr+dj/dd//RdGoxF+//d/H7//+78/c7wtW7bg93//93HOOefcbN90tn/ZfusAtW2LZz3rWdi2bRve8Y53AAAe+MAH4mUvexnOPvtsPPnJT95rUNhrX/tafPKTn8TJJ5+8V/ZPdltCYFdddRWOP/54WGtxySWX7HQgfsITnoDXvva1+OAHP4j/7//7/3j5eeedh8FggEc/+tG7fgJzjAQgvff4P//n/+CQQw7hrKCqqnYaLdvVdj360Y+eCyn8r//1v3DUUUfhLW95C2dH7QwuOPTQQ3H/+98fH/7wh/Hyl7+cZ9X/8A//gO9973s45ZRTeN15xzrvvPPwwQ9+EBdccEGW7bI37a/+6q9w/fXX7zYf7IQTTsB73/tefOpTn8LTnvY0Xv7BD34QhxxyCA/6RVHg8Y9/PD796U/jzDPP5KjWVVddhYsvvhh/8Ad/sF8da3+yRz3qUSiKAv/5n/85l5t1S3ZbQ2AXXnghnvjEJ+LXf/3XccEFF8wlJQPher3oRS/CN7/5Tb42bdviwx/+MB7wgAfsNvQurSgKhgp37NiB97znPXjCE57A49IBBxzAkcBb265XvvKVMxH6a665Bk9/+tPxwhe+EE972tN4LNjZuPPoRz8a/X4f5513XuYAnXfeeVBK4YlPfCIA4N73vvfcseCUU07Bjh078IEPfGDuhLOz/dx+Jsn3u2BnnHGGB+D/5m/+Jls+Go38Xe96V3+Xu9zFb2xszGx3a3WAbkmnYZ72xc0JIZLo3b6wn/70p/6Od7yj7/V6/sMf/vBMO66++upsfRIcPOuss/wll1ziX/3qV88VHFxfX/fnn3++P//88/2pp57qAfjTTz/dn3/++f4LX/hCtu6rX/1q/7GPfcxfcskl/kMf+pA//vjj/WAw8F/96ld3+Tx2tV3zbHd1gC6++GJfFIU/4YQT/EUXXeQ/8pGP+MMOO+xmhRDJbuke25X2fOELX/Dnn3++f//73+8B+N/+7d/mvl5fX59Z/9GPfrQfDAY71b/y3vvf/M3f9MaYmeWPeMQj/JYtW/x73vMe/9WvftU/73nP8wD8hz/84Wy9f/3Xf/WLi4v+oQ99qP/CF77gP/3pT/tjjjlmrjjhvjzWcccdt1PdmZ3ZzemzHHfccf64447j/+9sDLj44os9AH/++ednyz/wgQ94AP4f//Efedmb3/xmXxSFf8ELXuA/85nP+EsuucR/4hOf8KeeeirrmO0L+9rXvuYHg4E/8sgj/Ve/+tWZsUDqLo3HY3+Pe9zDH3bYYf4jH/mIv+iii/wJJ5wwV3Dwyiuv5Pvz0Y9+NPfL+eefn/XDT3/6U/+KV7zCf/azn/Vf/epX/bve9S5/5JFH+jve8Y7+Rz/60S6dw+60a9puje4OCSG++tWv9pdccok/66yzfK/Xu1khRLJbqwP0hje8wRtjsvO55JJLvDFmRrDSGON/8zd/c5fPp7Ndt/3SAbrssst8WZY7vQH//u//3mut/R/8wR/M/LavHSAAO/2TInt702ig3tnftMheXdf+tNNO84cffrivqsrf5S538X/8x388s1/qm3l/RxxxRLbu//7f/5v3t23bNv/kJz/Zf/vb396t89jVds2z3XWAvPf+wgsv9A984AN9v9/3W7du9SeeeKL/6U9/eovb3RYO0BFHHLHTvv3BD36QrXvVVVd5rbU/8cQTb7ZdO3MUVldX/Utf+lJ/8MEH+6qq/K/+6q/6j33sY3P38f/+3//zD3vYw/xwOPTLy8v+iU98ov+P//iPn+mxjj32WH/wwQff7LnPa9++dIC89/6CCy7wv/Ebv+GXl5d9r9fzRxxxhH/KU57iv/zlL+9W2/fE6N7c2d+0kOk111zjTzzxRL9161bf7/f9Ax/4QH/RRRfN7JfOed7fs571LF7vhhtu8I985CP9gQce6Muy9Icffrh/yUtecrPPyjzb1XZN260VHnzHO97h73KXu/iqqvzhhx/uTzvttKwCwc7s1jpAdJ3k9aD7bXq8BpDdr53ddqa8v5XCEp111llne9lWV1exdetWnHPOOXsdku6ss85+uWy/1wHqrLPOfnnt//7f/4tDDz0Uz3ve837WTemss85+wayLAHXWWWedddZZZ7901kWAOuuss84666yzXzrrHKDOOuuss8466+yXzjoHqLPOOuuss846+6WzzgHqrLPOOuuss85+6Wy/VYLel+acw49//GMsLS39TIowdtZZZ5119vNj3nusrq7ikEMO2a1ajbtr4/EYdV3v8X6qqvq5Ki+zr6xzgAD8+Mc/3qs1vzrrrLPOOvvFs6uvvnqvlcAYj8c46ohFXHOt3eN9HXzwwfjBD37QOUFT1jlAANciOvLlr4ce9gERBPJmSiXAKyi3izt28Q+A8kjbxV3qBlDx3tZtWEf+rjzgqS1KLHdpXTXn2VAurUsgpyuANt777RBoF8IKdiE0Si026A3DTGPTcIzN/VH4Xo0xNGH5QLcAgMYrrNmws5W6j5U61BqaNKHisoWCjw1vrcakCbW2bB1uN7tawqyFZdVNCr0doV3Vike5HttjQ/u8BrxR8RwUXBW+N4P4uQA0i/EcK8BVcTuTzt2rdA29Tn3L/WkV96Vu4/cGMKHYN0ws16ZbwDRxmxYwTdiBbjx07dN+RfsBZPcLN8X5dI2M4nZ5rWKbxbrCvAYc9QcVuFZpe6h0jrSeLwBn0va033g5oRuPeImhWs/LlfXQbX5eXk+10dEJz4mces/nrlrPjxVv4wEVVTiUFf1By5zP+pHvg144mbZv0CyGk20HCs0w/G6rcC/IPnIVYPs+bu+AfmyYCZ/aOOgi3nvGQ8cTVvHTewXn4v5rAzsJ97JeNyg2QhuK9bDLchWoVsN2vR0O1Uq4aYq10Mlqo4FqmnSusXCsL+KFM4a/e6PgCxPPRcMXse/1bH9P9xdtY3thX82CRjPU3B80tvA1soC2Pi2bvv/EveWVCs8YQn8DYYyxPTou+HevAcjv8Vg0NkKDx1z+XY6xPv1Oz7crfLaubsIKeqRQxOfVjOJnE8banZ4XxPENYOmeKfL7h9rnFeAmY/zg7DP43bE3rK5rXHOtxQ8vPRLLS7c+yrSy6nDEsVeiruvOAZqyzgECGPZSi32oYQ+IA510NNiRgXiBWOGAOLFMODr0u3zh8nG9GFAMkuMlnCYyVwhnSKyqWrGABgmNzPEBwoNMg5Nd8PALoWHFUhgZBkOF5UE44Oa+w+YqNGJb1WC5GGftbrzBZBxGBFX0MXZhENgxGoT9r1RQ49AAPVEwdWhYGXdTrCVHwowBE52HAh6mjE4FnbxLXqC3QOvifuMy7RV07GPnABr/HQAXnVdPd7mWHaqgY7s0vQtrQNdzvrMD5POBOQ6OygC6TC91IFx3NfVCB8BtVc5DtXQjCQdmjnkFdjCkA0QvGOmwKwuoWrzE4u+uIKdJ8T3BxyyQjQSetrOApw5t0+90OO2TM5PueeH0eHHupXCi1Jzf5fkKh9eWipfR+dL52x7QLERHexj+aLnrxRdlvI/Rd1BV6Px+z6OIzk4RHaDg6MTnXjhz1LzWGjTkyKsSxoYOK5RGFV++vY2wbm/VobcS97/RJocyOjJqMRUn9Vonx4evkYGL5237GrYip0WxI8uOthPOtrzO8Xq2fZWe+wrw9EI36ZrR/W1qQNG93iRHmO5pr9P+Qx/H/S7EYw0AO4z9XjogPhNeDprx4qtapclGq9I4Gcde3SI5JQrwRdw+OrGq8FDkADUKehzHmEalcSZuYqxPz6IYJ+X+03cFTfdZkTtDdN7epGdvX1AmFpcUFpdu/XEc9n4bf16tc4DmmJwhZzMHAJBOETDj1ISHVXynl5ROA3/m9AjjGXkchIpJegkXDW7+JWnEbKUnZmI8E/bp936aARdldISMZafDeYVxfPLXbIWeCQ1bjCGREhabyjC9qm2B7UX4XUcHw7YK5Y7Q2HINKOKLoVz3fF5mkmb+NOsMEYF4Qk50bPyuW6BsQnvLUejMalXB9mjmr2CrNEC3fZX3QaXSy9eCox7Ux7pN10C18j6Ix7fpO7WXjO4Pjpg44RwIx0m38YXbuBQJAYSDQ1EbBVekF5+le8qkc2TntlDZYJ4ckNSW5ADJWTytl0ciOUKpPTsjStyrfF4+vLwAQIOuJ/IXi4hUOH7xJGeOLDwrdO1SJEd+l1EuILzEYyASrge09PIVER4zCBe0P6ix1A8P1lJvjL4RHh2A2hmM2njPT3oYx2jleBRuHjcuoNZDJ5SrGuVq2K5aAXo7wnGrtXjMcbq2rtBwNHuXtw4tKjU7eU58Oo6uqPTclrNjgPKA43tG3OtF+kxRDJ8cCaTIJ00EzCRNqHSjZhyg4GDFfYn+pk8MLXQ/bFSVFkqn6JmMpAGAbTXsKO5sw0A3NGFKx2KnCADIWbIy+ghuN0V7inGK3BaTNG7IZ01OJIH4zPE9Kxx4q2bHdwPYqXF7b5v1DlOBvd3evrP51jlAnXXWWWeddbafmoOHm4vb7fr2nc23zgESVm0HzCjNfHSLhFNLE5EcjuoQxi1mXLaXvnuBWRMUI0OvAS6LM7EYzrUjoIy8gmIjRU0IPqI2AOBZJC1zIgoFED8mnIwv3cyMzDmNJk5tJm3B0aDrx4scDSpUgLpqV+DGSYC7rl1bxOpq+O5uCtPP6iaD3vZw3GolQVy6STMynoUZBcsRsZuHgqRJaJGidLrxHGWS5gUcmKInCVLJIjk+LcsiNLQehdVrwY9pUhRL15FT0jio1sbfbYIpLOF1Yr+lgasiP2oYPp3RaAeR3zLUzHmy/cS5yPgWEsJibkSc4TdptuzVVLQn/l7wPT/NAaJ90c49JN+Ho0E2XWOOkgmOj1dKRG7iLL5MEJer0ne6Rhl3SUK8tEzwvFwhYRKXRX4AYPNwhK39EIpcrsYcwezpFAlajSS5H21swrXrgVg2Wo+dPNIw65Hrs5GiDMqlZ58ijl7pxKWREUM9C+3ZnojwUL/oFMmRfCZbpfOV406KEHl4gp3os3BQ8bsy4rl3Co6efeLrOcAzH07NRF2Uk2NMiib5XrznKwtTJK6AdzS2KPh40QhS9C7dD1KMhZ9rK6Lv8jtFUhv53OdR2rROuidnoNrYRupLjrxKjlvhs+sUPmNkcoLOfgGsc4CELf7YQw19cmYE0S+RVKfC0LRccG1cRqKjgWi+F06DC1wKRfODLQjO2YMpuCxuqn283+l3t/bw0QFSlUPRCwO/ERwILTYaRUJz4wx+tLopfG/DwerWoIkkULdaQke+Ty/CXmacBjLZLkkQlu1jQqdPA5QTkI+EetJy8DbMqxGh7gBXheU0EBZjj2Ls82MiD4mzw6rTtecB1XoUowgdjhyKtXCh9LiB3og42iRevLoGGuGpUqpsP75Qqwq+F/uwKmAXY38vhWX1oka9RORVoIlcy8CziI4sE759xm9SEU5QdD9NFMyEXmKpSV5k1zrqqzq9TMp1BzOOL7dJuhkzaI8gPX6z5bCeJ7ir0OmFwxwmjYI5K5oJ7tIpanvE8VFoIscnQZqCgNvznLDgHTAd9VfKo4hkr4FpMIyziJ4KnxYa69G7LZRjSE8aPcP0Mgwdk+6lUkJ0E/45bS8dIPlCFd8Bgv7ieZWJzyS5J47Gk8LDl/EalA4mfjdFuF5ae+h43l7c6BKWIuhaK8/LrNNM+iYHybuU3OA9YBvC6eKyRqOOsJaySjzwSJMMK5/15GARh4dh6Yn43s5z2gVx3/oEqbuUnKAax+sqG/vI+fTsS9hZJCHI8UBC0qHfFVyp0Db7DlZycHPn4buzfWfzrXOAOuuss84662w/Nes97B7ULN+TbX/RrXOApk2GQ0VKJGQESEJgUxGJ6YgHzVjk7IvDvK3KIAYlZuFAiASZOWFer3JSKxBnw4IE3Q7ou4gSiJmNrWNGS5zF1eMC4yrsoCwtT+THGxXcWlhOkR7VpsyrolGJqB3JzmYiCM/jnPAcvghorlTZuXDWD2VdVHLWK0ivU2RP7kOaVbZpBmlGKbLG8KHLIzu8vU3LFGfHJHJruRYjZ2sTqLXIvFzfgKdoj5U577EtvYojP34phDHsUh9tjPrUSwaTTaFvJ5vCNs0moN4cibSbGpQLMVuv12BQxciTCKM1MTuubgpMmtApTZyNt+MCbkTRuZT9Jkn4dN7F2KN3UySa76hhVsM5qlqEIx3haTsZWAk61CpFvlRK5waleAvoT1UaNBxxpART8GW89ix70Pch2wgIkZAYFVGFg4rRIBvx1bVJj/urtgarZdhZFR/A1mus1GHZjeMh1sbxejUCcxKQDT93DVLWEEW2xHXJIm5ibKB7zlmVwT68Lt3/Qw87SJlsiBCTiRltpnAoi5TIQFltlZnVx/A+wV5GO5RxHRMbSREyINxbeiqM7MQYNmpLrIxDf62uhU+7UrG8RTFSHMnOEkfmEMFDGns8Lj2zjRgHJRTLkSAvIPUkQ6Ftyq5McguCAG30jISAhN59qTmqHiKY9J3arGZRgM5+bq1zgIStHqagFlUGYTF0Re9uJ1M2JTciLvPJkSkn4De9Eti0/ORBzyNLbZ1eBogskSI5OJY+ZeZXL70YCCaRLwUA8G18guMA71uFOIagVuDsHrOh0SMeUvzUEtmx6XwJXipGPoWhrdAmkRAA6fgMFNo5/JbEnRLnL64Habr4vsu1msgBqhX0hPRZVGwX4MbpHCj9FzSoNgkiC9o+8WUzSTCQiVCXGtWJz2MMlHjRAwDKgp0et9BDsym8JCZbw4mNtmmMt4RVm6Wkx+QinwKDFtUwdPTicILFXmjsQlljWEQHCAm6WGtCx602PX4xrRE00WqoOGLrWqFcQ+yX8Nlb8ejtiC/O7Q2K1XAsNWmS4yOdHpdfz/C7fLMRjqnBZCetcz4QAizGmj4Dg3aBvqd7g5yedpAcIIIA3dBC96Oz1msZzq3KFibCOvRSNzrBWq3X2IgZX9td2Ol6U2FlFK9RU2CyFm/EcWiTGSvm5hWjfJIi5QaAyC/LHlx6FuJqCtBx5QJpYsIZd9JpKjxntOl+i6JKzk74dAxhAWmiRRA2kBzlQjtUkexVKAcT+4Yy4rTy7Di1TmcOUTiN3HFwBN1FJ3PdKQZbWm1QrBGnEbMZZZgaB1kvSqxHzq9S7MTQqSordLgmLmWSOrEdZ1HqlL5fqmxMoWMx9F4qnkjanpodj0xwfuxk33lAHQl671nnAAmrt3hgWbxQ5Qw5an2oBkmDpp0zc5nSkpkWJgNmHSEgRpuIAxEfOuj4DonfE99BOED0MhhYKML/K5cGfhJ3AzIugI07bsaRhzIx0BvhhPVEodiIA/RGIDIDQG972L634qAnqfFekDfDF2Sp7bxeHL2U0olYKQafeWRHLyayugY7GPQyagcqRbmEyJ9yIqVdLOPUWRFRY3L5JBF4tSA2pwYouEEcCfsFFHmfwkjTxQ4KNIsxErOoMRF8HgBoF/MoHc86qY90ejFap5igXmvDL/I+v8wsFss6NtFjvaYbKNrIoFiJPK2bxPWMTk9vR4tyJW6/UUONG8xYYWaXSaenVLO/A4lnYRR8FPyjPrSDAu1i2G+zoFGTqGHk+rS9POJH6dbs1JvEWVE6fTfiO0U5lOC3tE5jPTqM5CisrvdRR6dHrRYo16LzTOnV6+LeEQRYGbWRzrPkofC9LJ53a9ILmcjTdD+0g/SsZz6HDxEjAGhi6LOu8+jytEnnqCgsqjIKkIoI0YpY38aH2HnFDhKNFTIi5Lxix2cSJQNcq0V03LPToUpAJnuE39MxTZ36NJcg8fydnR2O9KS+sQMDoiO5UkhhUMJDiYw7mPSkYgMER0mKz/pCbDclbeJyabS9ag4etnOA9op1gbzOOuuss8466+yXzroIkDBvwmSAoxNt4r2wWupIfBeZCpTqLeXkdYO5PAnJMWLxN4Erc9h/KFPqfYp09BwQoRIThcf6VYuqDN+VQiZqiNiMlMGRsjlI/t+3miNfqtWZCrKMaAGA2bAoVyJMUrfwOnE6wqcWUSEloK/IIRKwmG6VyOBQmBaHdCVwcxMYysqgdaVy97TQYbnh+doVYxHt4dR1D92IbBHG/+M1GhhQWDBwAUTki86RZ6JazOxVUimmz2oqQ5Cg1ipxPAa9WJpkMMZSGRreL1ouTULXuPUa4xghunE8xGgSbpp2I8oXrCXhvv6NHoMbI2y0EqGP2vJ96gYlECM0XqfMLYb2vE/XQ4nl0TIO3FQEbaaUxdCgXoqRrQXRRxT1KZCmaD5F/WihbxRcj6IQBoj376R0/NiRGB9x3sKGkgwXv480THzWy1WVyqAIyDTJY6Q+CGKlMdoUMwR17fieAkQGkaZsL416MUZyFlSK5vZpfREJmSi4eM+5sebsLylime5TD0U8qAgHonQsdqq1zy4XKQTXxBlrDRoqV1NrsD5FTZgSxMaC08hcOpVlsrKCej0fAqNlxUaK8MhxNLvP6LCclh74PEB4luhZy6gAU7BVOChmeD3yHCRVAT5tl0eFPNyeKBPupv2sILB3vetdOOuss/CTn/wE97jHPXDOOefgIQ95yNx1f/KTn+DUU0/FpZdeiu9///t46UtfinPOOWen+/74xz+Opz/96XjCE56ACy644Fa177awzgESZkYKGpofPN3mjg8QnR7xEk0PbArNKqEBIlVzk3ZHGghbrl+UXgCU8twseeaE+J6DIgJkz6KMg1ovEmJ7hUW/DN9LbYXjk57yceQ9jJoCnl7kNJA5lZRhW6GmOsLMOXqj4KKzo51L5xtTwH2jAXaANDzlB3NfiD62ktDtmUgqNV/mKW/nafTpN0lsloRJar8kTt48WVIoMVP9pSnFZYZ3VGqDE+F3TuFeyMs0ADFtm+oalR6eoErWadEY1zE1vjW4SQ+5bTZeszY6PbbVXJsK6wZFhG/6kYMRalOFn8sNL/gOmj+TynLu0PC1pc8pB4j4PFkasdRUmYZHkSYA1K9AuF7FVCp0lnBQSOdS8SedQ1anSovTIFhMIXvhTdecUi45WLoBKxKz0y8kFMp1h3I9Eog3WugIGZIkQPiP4DkR0VtKCMcGKKe5JISNyKOEwHSh4MdiwjSlhh3kLeJ9VHkgToKIJN3rtejRMuWYJA0EMjiQ5C0moxJ+PdY4G2uYWOOMYempeoUznEZRGkjbfDup00Pr5nAz7TeNl07UPWNIUM/2hTPpuZzmTYZtpHRCkktI56LSeNQk/qNURWcYvYkT5MnOYcfb2n4WWWCf+MQncMopp+Bd73oXHvzgB+PP//zP8ZjHPAZXXHEFDj/88Jn1J5MJDjzwQLzmNa/B29/+9pvd9w9/+EO8/OUv36kztS+tg8A666yzzjrrrDO2s88+GyeddBKe+9zn4u53vzvOOeccHHbYYXj3u989d/0jjzwS73jHO3DiiSdi06ZNO92vtRbPeMYz8IY3vAF3vOMd91bzd9m6CJCw/vWA7ieSnBLVv1PBwBT10bWfJee1jiMLunE8owkzwZiqPCQIQKOORe7qRRH5WYxh+6WWs1yqfprJVWXLJEaq01WKWl5aeS4aSp/W66T86jRszAJzxB4c6yRGJqJcZuxRbhBJ2PE5pswVA0qjSum8ljOrFAAfj6Ep/bnQMDF87TVyouxURCE0EjPLUpQhQWBZxEHNmwmmDBCnFcMjXJ9Lht2p8fK/Em7YickMwXRgcQ4M0SnOeFEKaTobr4udeLQ6MS95VjrR0JN0nQCgN04wXzFOM+8Mlm0p2qQw2RQhFUNwXk4+TfBkHj3jPhAzbzel3pzXGlMJDs5Lb3FfUBsH655FF80orOxNqmRuB0kZW9a5yoiuQo3di4gUkBPgs2tDy7xIaGhTuwierdYcirWYebXRQI9i1Kduk/QB3dNGAYYa42cgYFcqjhS6MkV7Wlm0VJwjmSyoTFCYqzx8zBJTwxb9YbgRhv3wWYlxobGGoz51U2AcodImQqVYNyhWKY0dnC1I/VJs5PD+vGK/O4sQEbSs6zSWZJFX6iOKShrFGV+uAFd7Z9hYi6hN67NorJQsCStAFPNVIvoX7w0hmhruyfj7nFqQygFQ+1YJ2gF7KIS4e1bXNS699FK88pWvzJY/8pGPxDe+8Y09aAlwxhln4MADD8RJJ52Er33ta3u0r9vCOgdIWG+7hxomB0gO0FRYz4x9cgQEZ4Q+4XxK99Uq8R36Bu2QMoEo20WhjpomzRLQLCfHBwCq5RrDQXjSBmXLWRmlkIimZa3TDHet1xU2YiZQG3H8tjFJmr4x8BHXV5OUHl1EvZyM51T7TAcHAPTEBt4IENKj2YEJi5RX+aBoxboAVI30u039JcsskCMDY5JTVKTK2Tb2q+sZfsnZSqoJ8+E5vK5bD0fp/Y2HpoKV8VjG+hSCd4AG8YFim6zidZVRXG0eSqWBX7zwCyqX4BXmlZ8gBWBVKmSV6oHgIGUvkNgdo8A7AZLToxySY6XBjlszFHABOYbp5wQX1mn/Xnkok17OfurahoKzcV0BS9FOlc05ctQuWyJzVEO/AA1X//YwNfHtosNsfeZwkjNC5+2L9LJqeyrBHE5wZIRDKpXVpfwEHZ+LZ9ZePO+RPzOxqayF9cx3c1XB96RnqFSnF3mVstuocn0zVGgpG3CYJB2StpUHSMZCI8lXiMzAzFOnc1UeNXF44nOvtE/lbmzi/rlWw0deFBV5NetaFC4WGXBxAlRueBTj5MgkR5nS3JBBWNzSjGuTnq95/Egd+1t5LyZDiieP9NzbnkrOcaVE4V/F/ig9a7aecoaQHB9ajyYjppHfvXCM0rPuNWDr2bbvLbN7mAVG266srGTLe70eer3ezPrXX389rLU46KCDsuUHHXQQrrnmmlvdjq9//es499xzcdlll93qfdzW1kFgnXXWWWeddbafmvV7/gcAhx12GDZt2sR/b3nLW272uGo6wcH7mWW7aqurq3jmM5+J9773vdi2bdut2sfesC4CJKy3w6FcbROEJVRFdRNnJlP1j7JaR0AkYybyLIW9iRQsbRp6IBImKeY2ZYk4IUNrDUNgjU5kSkuFDK2Gi3DXpClQc1hbkBrj/ktZSFBkSsX6kCjXPcq1GG1at1wHymxQ1lAbQv90HtOwlRAuCx0RF5MQynS9KJpBG5NUgnmZxL1EVIlqftWOE6h07eEZkhHEXt4GWWaSn7omttI5PMIbiq8UrHKer6mEJmhdM/EZgTLpD8X2TXIohyAAni3rqZmziPDwdvL8RNifVclFJDOb7TJEFvfdJmjDG8BSRk0pSdvh01bpHL1O+00ZgulcvUYi+feEpgq1NSP1ppOVWZjzxPKkKrDU4ZGFMrlbuKBmXth1WgOqGDmRDSiiPcI4m6tf8nMtYVeOUlRJ3LNZUKz9RArW7dDDDuL+K8fRHspi04Vj/a6isKz0rHVSZ6bIbt0UaCNs2k4K+Kj4bWuK7KaMTtUqvpWNuCdkogdFfcoNOQYkaNKMY6ZpYxPpWz7PIlLCplNWKCmDB/VlIoKnRAoW3HSAovukNByVZEqB0RxBbQYq3adVDoWGA4iIoMNMNLYYhcg+ECNAN6vdFhJb1D6sBXZb2dVXX43l5WX+/7zoDwBs27YNxpiZaM+11147ExXaVfvP//xPXHnllXj84x/Py5yje7zA9773PdzpTne6VfveE+scIGFm4lDaNs984cwlAdmIit70kKYX0FTqMD23cjygsgMbntNGiw2R/h5F69odFdqF4MisDxzWIx9IFY75K44Una1iiEu1CorghJjJUWwkTL8Y5WHtsMyhGEVHZ2ShJ3Ggq1uo6PwhVjdH0yTeg3PJP6BBz2hwfQCpFqzSIEhOnq+KLH1+GgKT2VhQyCqJA8HJ5OrhWeHUtC4f3iEve8HZK7OcF5nqLE1yS9KLXBTNVXJd4dQIbgQdPxNfo7aI+4V5IBVgqfBpIbbjgVzlcJZwRoAAmaVyJIIDIxwZ6qt2kJweK7KRWIJBpe1C+RbhBAJQPbAonXTGnKxqTvsqhQBk4dNOREVyEiDVjco4OmGZcOBFxo5sD10P3SZSmFRvTtfbwVDBVynwKKUOqLSJ9dCeICo1w7GwlWL4sRmC4S5yelzPs9wBeo4ztsoqSlr0miRyaSyK2FgjlJlJnHDclhjFNPZJ4VCbCIHR0N4oTu8vNpKDY0b5sw8EmJ8hv9rBbAieExAEMpvY+c5iRhFcqTQgGpGKV4iJDcHkSLM/XxhY4neRE9nXaIgyMFTsSLOQ6EISxnTVfPFaNoHHBac9XseaIH/NavHlOrhwsK6FAKMswiqg3X1htxUHaHl5OXOAdmZVVeHYY4/FRRddhBNOOIGXX3TRRXjCE55wq9pwt7vdDd/5zneyZa997WuxurqKd7zjHTjssMNu1X731DoHqLPOOuuss872U3NQsNMZGbu5/e7ay172Mvzu7/4u7nvf++JBD3oQ3vOe9+Cqq67CC1/4QgDAq171KvzoRz/Chz70Id6GuD1ra2u47rrrcNlll6GqKhx99NHo9/s45phjsmNs3rwZAGaW70vrHCBhZmzhqwq+SjcMwztyZi8iPBwFoPB4obLIA0M1YubNZOJacVZDCOfOwiBEwHVjw8TKkOUS9yuKf1I0yUxmIS7TpGKl5YZHtRpnnatRI2SjgR4Ta1CEt5sWqOPOGtL5STNBb22qg8UdaTgjRhnDYW/OjCkMlI/hAKPgidBcarTDOIPtE1FccxTCVqJOG31OaaPMhUxEJoen2Z+Aw5j0WwDailn+NAESKaoDCIhJRqbmsOpcITRNRGFXjvDMKRUgic/KgYXnXIlUn44iOMZzloyZCA0nKk5b+1QEVpyLLBkwt+ZWL0VrOMJpPN978Irvb9dQBCkfbKXYo6fvEfJRMuqjwDc+EfRDmax0vVIGXdxEkFclXAaI3WbFM2O/jDwKSmSIsJdqRbS3dVBuzpxbZivqFLFQFM2k7LhC3J+9FEVjsvPQwgxiokO/xdIgXDCq99Yv2pSxKQqYzjOlPHoRIjPacz20MRWD1aG2X2i35r4rNlLpjpKz29os6qNiFJijPm2LvCbcnD6SNfHoe1FA9crsd1dotMOYhbYkBDGXQ2PrxVAuBojwqRQNRYgekhgsCpeVROG+mfMsepcyX1kLbeJ5nNU1UK1Sv7hEjJ9QpmiAwEw7p1zML5A97WlPww033IAzzjgDP/nJT3DMMcfgC1/4Ao444ggAQfjwqquuyra5z33uw98vvfRSfPSjH8URRxyBK6+8cl82fbesc4CEuUrDLRnOLmj7inFmznwRtWRCVfa4sXzY5EtMhOu53o2oii6VTdNACT5mqjbvs5ekTOEEQtiWxNuKjalMHITwdjEi3oNjJ4xr8xQGjl5evkg8p6aAKmMjqBq81tOUl3iwmFViDBAry6MsgFJ8R4C9SBzO9U2mDCzD3qFf1FxFV6nmOreook1QI78Ys1ToNFDSNYDn7P0Ah01xBXKV2Nyp4P3yS9pPwXj5fSShO1eJ+2jOvZMVhpTOlIRVhVPAzi/xenRKsVY+OeisMt5HpkYs1cel48OfdK87Dw86h/h73zEM4Y2HilCPMi7VkqLudgqe1IZbBVC9PUr5rzU79fK8WGG4SdfINJLPI4raSpXvmLmoG8ccDoKzVetSluIcnpgvCoCd2FTE1VaanXW+nkLgUaZYk3wBlGHu3rjVrL68owwXoVcmVfd+0XLWZ2ks1/jTUf5iUDbsINU2jV0rRazUDsC2NIlSaaJWpizEYkywU8EOvm4KqIXYhzHlH9aGIsBAmBRJZ4iMlmmdlvuUKUrPfbNUod4Uvo83a9SbEJfHzwUPRwWPewniYmqR9kkYtrSp5pmAmx1NGloNF8UeMTLQkSfFsNdaogf0dnj0dkSHcM0mzpMYL2zPYF4G294yl3zzW739rbEXvehFeNGLXjT3t/POO29mmd/NPpm3j31tnQPUWWedddZZZ/up2T2EwPZk21906xwgYeMtBdyWQmRr5HAAEGe6WhBCoyUdmKT5orwg3LkUrifpe2WnMmJY6p8+vcjo8RmxkyJAklwrox+UlVNy1CeRem2lYSvKwBAhYwHRFUSGHFvoSFJWJOlft1BculoIwZEZDcTK39A6CcQREbI0XB7DG53BStPmdTpHPSfq7IXuzUy2E0eAEvE5QVBJNyavYh+38YAi0TbKEBk7GIociDInmX4RE0KRkbfpXilpJlwmEcG2L8QcaXMvI0+prIcX5G6+J43KI5DEi58qK5L1mfh0Rd4HMgrFtaeIZFoICEylGTktkwR9bVI2k9EuKycGAG1rYOPM3NkUnUhwnwdUvPe8CvpRyKHNcj3srFqVsG4DHSMVHL2oG4CSF6bvV2qU1KOKRvCu6lXwg3DDaFQcGdIqkaspyzE8fxGSHCsU6/G0ZAkP1rUpmBxdR7hxMhAQWa/Nyt1UkRxNAqhaeUxseNbW6wrro3BT1LE0ilstYdaizs8oiW+6Amj7dJbh93JdQccIkmpcKhtTRV2m2kJTlLcuEzQm+5PgwEEFNwz9ZfsF2kGMmEWyczvQfPx2OJshGP4TP62a8wpXrOnlG41pCBkQcHebxEOLNY0iRnuoPEy14lGturis5axX1aaou4t94AqNdsGgpcSQzn6urXOAhI23argtCXtuFzzsID7cffIOhMqaU5y9Q8sy9WGnEl/BplpbvEyOwwLmyIy5DCKsLqkT5ExJeEe8xGhAaYYJhskE7GifTqjf1prF6EwtioYyb8JyxkxwhuaEwmW6K8FdNIhUOqURF6ldbV+loob8mQZHZdN5S34Mp0IruTw5DdPnCQDa+6wuERB5UiNK+XUseCkdnXnqt640yYERsFfKslKzCtKCNySN+kJ5P6NYC4TrltSX4/7N/BcHo4Qy42wetaVNyTO6BRy/12SmW3L6pTCfZhgipW0THFEYi0FJUE2NYREuTiGymep4I7ZO84t8FGug7dgYoC6rePxUGZVe4oW4571JkA58ARP70ZDT3lTpmjmkrE4B42Tpz1Pp3F5yWpxjOC1wxaIzE1WWvUr3OlSCsTm7ri8mVgPHSs6IWZ69QYNelVTfy9hfhVB13mhCv4yaEhvj8L1eL4ENOt94/nVSEXdVQBq5DT1y0uKySicYcaxSdiRllFY6c4aSBAh9aiGcKe4dnZxbOcmiMc3UQBtFWNuYpdb2FY8BrkoTFzIJR2eTS5kZaNMyVu9vUz1AmiQWY5c4Po2E6xTLcJDD2g406iWdF9fdy9ZFgPaedQ5QZ5111llnne2ndktE+F3ZvrP51jlAwupNgN0KtItxajG0IeIDpOm0FXgGEDJZgBSacEh6PF5uKGACERlIESIB2YiozjwdIUDAIyTbPhZ1y+q0HYuCyTpYYvIyl+ALQcqF4vV1nA3pImEfBuCQAWXO+NJkJGeC2xj20hCk4EQ0b3tC0ExkeWUTGI64pfZzHTaLbHZHJTz4vKzPInSp0jjBV+BMu8nWIiMsp3bTNjkhe1qkT9di1in6NtcJil/ULATgoTiLJYtMiGPR9XZebK+QiMsQm4l+m86Oy+ayHtzfIQss/KdZJAjDJ82WoWURvyJmIg37NRaqMLXumZYjFpurMZbKEGrYTIqbAEd9rhkv48frQaNkx3qAV+vVHhBLtcgIKN0b9SagWUrRWNOQ+B+Y8E9k6GKUIMusppuoIZUyxzyTp/keaoVYn1JZqQsiQVOJm3pBox2GVVsR7eFIUCVI46LsBUXTtPbQMerjvcK4jZmRkwqTqPnDQqcjA006P2M9AxNnWkyF575TVuVZiACgFWcTNkIQUJYQkTcVE9CpbIhIrjDjJCirZLkc0lUqUhTY9lOdN+pDM1Bo69Q+joYK4U3OdpyIqM5E1GpkDZ9Eeteth5qCQL3RecICQcylhhPlNkL7FOpFBVtPDcid/Vxa5wAJa5Y93JKFj4NT4DLEG70VnzQYK2CmhpOAunSjOItFN1NZPchhK5mhxC93K1/SaaDKsn8kFCTTgKcyoEzjgTHtV3oB8SMLWedp3RQqBg9oCRv3RsMPQsM4TDws+CVpewqW4aG0fykvIM+H6mchvSP5WBLWor40E49ynbJ7Um02yW3iPiw1bD+qbA8112ai1O16EWiiTlizycEvR8imH1P+vYKdUDZJAbOeskmI58EO1jTvZur/yoOduUxRWRQdnQdXeZXgKJrZacEXkopp0tmbx/Wa9zLL1LIVGJIsNsgRUmgoVVl7IFLBFvshNevwTdtxUH8FALCl2EATL7qDRi9KUHNxTm+wgXDvjG2BOvKBqF6VqixPGpwCpkUypzuVC8KOEkyYiuame7btpZe/5FxJzpiKXn/WV2JSMQ9OlgrcKZNOcvro0ydOlQI8FSaOhxo3GmMQvqngaULU6ARtUUHcsUqK3la0URzTs+Cg4HJ5n6DU6KA1iyobN8gZyoQnxSSLnExWmrZTMCKZEkWOGbp34l73gLxOtJlNfUzbcW3GkU8K1WMBSTovrpm85+N3WXsQcr147wkHyJcJkmdhyyWFegn7tBhqB4HtPescoM4666yzzjrbT81Cw+5B2c6Orr1z6xwgYbbvgconkqdCipBw1CaRCjEFcdHvTMKbqKTDI7V5WL5f6MdYJLIxhW5tOrArdaovNJiFiqCQakMVaeZKKqDKJ2G/EEWgMG9cr1IZjMKRoxqCeEwzI42Wsp0GRhAEJZSVoj4cgWFSb5pJFuOk2WImjknInInh/FyYgowJ2HFdIp+2A4N2IYqrUTXuRYU6RnjqZaDZFIeGxai5slhjkxClq0z4vY0duzrpY2UjTJfHCrCxw5TVfE8kracEUWlROTrBkCpFtnwOV+3sXIG4CZ2ywK7miUFmm+t0z3I5AknkptvYAxBV21kHSxJ4qZRF5VH2Qt9tHoSQ3eHDG3Gn/rUAgE1mAy4O3I03WLUhXLQaQw5rtoc69mFlLLYOg3KjiVGA1XEPk5jN1FgFjMJ3qmlnRsgyIyFgEjpHyui0/VSuJFSmj9sJ6FHWJWPYiNnhqTK9cip7bqejrUA6vu2BxftYzE+8y1SbosTpoBArII8YU3RZlACRpU1mdLJ29t14jnQTD1y2pdhI0SAunzFOUZ+QHEHtovNTXOIG3mSaWGyytIiIlrL+UBwDq7U8Sp1qt1GSghBtnDRQtXjN8zFIgDVB9vMSNQAjIoIpQ6/tpyhx+gz3lNuHb06/hxwgvwfb/qJb5wAJ07WC2tCc+gsDHmFZeFDAWmaSBonMOFyb1HiLdaCKxQV5EBk7UZsqFVaVLybPadMeylHquuayXFwHayodnO/5eIXbQj4EOdwFYDaTaM55pbRpBccvRiEWSWm+U1kb00UXi3ESG+vd1KC4PuBHan0jpdbSgNXvcfqxr4pQMBVgPpGrhAM2NJgsRx7GkkpyBlSMctnDRmenWq6xdSE4O8u98LlUTdCP6cWFTjWY6CW9WNYoo1N0o/IYUbp2o+BH4jpwh+XcHdkXRZM4KVoU3nSZeKLYll70WsB/0bHyAh6SKfNqJwpoDAvxuyqvoSbFP6VCNAC0ix52GF9Cwwb9mKJN/ea8wtgT/jPkmevElez4XFeHC3NTvYDtdVi23lTYqMN1pqympi4420bVekYIsRghc+qlYCY5Lkrc36muWUoBpxpnrnJAdFBQOi5MSgrD3mlWqNYjzZMB1Sp+xjJnjB435QW8KZ5BGk/mQN9QOVwsdzxTx80EtWfejg4hHCGS3fDeJy6VT1C9LIYqJ2ksqCkmZDyxKhXfE45kLnR+3JQxmZ8brZtBtHRP07Ean7hFTeJkJWFLBy5z7oBMwoAgVBoklWIpjqxQM88UPMdXvFHQkUumKtlfaRNTAyBaQGc/19Y5QJ111llnnXW2n1rHAdp71jlAwoo1BV8lzRYPL2bWMUQ7UShGaeakeJYUP6dC4qyrMUkZKZwpMUkVqJVNMxrFsFeKbkAZeILIag8jK40jzH6zDKXpmaAM8YeTi8fK/0/tpgCMK1IZBQ4pTxFCyZigOE6TK916hrWqHWGaV96wDrU9qpGNxmkHWgHDMK30m0KUoNncZ+KyqzRL/RMpsR2qJKg2CBXMAaBZ9HCkrzKM0vaDBssLYYp70OIqDh4EJbStVYhAbSpGoCIfjTcYRaXE7U3MSnILnJEzGZfAWoRkNnRWngEIRHOGGUUmjjSamQfy9iwxk2fuYvzSTdJlYphy4lLW0riFasTMF4DrFbAkRNc3TOxsq9SHVGW7WUrRHtd3CbaJdZd0z6I/CCe5PBzjgGHou4NiX/Z0y8TnxhtsROxsezPE9XU4yE9ittdNowE2onBfMyqCoB0C2Tc0ViXR0CZBXxnRm6IrLeApa0jW4oqf7VCUmhn4FPkZxOevdJyFBQUW0fMx6uprzdXDoRIMqCpBuuU6ciJibNUMvum1gMUMsugCEKJKDDvXgOKIRk5I5v1J8vVUNqF2MtNTpaxTL6Mu8XOCVB/Pi+hwHGtaMcbI8YYERWW2Fu0j7CCdoxyPyHSDVMYnjq3FSHEk3lWeozJUAV63hcj8sqmmW23z8iZAwPgyWDGPtKvGclTICD0oZQsxjsVxp1HQzT4mQXsNO6/I4C5vfxs25hfMOgdI2NZ/dcBySsVsRGFICWEYkW3C4WEaRBqPXIwvhXTTuiKMK9WEOXwsUmwH4RK1g8QBslWqI8XcjF56+Wc8DVGwM+MS8Ms3LAvp5PEcRsKJ87k4H68bz6Vcz88RiIrJIyqyWkOtRyeHnJ22TYVRlxbhl0LD20191JujqNtS4hVJSIZfbPQy64k+GDi4KFxpFhosDMMJbVkIOOSBg3XcYbAdAHBY/0bcvrwJALDZRJwSwHYb8pd/2m7CDaPwwr5uHJyxH68u48Ybw3e/VsKMUh9KMUYgwoQCxmB4RsAVScpAMRQqMwXlCyTBZUl8k66H7aU0YmwqOEWbHZ1BgivaQYIE26hAbIc2OYnDBsNBTGMvWubjmJiWXZkWi2X4vW9a9OPbkxzHka0wiW/GiSuwIzqPN06GuGkU+pZ4VJONEj7yevQoqfXSpxS5zOrqiaynDHaaduqFyXWDU+LTd4RnzrbCAZuCgFXpgQUb1xUQtU3QmGJYRKVsK5MELVMhT8fyGUoU8qTaVb4xnP6vJ5onWWaSHCPQGCNqpEH4WuxAyee6TuKf8MJZElIZTmRspiLE8bMf4EMgQPJpwkVjTRrDoAUOL7hLKQsspdwD+TmEtqR2tVpBEdcxrqBt4taZnhKp+BqaYNMIganGzhVrZSTM+jT5nDTQ8YfCe+i4XbERTqx/YxB1bAmq7+zn2joHqLPOOuuss872U3NQnExw67bvQkA7s84BErb83etRSEynMPD9qHHTj3VthgVDMl6WMpg3+xSlE3Tj8swmWkUnmCJpllB0R3MmUzNMVdFtpTKdESCHf9oFx5AFETu9T1EfNdGJ9EezTidC3gU4yuAdZkpGFCOPYiNGDNZbmI1Yl2gcpppq3IBZ2t4lJupSCD24hR7a5dDwyeYS9TKJoKmsKjkQQ+2ZvkqcLYtMJMS6SeWgxaAf2rDUn2BrP0R2bj8IujSH9LfjdmX4vtlsYCHG3RNkU+BGGwm6zQLWI/a3FssOjJuS+0uG1L1Cyu4i0vlAcCznQBPTEW0mpIqZPWeUuQSHuQJQMSKWR5hElKxKbQBCxMf2CfJxXHLBRH2jYa/BYuy3hWqCzZEUvlhMUMSLb+KNbL2Ci40vteWq5usxTHBDvYDtk3DxVsZ97NiI8OFGKtNA+knlRsqS1LWATUWUIoNR6HETopMcIRIil96me11CYXmWVvydC6d5Jj5DeyA+P1xxvLJc10wyKrLsnCmi77RRpMdohzIKRxrtuTQIRSQ26gp1rBDfjAuOMLmRgRvRecX7ZSy0wCREJgROU6KFgNlEdmUrKsQTnJxHlOO1H3gxriSiuI5isUol6JyWAYBtNVwTb9YY2cJEpzFGqSwZAwjPE0XEdOtnonrymbPQrB/kCwVVkYgliWgWCdZqUzSIPr0S9IPWQbU0jrUwsQ5iQXX9vIdqLNp9iIF1HKC9Z50DJG00Cm8VGomMgRrHmljRKTFlyU6RL4qQYjlttL3WXAAUQMroEjWxuEhgpeAqerGJMHSZBifJ8ZkWX1NewHAbGo5C+zRIWZXgronKXjJAHCilKjQJDtapoGqxQeFgiyI6PWbUQk3ijuMg4vslfBlGUl8VaIehv+pNBOepDJ4h/km7mGAZ10/cE88ZOUjZOfGlYUqHXi9yUgYTbBkEp2dzNcKWKny/Xax6uMmM2Nn5abMJG9FT2Igv7w1XsdOzbiusxAylJna284oHdlf4TEmX6meBHLfp9GRW443dLrL2tIime8ubJI6CeHF5naQLqFZS2wNspjxMUEs8Zi85PbpnUcXU9X7st0HZcHFNSv0HgmDhclSm7MVGOmg0LqW2j2Jj6qjovFL3sEoO0KiPejX0rVotUG2Pjg8XoRSKzSMvYMDYflFDLRSvjU6ehBOl/yEfxenHUmRIyXRyfgBMcnp0aZMqc7zfCmMxiP3VL1oMMxgwFSYFwn1C2W0bTZWUnF0aC2hdpTzLLJCDpJXngrJK+SQMaXNhVSDyeAS/ZhrWCu92ASkyDJ6cDp5MlciKP7PTTJ9DC92LjltpUbBydXwWtYcxyZlrY220RpkgYwCA8q28Qub0sKNKUJhE0AQvM7uuov6ihEqVcPiA8HylOoeeOZgEbynrBT3AJwfJuvS9SbUPMWmg3D4kAXW216xzgDrrrLPOOutsP7U9J0F3ENjOrHOAhLV32AblK65pBYjMp2jKe4BmBc7x93ylGLXpG45+NMtFJhQI5FCWL4SQ4bzZDqZmsDRjIUG0Js2oghZZvhNlwVkfYYPw4UQESYnwuRZijURuZuhOKdh+rPVVGijCXKK5UnMpjGZBp2iPqO5MmWV2mKAaO3RZVg4A6NIx9ACAZ8Y0gzbGYSmWYThgsI7bxWykA6oNLBqKXoRppYVCEzt8w1VYI0G+NrT/pnoBG22sRF73MW5imYZYf6m1IpqnRTkDnSAVjuCIGa6cofIevMjUEbN4WeE679PYR6VKEBdXFBeQYYEZqEg1Cj6GBGyrMW4ooyUsG5Ul92dhXDajp4hQv0jRItJHcl6hjScxiv22fWOAjVHU8VmroNfDMcpVjSIm/lUBhURvxaFkbSzLUCrVbVI+ZdK5ysD1UiYbEJ4fvqfKlBjgSqH5IyCwLFuJiLuk+VU5KIJyjOeK9nyfac9V2SURfLkccxZhGx+mlbbPEaC1ScWZbm0k53qrU9TGgzPOOCrVJJ0hM1GoSJtnkupfpaQL5HDsFBTrzFQiBH0VZTOSvpfYvgILJfqSoq4pMmUKAePFftPKMyTYWsP3VzMu4MaR7E7kblGlXllwf7CApE7XUNJf5Bi4U9I7lYgR0L1moVLPGV2ZhpsICHIksknRyKQ/FMrttO0Y+CH2iQUO0K2HsfZk21906xwgYatHDNC3vVRMb+JSbSlLuHESzULjWU2UoC43qFAvE+SThPnaBSG+JgTbZgYl8anb5JQoCy47JutI8UPeioe1RV7AEJEnkqXLKl7O+xSDsqwXRnwDWZCSszZKxcVEuYhgT7H6cjtEKgxJjk4PqRhk6eGrxFeil5CJofaqalGVEW7Tfq4iqo1cgR11H3U8yRsnC8ytmGcanl9u5PSMbYG1OrysRk2J0STyv6Lj46yGo8HTqWzg9VOQpBVCbzKbSUKOMtPOTzmkvp+4YLJ4Z8YnKtI+Gd6skaABPn2V8btcGZ3XeA3q0ouXYZ7Jw/CjyFoiWEgaQa5uYmDWwkn01jRMVBEuRkIUdJReMJnNS0+mF5f1XFyTU/4rDd2Q8rfO4DLuTy5oKyCXDEbBzEJvFQs4OlI5dgpNrFW2UZYYRV7YdWYBP1RbQhviATbqCmvR6anXKqjVJJcABEdGnvu0mKqu0/OrhSChaTxnoDKMYwXkI6QXuNhwL3cM6Vl2Rjg7NAkyyPhVXIOM700NR3XoCo12yvOonWKYr60LWCFnwPws2r5Mt5mDUN6Wcymx+xkF+KkMQZneT2bF2y0rfjvNJ5JOIpAkDJycCIYVqNCurT3wd+js59w6B6izzjrrrLPO9lNze1gLrMsC27l1DpCwdhgIniTAZQyS5H2MAoTZeCRB60TMZO2gBY2o9I9mOUU/7CBlZmViYVRxeaJRrIfvhRAmSxoeQnNIkPqSGJhLRFkFJmfT7M4VSswOBSlXCvRJDQ6qvyUqSJPkvW59plfD+yqpL0LFZCAK60XxMiJVwnjIaBNopug9EMssFGWY3g37NcMw3ism4BIc1bQGoxhS8+sqaak4jWno27UaiBEcb5UI0cQPkyIhc6EJp1JEYqRYmC+EyuM+5mR+hYrc8XcRAaI+0Hb2d68AHWfDWtRjy/RwJKRJ5zqHCDxNDpaREiCQTN0cYvHM99jAeXQEKvNQSt0ZEelQTmTIDVNEwkRYqFnQMBOq9VXG7d0UUTVl6oRjSg0qw2J5pk7lDNrYR20/RTxUlSARFSNIrtZcx8rJCJe8h2LHjQGs0DqCUC3LY1AfDOoEW/FnDVHmIenZaBHJkcaRQFHpnKNhrc+i1JIsD4TItK2Erhjrhwl9Lfps0rVzdTof3qZVcLHfmlajIchQ588RGQtLFgBiRJciqN4p2JilhTaV5UhQcB6pkbXXgNA2yjgz45QxmT0T0TJhTKklxpH4/J7OBClj1D5F3sL1oOSUfWEdB2jvWecACVPOh9BwnwYGMzdcSgNC2xfwDmUyLXiGFtwgZU0UPYsyQjn04Fqr0cR013ajAFQsrknpn414gdhUNLTccJyGznwJkdbqCs0K0loUQ3ViEKGHn45lK/FClBlngk+UQs4qDz/TC5Xgn0qIE1aJQ5De7sj7VcatY9bduIlieWs9zvhS2s8Mst6pwKlACNlzoVoxqNJAXjQqG8hkQUk6v4x7I2AnOr+5vJ5mNkSf9cEASUCOX5yprpNzmOH8ADkfQ81ro3BwJAQnC3zSZ5Z+TynDtH0hODFK8pBUUgsmkcJaZc7vjJOlkPgcIisJCkDsA/J3VatEwdh0bYpxdGTGPtWGql1SwGYl7ORI69qhdDQZUAyzuRUJBcX+NCrL4AMCt4qy6ryAyyTPhDlytXgh1qGd9D0c32X3w7QEgly3GDuY+CyzKnxr2QnySmHe5J+dJJcylcIPdA7xmShTYTTlAeW1+E77ip89Bb60Wtxz8d7SDUCFRp3X8ARXMUzqYKoEXVPWXKmT2CNNXGprMK5D59tWo51QTntsfyNOWjzLshA1TRS1kIwwEwiuYuyiQsiG1POeD5WPKzcz2VBEL5iGb/eiOehOB2gvWecACZssK7SVmksZcwKjZi7LwMNG5WGK7qjSJaKgSdWyC+NYTVcx4dQy4RTK07OPWkeypEo3vW5SxEK1nh0fmv2FH+KHSbNlTr03IgJUpvR6mv21/SmSKHWCfO7mRC8yHozYhpYXY6TZS0xx99qnF27lQntF+7P9WgXXUueLmSLzpES5hDY5ZlIjZl5EIkuNFUVak0p3erkyx0lE0aapSNNRMFuKmfVEzjpFFE5WIpfFLwFAOljx/9RHdGx6oXsjU999XswXMYJH945wgLKCnYL0S/3tGgU9ii9POSsWjh9zL1KzZ5xU/mGK55RVL9fIXjxAiIhoEengF5vgpCTnO73EvCj8S45C4CB5Ot3ULJ2eg6SBo7I2UptYB2ucuDi6Tu1i/16k73szG72QZisFF1XRqcyLbguOfOVOpjgvUgZvHPMTYT3zp5iTWJkku2GEOrm4jzInVqamzyOSU78YH9SxAehIhi77yenZ3B9hKbK3+6ZlIi7z7doCo7Li7xvx/muiI+SB5Ck7xZFyLkM0AspIqu/t8KhWiDgvrqlQi2fHUPLPxfnPc+AlL5ITVKrgo9k5pW06+/mzzgHqrLPOOuuss/3UrFes5XRrt+9svnUOkLDR7T30IEUnnEFShyWTMIyYnaVpuQ5FVMMe0EaIyxk3k8ItzRgH3yOxuWCNAtd1chUYy297ClXMtDFUBLBNGSBQKSPLi8wsK6IQbhoCENi4zE7LZoUyi2Zq9sj9AWQpqt6kumSOsr2MyC5yioXeQiQC/D2sK5M4FYfIEWeE3iYoKYMmxinrqNzIIQogh7OoL5qhgrYU8fA8Y0/rpVC6EwKYkptB2TvFBChHKTonxS3pmBJy5OWcgZX3t+R2pHpM4dMuONjIndKDlvlTJB+gleeoo1KY4UZ5n66B8wqU7W9h4OucXDQN/UmOD+9PRBEyoWSCUmhWXYp7TsB4zCUbGBQTigDpmeuRz9xVihDN4Y5IVWmv8u+xExLMYsHRMwnhJX5XihYpmyJP84oRKyuKjUaorBh7aD6vOSrHOj3rdLywX5XuI4IRnUjbrn0qCipqDOo6imA2Cj5Cma7S0PFCU0Q7FGmNUGeRri1HJ4X8hhf/UgTXaMdjW+s1Z2wW2rJ0An32TSukF1JobBxvjglK5gsZmwrhkohmuQZUK0QJSJFCV+oU1abnpFJZBi5fUxmRnJchOOfeDbICHm6872Alu4ckaNtBYDu1zgES1i5bmMU2d3po0KP05za9hGWIlKLTvvCsKOuVgo8DkXUpXM+OkM5vTCINKlLdLS2aYQyPDw2amFperyuUa3FAiGUFitFslXBA8JUGYhCoBHwiuD4ZZ0SEypkzIn6XKdzTBEb53WsBaRDpUXmh56Gy7cgxIg0S9Ky4Hj7BOuxjenYYlQc0XRsnORmpXxIPI6XeymKrKR1Wjn6pjzJulDgHKlhZCMJrySngHuVafAmRnoj1iWhuEpm+7ZF8gErFb3s5j2S6nIbX6SWkdNKwSZBrOhXnFNoIcVGasmsNlyhQYw0zCt97QnemoHNZB3PRTO0xQ7rVwukuk5PnhcA6OT26zfvTCseJf4/9ohuVVSoP/S7gJZegD6kynE9WkvPAjgRRT6op0vrUBEH2+4zTT/8XqdhSlsCz4y3GDXpkREHQncMw4rxpXiAKDxdr4WBmbQIIDbPQAB14QAB8aXhCFc49QcfAbH9K3hl/ZkOWnCUF7Z/1yN9yXrES9NgWGEbCDslUjNoSa5PgoYzrgu/JNpLS/UYBwxpSijWkSprUrKfxzmugXiI5BDWjASW5aBlHTjg1vG7pudyO10hjD336uL/RHDyzs5876xygzjrrrLPOOttPzXnN9fdu3fZdBGhn1jlAwlRloSqbZvyido+angkgrjYdS9eeCygqLYi0AuohOMKJmaRSCVrjgoKF5wiSL1KqpoQhiMhbbjj+DpfgMEV1hgQ7tVVitsuwgyA4isiUsomAmEItvCs4OWOiTwExQER4eEbZ6hmhRiDCQkTm5SqyKWIGIImzUaSnSenoxYZCEUR5UWwkiYCM/E0pvTJLi0PlIvKl89lwOCYyojdfjxYwMjMKYYbOda42rJAwSNFBOi8PlYricvtUKnTbA9f1knAYRWVMreHWCCot0EYIt2Yib+o/3WiYjXCMfuyrcg0oV8M61ZpHMYrCk5NUwJfJ9nIs1VJaQaRa96NEwTAVt21LCcHGXRUSVkq75efCI0VVRK269OkzdfO03wTxekl+FbAWRwFU2laSfZMwnxfL6H4SMLjsE4GfKBENoufHEIF3IiQUWiF0KMj4Zay7V65ZFOsxe7S2qSZVzO+X8gBea0BmfwFwvSKrPZhgV8XRIIqAOlljUELfFOiZEm6lSHiKJKYxbrJW8bq6SPA/7cxOTCYQSfdynyDVBiJDUPQRSwakZrR9JdL7xXUURHZ5PdxUpDEQvgmm96HQa9wuReXEmGjSGL8vrIPA9p51DpCw7EFFyG4wkU9Bsu+FsfyQt1YnOCHG77X2KIqkW9NG6MxZnTI0eERBqsaOxCWAdB5iFk6xalDtCIt7NwG9HVGWP5YSMCPHWL9ynl+ohga/UqEYpQGv5VR/gshSkUlXqSwDhDg8SVskvQB8Kfg8opq2YwdFc8YWmXKKF3ntAcpaUpjdlwfcJPJyap2k9Cf0MhH6KiOgjC91dgYxBR8xbCVgitgYN5lygERGGR+rTu3SwgllfkdMaS5GFnqSXlLM44jK4XZQoI3w5mSTQb0YeS9RTqFZClmGAECV78OGSmhTgT+1SCdX5GiLtO2CXq7r6Z6hz3KlCfAJAD1qgEZ4FUWCT+iTv2uTYCd2MpNGiitUBoGx70iOsKwAL7P2KPOrkZl6fiYNPsCIYRtXJa0Ur5HUi+V9LB2cKRgkc5BK6fjEe7/ngCq9GNk8eMIg9XD4uXaKU7rJMTQjhSK+sIt1AemNErQoy8/w5L/QrO/l49DtVRo3fKHZEaVj2b5GG8vSSKkLyc/iyVCVTwbIeXX9WPqkSqrt2aSEoG2roKgCe5uKL0vJB7rGpQV0vCfLdcHXY5Vwn0H5ZPJZzs5FOGvUMvZZBHQodYB4AtT3iZ+oIDy+NFllDakiqNWrZt85QJ3tPescoM4666yzzjrbT81hzzK5Oldt59Y5QFNmijxba2kQC2pGEcOeadHE2KrzCqNYMNPEmUJVtOhFPKRxhgtqtk5zLaFGRI0sRYhazbF/hnlqDUMk5w0JefjZLBaFhJ1JaI2Iozp9z6ZJglBKJkmDduDhhnHaRrOkKfI2N4FmSz7BOygEDEi6Hg34XHWrWO9D1sei3yXxU8IFmbYPRWjGiRzqddI6yuv8xO2blB3G8FSTttcThyISHc16OKgeTaBqSkXyiVRbGICiIgwFFWiXQvihXipQx5pw9ZKKn1Pimf243/ipe5azubQC3yd2YmAjtqUoGtYqjogV66LY6I5wLv2bHIr1eC7jNt0HAh5ql+J0eLmfMg8LBc+6RoKwLbSMUn05sUxm2Yj7jK8dQz5ewBw+63sAMI3L9K5kZhOZj+rkdpiGMq80nxtHfYoUDbJ9AZfNIfu7ChwRIH0v3bMoosifjH4olT8LQIj6tKwwbWYyBE2tknBfne5l2pWtEovbFQp6sHPRGUk691oI/jGpPi+eSxmEklIi+0AWU83IwGQ0RnmkmllCzd6IyCxFeJQVkBX1hazlZZG0lEjMUmS0ZVpIotZZEltVpCELwSRgKDVkt8VlMgFERJlJSyzoBMmIdPw6HZ3fh7bnQoi3fttfdOscIGGu1WhrIyTcHTs7A5WkP00c0YwCBoOwnApvDosGOj4lrddYM+Fp22jyiunTFhSN44ASX2xmrFHG8hjlahL+KkaJX8IZTm16Mm3PwFE2EWU49TUaqkY/kBXYw2fbT/wb13P8IlalQxkHfi2qPnO7BcxHnClnwefiWwVFyr51gq/ky5ALugpF5YxrM6dsh4QrZDIKq/lOZX6ELwJmsYCn1Fpe5rjIpBm1MOPgWemaMBsFNwyjru8Z2GEsertcoF6OGS+bI5S1BNSbYt8uWWAhZurEvjSl5SytnnFcUZtMK8+O8qQpuD8xMShWY3bMjrCstyOlBBejpBieHB2g3hQVxw8qU5YM8SZEavA0D8pPv3t9zo3KRBEx5aS2qQ2mni3fEjg86cWXQRbUfhLyLJNTI2E3Fp7saZHNl8o8yGKoEg5L9wbxepDEOY1Pjk+8XkVlGdouC8tFP70HHH0X3cQvTFE6RMouONnHhCBReQUvOHyiAHFYEPchtnfC0Wf0RvBc2CnqC1irSq1NUgEqu7as+E0cn1JsIycmxHEaIxW/HQuJBKFervzsccMYEO8JhpjleqJn9SzPKjPRLpmhmBUbnhNMSf3mmefpjZ8r0uqtYoizs59v6xygzjrrrLPOOttPbc9rgXURoJ1Z5wBJi+RFG0m3tjawTQ5bDasagypMbRbKGlUMX5DoV+uSABiQIkOltrBG5+taDUd1rGoDRJJyQbDXuuKoT7mOlFU09gwTcLFI71nXw/Y1RyQIcmkWEsHWDsDCeUywrVyq6WMcR8G0TmUSiLxtnUJbpxA/iKTcpFlriuqoLCIAhJC/JL9mOkCUWRX/76T+ihaICs3oSrG9QjZTc0X+CYgaTBvyuDSD1miGBCsZ6KmonasSwddWCjbqKjULIeIDAM1yhE4WLcwgnPCg16BXxWgSCxL6RIZHuifqeL+NRxXseoRX1w3KWNOq2p4gLo70+DSDtT2FepFm7KmPZAYh12yjDK1hgOGAQHj1Zk50oE2QJRPQx4oJ6BB11WTGjiQxU3SNyczWCzE6lWUoAYDXKfyUQT0mbZML1KXfp4U+Z3WucnKrF+KcKERpkBgV0sahoOKfU7AXFeDlT6tT0c9GJ6hyLIj7VLxTiEkaJj6Lfld5u6cFNbN6a2K57DeyrI6cTxHf9BzkYpCpuGvsllEq8qraRKyne6Bc9yjX0zWeJzwJIVaZIn5zcCUlyololZWjAcI9wokclcj6EyKU8+rjuQqpfFFF94DLEjjo2kv9rGnoS+k5bd5L5qDg5oWtdmP7zuZb5wBNm4/OCMJg0caX+0YcGNww3UwaHq3JvevWaYbNAGQKu3UUBqOXXF0XsKN4CUYaZoP4PpHPsZGymsp1LzK+LCtAk9lKc1ZRvaQxiVDMZHP4vVn2sAtxm2ELE4u0VgS9TD0jFOK1rU5ZWGuhrcWaRkX4fiuyemS2V4YH5MukUrTE2UM4P98mU52e3u+UZSFxHxRkAdG+NvGopBNGx2yV4EYplWWcsIlQOqepD1PGFhd+FU6EdRrjSTgIZRM6nyrXt61BQ/fBRkoN7kf4U6YBew3Uy+E7ObehveJT8CyAeF3i720faBbD92ZTfBEstygilNsrU5Zj0xhYKlIZX+JBQyG9JDnTjI4la6w1Poe7uGYWvWzEC33OS64d6OTEFSq7T+gzU52mvtCSmxQ/i/nQHjtCWvRhq3gHBO/awmBCEwQ1NSmgd3pcF7XmGmp6kpxEdnqmiqkmhzD+bpGcAuEIhN+IKBR/nrrnpWo5QFmcaRVqg3y7k3MgnRJlRSFcoa7O30dAtR5WJqenXGvz7ChWh03XnivXtw4yfT8Vb42fRoVi1EBM2Y/nQyKblZqFZ+M58DPKPEagWYwO/oJNPDuSKzFpBuEFhCavbTqnOcfcy9ZFgPaedT3TWWedddZZZ5390lkXARKmtpdQw5Jns4AIp8YqxqNWo471vTb6FYZVmBLRZMd6lWV7MWwkM74oG2piEkFYiIGxmN84kQKVFyJ+ZTFTXbwZKoa42oUEabSL8WQWLKqF0NZ+1cyU4XBOoY1wXNMUHJFQKyXK1bCc4DgzSjNBqd9CM3tnFLvWdkpgDgiZKEl0EZkYI8MQQteDI0tKkEIJShJ6I6oVmkAiu4bJnD610escPgHymbKMCEi9FCfEE7nGWc+nyA/PLi1UvJG8U2gpqhLvHe8AN0nRP9Y3orY6lcE3ROBVDhzhkREFGYVLek3hsx2EKBUQoj4Ef5ph6Liq3zIh23vFmlau0azxoseJwJ5FMuaK+KXZPt2/upU1qabqVSHAjzTjV0MS7vOZSF+WsQXk94783WB+tEgKfU5zi72AcBsFXRPkhxkLQoqev7OAafxdQoNmJKAihr28gIV9DgeDyN/xWCbxfrPaajKCIwOBHGWN/S6gLFem58NLyHBORA067ZehLAEjZqTg+L1ZMOBaNUhkd1M76JhRyUKOdcvfoRTcINzsTocb3fZMivqUQk9Kp34jmN2WKZHCWpXGCxlljp3YasMlVxgashJDVgz3olVcJmTGRm7+8r1gey6E2MU5dmadAySsWNcwTSKa+CKIZIXv8bNVnIY89gqT+BJz2UsjpSlLHJ18Dk0p4CLtWwnuBD+XJeAFh0eJzAMalOa9AIDkKNCL1XmgngTSSl30uDGceVYLCG5dYTHyTCT3yAhnjMxrlTkw4aDI6vEQVyalrSJXXqW+LWcVY70TTo8V8AsN9C34ZaCbBMmYSXIeaXCUNaL8lONFn1ntpykYxVaAi/wBV6Z2o/Dcn0pAigx/WsVOL0kBqLHhlOF5g6zyAl7KnMy0Dmc1CarStOJx+N0z18svWJh+lHSIsru9KneAxqRU7hR8rC1FNau8UemFL9rI/d4kwUItv7cufSeByEkToBAEGETHl6BuU+qWjnBxOxDnI17cmpy8HmAF3DXLARIdpxJ0xHWwfHIUdAOYKMxHzgucdC7iTkDco7RfPsSca5f6IvG3dONhmM9H/WOF6KNl8UxfqCSzEKGidqBhKeOzL516Oi/PTr8R0BuATCWb+i1DS7LzDc9/wbXhHMyI+Ifg/RCMKbMF4QQ0V8WJVSloAkbDUaHnmPJve0m1emfOWrrGov6XAaYVrFWbhBaVU9BxfPaGbhhkmXBzYXYB4ysPuPG+cyqcVxmv9NZs39l861zDzjrrrLPOOuvsl866CJCwZouFLR3PBAGkmT3rsGggwgJWEE454tKmOj9mnGYeukWmS0GfmVYNwUYxUqIqwNFMUmSLaFGVWYvIAEF3uknHpZmTFNFRM7PZuH/KkBqL7B1R8Zuhor7myvJWls2Q0SgxA08CdD5+OmCQtJY46uMUQNkzHJlSKTOlVVl2TFhRQFQl0C6m74Zm9ASFCZL2vAiPjKJJrRgZreJIYOW43IKEVIgI62oRraoVC60xlNTkUR0J4wFTUJbIYpFVy5NujVw3ZTNRlosqHZPey6rF5oWAtS5EJnuhHWenjZoSvSIcYN04jGNEq/YRmvCAihCvbqbuXyCWv4jn4JRQodVJ2I6IvA4c3UBpODpA0YAAfcRVjSD20qil0rXXDpyJltVpYzhEZVCTEs9VaKt4vlqgXAttrGKNtGLioER5CjalMM0xDVlcFMHxmfZNaEqqoaZ80kLiUjaNg7JUnyVFFb3TqT9jJCiPxiaYPBH486hxVt9uKtKh2jSGmDpBmSSiWYwt6zp5BRahpOvlZSafUcCAGmYAlDPtlTYj4DhIteMk9CdjGXN1gGSUVyYGMLkbnMXIJknzhRfEeZEZKMjyygNuNAcb3Uvm9hAC64QQd26dAySsf+A69MAxT8NZxS80L17CJAymbQqhM2egyQdXdjS8DIWHT2fSS9lpzIZuXfo953nMchhkbaq5g9vU/6WoIJDj/6FoqE7LxcuXfydFWSksJo8h+DM8EPHgqxhSActGImDv0bnkDK5J4mOYSQ4tUPuyvpiTXs8v5zm8BUA4IhZ8DWQqshLtBkM+Zn7KsLgG8nprkUlD58Iwx5zsHdsTirWFgOmK5JAR78j1PKt0q8qi6oVG9nvhjV4WFguRq1Zqi8290Ajqea086nhDiKsBW+qMwwaE5COmKZWahScbch4miqE93WjmCBUbHsU4ZhGOq6xf6bzlyw/IVYydgGeyT/GSY0fEi36mn524d0T2GnPZGp/qpY2Scna1PdVIY85Ka5GlbpMTZ27+ReO5yjEAQzCMQp5vDajWpYlLITKkjPjOfaByJ3R6XBBN8nLiYxMMx/Ia6xZlLLxqViZccJW9rtLA9aOg5lKVsk6j7EKzoBPMLeutyeskJl48ngnnM3P65Tx0itejLeZyn3Sd7wOIz0w5u24GEdMz1XMA8fkKBxMLo1JNyMJYGO1hNybYV7bn1eA7B2hn1vVMZ5111llnnXWW2bve9S4cddRR6Pf7OPbYY/G1r31tp+v+5Cc/we/8zu/grne9K7TWOOWUU2bWee9734uHPOQh2LJlC7Zs2YKHP/zh+Na3vrUXz+CWrYsACatHJbQqmNAMIEU1KHwgYBjVqlQHR5Z2ECJnRiynshUsCCiqWc/LSvJGZZkY86pkZ6JyZEqKyoVFtswJklkpCUzNsCWkUuSRHyBmPRH8kkVSBAFSwE1qirisawUwERFpVidqhDFZ04uZotAcyjO7YrumYC0p/kdtnSm1gTzClRFaebZMUT7Ps3jlVCZmR5DjvAiPhN44M2biWdPFCWIxibu1reJ+a4EZuA4AHJGwBynDb9ivccAwpBH2Y026YdFwxouM8BDs1U7NEHtFjAJoxzDZuB8u/qQtMNkcowBWs15UQxGiScGaQWqsc9FEigxRxfAp+IajhvI+lJHKKdh2OlNPRjdkcgEQ7ici8JZrSVOrXGnj5wR6JaZhrm/Aj8PKfhQ/rRW6maK/jIaK0RxVVbwMEUaE1hwZyoIztI1SKZpE0JpWYXnc3lfxWeklXJYJzMbBGIoKiX0RGVlEQgOJOUJ7aw7ljnDPFCvhGqu6TVGq0sANQjjH9SMRfaFEs5gEVpuFeO2pnM6CiAwXIkotEhnmlUxRLRe5z4jTDOlnETvP62kJxxHUp1JkhyKK2VggIrOZECrB6NDwNJYahZboDpFtX0eRWLexEyxvL5iFgt0DAaJbs+0nPvEJnHLKKXjXu96FBz/4wfjzP/9zPOYxj8EVV1yBww8/fGb9yWSCAw88EK95zWvw9re/fe4+L7nkEjz96U/Hr/3ar6Hf7+PMM8/EIx/5SFx++eU49NBDd7uNt4V1DpAwdW0fGPZTrU+NmUE3ywZp1Uz9ovDDnJ37tFwO/Nn25FjxeuJl1SYHqhinDAxKKQ71gxImr+jhp/B8lWoNZbwcwtlF5kzm9AwcXD8eQ9RHmlN3NZ2qA2zkiWBkoEcphTq0X8BKYnDLsoqEKFwGHc4ZdzjUXqWfM96A5ApInTZqA8M3yMQDZbo1ADidBkoZwpecElbztcI5nXdMl5SRSyFqWVI2zNCgiaKbzYJGG9sYPuN1rBIBhor1LvYmDHH1o/ddKJeUpl3BcFcVO7bSLTtDwyKsDwS4bGSpmG/MfLQF2lj7qnYGTUy9IvHPcVtgXIdtmsbwfdA6oCbVc8qEa3S6HvK6Sn9MQieCZwdEh1hOQGRG5RTEVWwA1Wo4WLViUe2Itdl2BM9VrY0AcnYmNTxhemRFAaVFwyRsRdlERfwcDIBe6ANfFfwM8vPsfXJUvE+TF0tkO3HDaMUQmy90JhRIvyvh4FCHSQ4fOd3FyMGMRVHcCOkR98ovVHA9yrozaAcJ2gKAdqjQkHr4gszuTFAsTYxmJhAkkcGK4mk8MELok2UVap8Kxk48C78yzN+KyaOAT12l2IkzXFcsKVjLDEDpaPPYWKl8Ujgtp2A8oAFFCuj7wH4WENjZZ5+Nk046Cc997nMBAOeccw6+9KUv4d3vfjfe8pa3zKx/5JFH4h3veAcA4P3vf//cfX7kIx/J/v/e974Xn/zkJ/GVr3wFJ5544m638bawDgLrrLPOOuuss19wW1lZyf4mk/k8prqucemll+KRj3xktvyRj3wkvvGNb9xm7dnY2EDTNNi6dettts/dtS4CJKx3rUJRqSw7J8FdaT2GNEQYlmc7chsRBZCVsWkbJUTQwj4SKRYIUQLaRlnPmimqcVAuDxe5yoQIBULkgkjMVC5hsqxQbwmbNEs+VXYuUsM91wLznPVQ9FoMIqmWogwEkYQmpxIfo1juYbxRAbEUQLHDsLAjCSlqWT5DRkcy2CxFUiS0MW2uBCzBUlZGz+ZEgHyeKZciarErNhzK9VysDwCcJJ6q1MepsbPtkpZlBYnsIK42P2p41k/aJMWgRLkQoaah4Vn4ZElzP1C72qU0jxkUDRYjm3dzGaIbPRHhcV5xVkgTIzny91JZmNhhjTOY+HBNN2IkaEczwEbU6amd4bpY9FlqB9OLhOuhRRkfFnnPTNqC909V1ZXyfB+R1U0hxDkN62/ZcSoXAkGQ5/tsPRF8U5kGi2ItRn3Wa6hRDAk04sEeBixHLS2maI3M+CKSc2Hg+jErbqlCO0zXCQj12KRwH0O/bSIdmygMWIxs0kVqUzSXTZSJsD3DERrev0rjStF6lKR3E49lxjbdy86DVBXbYQnbixBX1BGylWLhSVslOJYiPe1ARH0GU8RhAOg5zlhT2jM86q1KUb8IKaHWGW3AiAxUIGoWTQS8r1K7wjFTJt3OCNOex2EPH48l4WYplEh6Un4sM+hmoWeKLtnpTLK9aBa3DsaS2wPAYYcdli0/7bTTcPrpp8+sf/3118Nai4MOOihbftBBB+Gaa6651e2Ytle+8pU49NBD8fCHP/w22+fuWucACRte51EUXrxw/czLVy6bB8cEByhBH0oIwfF3UsK1Lg16reOsC15mpXegEhegKuBj2NpSVsbAoB2S06NTEdRYpLPeBLSb4/6HLYrK0m7DPrMx18PEgawqW/Ri/apN/cCRGBYNF3ndPuljvBEkqEdrYUDVN1QYXB923LsppRIbHtxcCkOL1FlZ94sdP+FQyhRrJwXX4rUxIutuHgQmM7uyIpRxoDWNR8ocS6H09DJUUDSqZrCWqHMlah2xkzqn2KOyHmoSG9C0QEx7VpE7otoWimSnXSXUqFNdJCqK6osSqzq+vJXPCvACwMA0WIrkpJ5uYOJJjn16/ClMPnYlw1mNN+wkEYTmvMJGS7CYzgq60vGNcIpIYFErz/sYlDE7zVmG0MZtStMhp6e1SXXdjgsg1qKrVjSff7UjbNPb4dDbHlP919rkVJDycOsCDgeE68L1yKj9JmVmFYbTzEmhuF2qMNkUeSDLOnMOZLp2OECaMOkmOdhVnAB47RnKapUCovhfXjsrbt+6dE9qlfhycSxBkyZJYYxJYwudN4sn9go05FQvJIhVqm1nMDhBQUJwk+Gu0sNXU9B4acW44plLaRvNde/IlICNlU19R23x2jOspVySGkiFTvNafXMVrGVG5lwYOvUlwW1y4pQ5QGIsUA6w9ZzBfy/ZbQWBXX311VheXublvV5vZ5sAQOKhRfPezyy7tXbmmWfiYx/7GC655BL0+/3bZJ+3xn6mENhb3vIW3O9+98PS0hJud7vb4YlPfCK+973vZet473H66afjkEMOwWAwwPHHH4/LL788W2cymeAlL3kJtm3bhoWFBfzP//k/8d///d/78lQ666yzzjrr7DY3Koa6J38AsLy8nP3tzAHatm0bjDEz0Z5rr712Jip0a+xtb3sb3vzmN+PCCy/Er/7qr+7x/vbEfqYRoL/927/FySefjPvd735o2xavec1r8MhHPhJXXHEFFhZCVOHMM8/E2WefjfPOOw93uctd8MY3vhGPeMQj8L3vfQ9LSyG8ccopp+Bzn/scPv7xj+OAAw7Aqaeeisc97nG49NJLYYy5uSZk1r/JoigsR2qUxwwsJS1ELFKoGwgzkyy7hWcTimECCquruoWq40xVZHGx1sewyiI9lrIxhjqFqgWxOZuVUqXyAe3Tc40bNzEcntakc1E4FDHSU5UtBhHuqoqWIRGyjbbEWh0OcMPKAurrwkF614f29a8H+jeG8+lvtyjWYlZRhHxgfdI80kkG35c6lTmgz0JlAngpRJ9C4jRD9XL2J6M9NBuv8+y5JIIXZ7UGQEkznDTbVj7NzOkAAZ6MM9/aQdN1jFEdNW6AcZxWOpdm90SkLYoEqRgD3wsnQdc76L9EuKNUqfRBX/E1lRk3pKu0ttrH90dhX//d3wwAWOxPsG0Q8KHNVcx0AlDECFHtCiY5185whGfSFhwNonvXe4W6Tc8U/c5BMqf5d+9SXTFTCEgxkpltY3KdLUmaBQCrOGOsHCmGUAnq6t/kUa3MQlx6IjR7yLQGSmqr5miQ6Ay4Yei3drFEvRyjPUtEBFYs/umExo0r8wyjcADwyOrFuvR8qm1GRHIgatalyLEWEeekVeSY0MxRH3EfwieoiO4dOyy5xlo7SLpNbV8Qh+dkgspECfq9HYiSKsZztNbTuAKDhmv1AT4V3QIm8T4iEnSTqAZe520IfZAnmMxEgAoZDUJmvJ14/uW+ZGQoHF9NURXCchkRlvQF5YC2mbp/foGsqioce+yxuOiii3DCCSfw8osuughPeMIT9mjfZ511Ft74xjfiS1/6Eu573/vuaVP32H6mDtAXv/jF7P8f+MAHcLvb3Q6XXnopHvrQh8J7j3POOQevec1r8KQnPQkA8MEPfhAHHXQQPvrRj+IFL3gBduzYgXPPPRd/8Rd/wVjihz/8YRx22GH48pe/jEc96lG71SavwS9BLx4Wzy9D+R+VhWRpeyfVV+nhtApTaAF8qaEiqO0KxWFxdnT6OonC9VWWjj6ToSSUi2V6MGeIjBWUqFfmYvjaxvC171k+LWs1RlGszloNO6GMrsi9WNMo18IByhEwoCKukX9QrTlUOwiOaGDWY8HYWgircZp9ARXfEFbLPiDeg0JLTk9P9IGs9cTOFCCz9vjake9RgOEj3SjO2EpcBAWvpYMUobFJ5AXVDnocHZxJE5wcIKgAEpekDst826ZMIu8TtDWIb9GigFsMnoxdqhjKlAUgOQtnqNAsRkhzMRU2bZfiNVywMMNwfIIgAGAS4aPRuMT1O4JEtjazA7dtE5TlrRLfZZZWfHmXFiYeQ0sFbErZrw0cvewaDRU5Ot4pzrYjB6cUWX3huYrfJUQhr6fIFgSC898s0v2ioJfjcV1/rjozveTMxLGjbWMftwONlhHHqdpSQC5GKdW4TZIjYERRC0dZZBNyhpQ4h+DkUWZU2iZJPyiRIaWzzKiwU2AePSST19iJvIWbEgzM6sgJx00KcsoDKKpltxM0iPl4VqUsyVE8V3GOrkznMA1fTe8/K9AqHU/B65EcTPpdZmymAr20zAuJkvTcS+6e7E+Y5DDtC/NQqXDrrdx+d+1lL3sZfvd3fxf3ve998aAHPQjvec97cNVVV+GFL3whAOBVr3oVfvSjH+FDH/oQb3PZZZcBANbW1nDdddfhsssuQ1VVOProowGEYMbrXvc6fPSjH8WRRx7JEabFxUUsLi7e6vPbE9uvOEA7dgRAn1jhP/jBD3DNNddkbPRer4fjjjsO3/jGN/CCF7wAl156KZqmydY55JBDcMwxx+Ab3/jGXAdoMplkDPiVlZW9dUqdddZZZ511dqtNwli3dvvdtac97Wm44YYbcMYZZ+AnP/kJjjnmGHzhC1/AEUccASAIH1511VXZNve5z334+6WXXoqPfvSjOOKII3DllVcCCMKKdV3jKU95SrbdzsjY+8L2GwfIe4+Xvexl+PVf/3Ucc8wxAMAe4jw2+g9/+ENep6oqbNmyZWadnTHW3/KWt+ANb3jDzPKVwwsURZHKTzQ+k8oHYm0sDoeC6xpRnaMg607QiE2kWOdYD4SExeygQLOQYC0iJXKdrZ7Q6+nJGZnPox5AmAFJsi9rpsRlFjBcIT6FyCmTSLkyW5fC8tUkifsVMdIT6oOJkDBrrkQ4bZz0RrSoH5QZQ0EaLpK7baV5Rk5ZT81CDvnIyu1AnIFzpobPZsMz4mtNLlToqWYbzyo913nQtUOxEaMqJBS3PgY2Qif40Rg+ktZ904JTTvjgQgCvqqAWA6SLpfDZbh6iPiCEHEYHmERaj9EdO0zwpR04+AhV+spBxe8FlbyoWlRRqt9oxzo8k0mMrDWG4QjfKs7EYRHPWoh7AlPT7Ni3ImJnY5TMKiQoJ0YxyrFK98tGTj5N5T7i/0X9u7Bg6lOna+tKAJSNFCeLarOImjQq3b8+RYuIgKwbn8rOFCZBqBT1KedHezIdGHFvZQKgLJIqIsMcmfVcsoRIw8p4eM6uSJAgldhBq3KoKF4nqZeTfk/t8gopIixGdgkJZWi2IPtOL8tMRle4JNAsmRgCtlIu9ZGpIaAk6pd0f7sKqX6dlFriSE66tvMEZ00j+0VA35xBi2xMT1ClvL9TlLmJj6o3qR4ZR2YjTGjr/ebVudfsRS96EV70ohfN/e28886bWebnJHtII0dof7L95iq++MUvxre//W383d/93cxvt4aNfnPrvOpVr8LLXvYy/v/KygoOO+wwNAshbEspk3CKM2bYARo5FCMhJkapzFlGT3zCjIYfULpsH/XW+MLbGkbVyWaFJg7mIcWU8PV4DoUX2Q3J6UHlgCKlmwIRgqARx6qsmCgQXhR6kgZVGjCK9bCs2EgviyA8Bj5vGRIOn4lrIC1lYCmG8VxlABXOOwvLs1K1gHoGKsE7/PJPLynbn03fD8JksX3SAfIQgnvxvMcaBQlErqX2Ul/AA8VGqgFlboikk5XV0NaVNbg6doxweJQxUEUs9hghLrW8CL85cNTqA4aYbI0SAVtipt6yyNBb9rBL8T7qx8/CcwMlH0JpQOnE2wIQeDlNdCJdiSY6Pi4u842CilybAK/EF+pGWFZsJMdQCb6E8omXIx1tP++5or60wslsvHAElOCjhU/bl/CKKEJJL8MiOQ+qdHyd+UaaaOiNsJEZJbE7I5z2RnBKZHYPOziyaO8cWIudaCHIKeUUJARFL1FZP8+5BA8SV4YLbNL2BJdJngo5VQ6ZoyEdgNA+4YCoBGvRZMgLxw5mJ/Cind2XQeojgt0485L6YEZROd+nbFfK8oqrGaQ0eoP0XHNfChjRe9aG5PGsUZSoB18AmhLpJJTKF1w0T89SEWT7XJk7v3ML3baA3XelwIJ0xUyjd2/7zubbfiGE+JKXvAR/9Vd/hYsvvhh3uMMdePnBBx8MADfLRj/44INR1zVuuummna4zbb1eb4YR31lnnXXWWWf7m9lYDX5P/jqbbz/TCJD3Hi95yUvwmc98BpdccgmOOuqo7PejjjoKBx98MC666CLGF+u6xt/+7d/irW99KwDg2GOPRVmWuOiii/DUpz4VQMAnv/vd7+LMM8/crfYoi1Q+evo3EVbVddS6GLcp8mMJoijhYzZJvbWP0bbQxeOtGvWmsCqFWO3QwxEJuXQhggEkt1T5lDmGFO0xpeNK3/2oqSIn5bU1iQC7GqMvO0qGKYqNIBZH3wGg3EilGXTrs1A2GU8kFOBBIeuUpWWjoJqUkHfFvBpnOYk50xmJUTDSG/F9CxWhA1NZVDHqQfoyhUllHrwPWUhAJG83RPqO0SgFtLFzda2AeO4U+Rpc36D6aYj6qBt2wO0I3DBfp7IImuo9aZWIzf0esCk40faAENIbH9jHWET66uhj18vx/BYdMIgwYWUx7IdjDHp0PX0GZbUxqoOJ5my+jIdp08yYZsmlINXO00EhCKHcAKoVH/vCZwJ0CYbYSXg7Rj04yiBuRGfA5TraHph4rMVsmxMjSxHdG5AmUqrGDYCjmq4l+DQ9P/A61cfSKdIgM7Oye3lK4M72wAR4SfbNalNR9KXO9zsNG3klIilNikxx2Y42wXEhEpOepbAwRyF1zFjTjcgUsyliwlmSRqXomsgOZZ0fGQ0Sx6C2mEmekTZTN28KAuSIl6i6LivAy4gbWabJ1aYIDd82JMbqFKguYBaVbFMfZTUKh5ixee3OSNJzomHTUGd2nQEmqHuhn9nZz6/9TB2gk08+GR/96Efx2c9+FktLSxzp2bRpEwaDAZRSOOWUU/DmN78Zd77znXHnO98Zb37zmzEcDvE7v/M7vO5JJ52EU089FQcccAC2bt2Kl7/85bjnPe+52wqTnsLg9IxZwJGCqHjJexXhjoWSH27KWmoWE5+jXkwwR7voYYe5cJgqLQ/wVdXyy4+E7Ix2/KIHkrDdsGhQmfjyjCPD9kkf28cBY9hx0xDq+vAGGF4XX4arQLkeX3ITmQmRPrNUfJnVZgj/jsuKPNw/TxAybSPggDkDpaum0mypjwYxDX/QoiwF18Xk6c1KeVYTtl6hiSnYNQpYelHSQNoohvx624HhT8Oxhj8JnkB17RqwGj3DtoVeiKPqIpEZDFDFGk+LA9jl0N/1pgr1JioSSWKUoV4SELK27CA6PgtRAXixweJi4BMNyhZFPC/KwKpbw+JxzmqG8/SGSVAmw5tT3IhpmMRhriMglYlZMXnVolgNb0E9rpMoJ22uFIsEhtpU0bnsRb5Rz/CzgEKL5yZlVrGacB/8TNihg+rFvoncJq196oNWw1H6PUMrnl+YAR5Lb655YqXz+DySKwMB802rjmeZTh55htFUfxeT5EQWI5eetUwBnt64np1Iru8lso+UExOuxqb0fqEET9fAVwVnE9JkxPZ1+t5L4oG2TM4SOwqyC0UfSHFRN2dik2WWCcdSLpeZaDOmhLMlBBOn1DfivlJWlpq3r4yclTy4zPESBVmB6NDS9VY5PMoOo7g3XAG4Oar0e8s6CGzv2c/UAXr3u98NADj++OOz5R/4wAfw7Gc/GwDwile8AqPRCC960Ytw00034QEPeAAuvPBC1gACgLe//e0oigJPfepTMRqN8LCHPQznnXfebmkAASlywbUIe4n8NtkUBymlMwJylo4NwPWmohjDyBcaJAeHPntFi8VYbXtbfx2byvBClGUL2jhiWK8wjoy82hW4MTo7N6yHt+zK9Ysofxp+X7wOqAJtBcUoRo1E9fHQ9vRiAgIJO4vwCN2NNNAJp0dYRsSO/2fuhyC6ZoOjiApZ4gKUnsm+RnCcaHZonWL1Yuoj6xTaqCbcWo1mnJSD9XpYXq2GBlQ3BbVvABhc16J3XQj9mO3R6RmJCofDAfwwpqlvCY7Q5IAKk+joSHK2K9JLne6DwFeKyyoHHwvKGnLsesmJdV5how7ewUaUH/AAK+kCgJKcJ58cHyDwXbgyfZ0KStKL14jIga69SAenTwuzETV01idQqxupP+hFHUnzqt8D+jGqqCsm9tPL25UiIlilWbqtwrMR+iMu6yVdGVU4dlY42iUGbt+qUDwVYE6XJOWWqynSQmT90Mb0KV/4tJxUAbxO5QaUUBSfFwH1RfLBtGX/WjwHiWfiCpNSpnmZmutIkEll4lDBfTa5QMlSLVWSz7BRVVqqPEu9H365i7FrrlMy5TSEZcKnkBEeuud7YRIDAL7ygns0Z4akkJxTjzRJyUjaKdrDPD6WBxDfp4j26ZqnCBU7/XXSkSpI/VkoUbdZsglmnETlAbh97ABBc/maW7t9Z/PtZw6B3ZIppXD66affbJpcv9/HO9/5Trzzne+8DVvXWWedddZZZ539otp+kwW2P5gderiFxEXwGlMhdsTpY9xA+m8c7nU8W9eVRX8QpqXLgwkOIDXeXpiiLpgaC3EasqXYQBmnNE2cmk1cgRsiYei68Sb8eDUQSW7csQi7I0yji+1h6rJwU+L1mHEqKihnn8wFMCpFKkj0UYSppWX1cORseirqk22zk4irnxMBysXVwLN8x2n6joUlnUoNrOOMsWkK5vrYUQET60X1dyj0bwzr9m+IUZ/rW1TbQ3/rlTHUpM7bt3UZ7eYQ1plsrThja7I5wlLLQLsQI099l2fo0T4iX0n3LHrDEFUZ9muuf0URPy2Kf47bcqYQqHeKITzX6sD9QchkK1cTrAmEmayM9HFkhyJAE8cyDVKagdSr0Vgo4jlJUceiSHIFZbxh+hVcL3x3wxLtQqyVtZAiD1KlXKoJT6v5Ko9UqXFs0r0kowDxOps2iekR9EezcSBGBATkNw1LhQPHzzn8FClumLVV8leIr1Qk+MWKe524NG0TuD90fG4vtdX7FJ0QUJNM204QmegKo4CYXYmeuF84EqNTlFVmpMnnFuL7FA9qmh8zEwGCiOKWQm1+SPCuA6Igp+lZVBReUyliqwWkT3CvdTqpg8eCt96mZcoqOOKzUQbjWMELCDjL0BNRIgB5PbbVBPVT1LLtqxTBlSKXsjYaZZo6BSi/S5P328qsV7B7AGPtyba/6NY5QMLswRMUS4FYCwR+yUzlYKQH11nN2dC0TAkcvSgt67Ns6o/Y8VmKT2MpRpY124OOb4tJfOqunyziB6tBFPInN2yCvSGMOMWaQZ80eYjIO8l5C5w6TsUFVXJ6nFS1FQ97OsGc8zAtOT+N9U/30bSqrySS0jIyDXDKr3IKPr68/Sg6NaZAy86WhH/iQDhS6Mds9XIF6G8P2/dvalGsRk7VWlKiJk6LLw3aLZsBAPWW0O/jzYahzno56c2Q0+N6jvlbKByT0nXhUFVhtF0aBAfnoIVVbIsNO7Baw6aIywwjTmOhsBZH3ZuaIW6sg6N7/SR8Xru+iO0rkYM0Nihuio6dKDPCRWYnTsBdouhubfmTS3RM2uTgNMLpkYV3iYxbVQnuIjmHxT7sApWJKFAvE+cpvkwGgtS+E6ea7q1iXcHENPbcKfZpmU6OwGwRS0HabZBpxcgXIhCcQUk8ntYksm3iKCkrnCHhwHGKuQbfy7oRGjNMIJ7iZJFjI9sd70MzSTITZKEQb1zX+nQ9xfcEw6vMmSLLUurFs0in4H1yEGQR1+wZjsZwXS9Bve3Qw0U+G/pR9b2f8/UKLT3ZdNxpa5xJ3L14Du0E8DZ5mVpIWQAR9o39nWn/CGenXAsHK9cdq767Qgm9NXHPSk6i0Fvj0h9E0Dc+kO9H+44F3XGA9p51DlBnnXXWWWed7afm97AavN+DbX/RrXOAhB2wdQ3FQoqZew+uwUKZRk1r0EZyqlIeLYnNUVHHRjExczIqUK+FKeTKygA/jNktCzFKsFRNGBopVErnXm3CrHvHqI+1tQDJtOsFNBcaFCF0grK8yFgYpPCurBk2l+Qs0laZIKnAYm3Ki1m8mL0pQVqUQmpADEm7tExNkRWnC5XKCFHaLx1fZoUoMdOjLBuLcj0SzUcWehKLktZtYrOTAvdCD81ylChYMmiH8ZrGGWEzFFl7CylDyUUCs+63KKNQ4aBfY6kf4cveiIuMHtQPuNSh1U3YWoQI0FazxgVlx7GTt9sFlCJktz1KQK/GIrMrawPYG8P33vUGwyiFtfhji+qmMPWlGmWqdSk7yNoQ5QGANi5rGoBVqxPE5a0odBcJp6osoSL5G4M+3FKY8tvF0JZmucSEoj7LGnXsLxL0tH0IgbsUQZWp4VLlWEZKFCOSAr4hcvW0iB+ATCRQRl1aeU/Ohhy8yrOZwqcgsPdSVCQrtCugJE7bbgXUIpTUpQhgKq4ZP8cOxUaMADWOo0lEZnbcA+EL9wHScbOEBvFcczFU+ayLCK6MfE0XO82SF0owFYClOnoOioQptYMhqD9GzLVxHAlvrebxTKvZa2BFtpdzmovmsnTFuIBZCxehWFeJFE7K3lPRvyJGjss1xxmNBPt6oxiinSxrNAsUAYpt6c/PTPSLLUyMbhUxwlsYh17Zwm5McPXMWXX282adAyTsmAN+gv5iiSaOAq0znHlFFbI32hLj+L21GuMmKvxOwmeDMmmyjNOI4tcKjE2EWkx4wVwvsyNUymiB+GQ9EZuqJ8OlzBEp5c9aFoIeMC/66Yuks+MG8QB9yyUWlOC0OKtSVWcqoTDR0FxiIJU+YCVeIdmvG3D4mULWZuxQjEjbJO8Dx3UpqLHCGXLphcaV2FtRDdt61oJxCz0uOdJS6ZGp4rL8PSJNzZLg+CxZqMhnWFwMo+/yYMxV1Q8arGJrJF1tKkZYjJ2wOWKSS3rEDk7jDa5uDgAAXNsEHte19RJ2NOE+uHa0iGtWwvLV7aEx+sYSgxtD+3o3AYPrwjkW65ZLrZDTo+o2FWZtbUpRaZNTxN8BdgizW6MkTaM+ENP/7aYhms3hBqtjodHg9ESHcSH1nSUeSM8HpXIgqHVLfhRxaKSeDzlDNjlG/JllBAmILFFLEqwqVvVKLNAETad2qAxOS9wOgrJ8mzK76D4mJ4G2JwfK9nNnH4gw0zyExKf7mJTQ29IIzZ7k+CXdm9RGbVMRTscq4SKlvgVriXHZD+FoeIVMiiCNHXMyt0rHUCRzGrXjQrhzxcC94jlS05pUVHdOVwApy9H7UK4FANw4fOqRTir160BJMHeU8qjWPMroRBZrLUxU5FeNhY8yDe1SONlmySSnfVPS5GqW4j078MzdQ+k5NbAaNhhGDufmyDnY0h9huRyjXqvx7Z2c121tFoozFG/t9p3Nt84B6qyzzjrrrLP91JzfMx6P25kH2lnnAEn7tU3/gS1L6W5Zdz3ssGGKu8PG2Xq9hNUmxEs3bIVxG7pw+yT8vtrvYX09/N5uFFAie8eMpm5ir3LYaA6ZkpZl4WkhHpipsDKs5QW0JeAIgrJ6jrPbTITlytJmGRp1VJK2zqTpsE0zdCXSX6azSSTkrK3njLRyLYrtrbXQYwHfkBKuUawrM9e0StBe/Awib7NCJr4Q5NAizawJ8mgHiuGu9OngFyOhc7HG5sUQzbndQph+HjrcgUP62wEAd6huxKYY7TFwHO0ZR0xy7Cusx37bYYe4ahIiQFePtgAAbpwMceNGuLdWRn1MdoR7Ru8I/V7tUJzl1dvhudCs8j4RVSkCNGlThKdtU6glwl4ZwRlgBWsq1oqi4AgQ+j24qGRuhwWLGnJmVyXI9OI+ZBXn0oU/ICPVepvuGYbARNHPcE8hLo+bOwGfilpcHBWd5MVOU5aVOFfWsBHq5gKiShCrT/Brm8NCQHy+ZOKAhI0kBEXtltAcP8OxD/tAyxo3ouBmIaJVxHVuE3Tm2xR5UiJalO7z9J1JvUYlGFJoIQUxUs/bARGmlNeLIjj0BjWK4f/MmHAtxgSZCNFqzvaj5AWIAqfKKlRCZRsIWk5Spb5aiVGZ1Zhltp5U+FWbCk37fsEw92RTVOHfojnqM9kKNAfEfWwKB1sc1Dz2GZ3U90vtsBCzNg8eRmi7vx2H9m7CWLf4+GxPdPZzZp0DJKzxBlrVMDGuvmQc+jEGXsaReMNW7ADdMBqyEOHqjsibWC9g1sLDOFhXWfXxm+PCzIsTT2disUBXL4WyCYJwJWAHlOHkOTODHZ3KwlB2m4AlGCnQjrPfvFcoY/aa1h6tprB7DFN7QEVOVCjCmp+XtkKkbwKYccpWAqLT40QqthMvdxrISG24NFwt3pUmwQSl5n6hzBjlvMi48Vnx1dBvOexFJUmapXjeyy36S2HAO2BpHXdY3AEAOGrh+vDZuw6HVTcAADbrDTTxglgoXNdGCMuFe2PVDrARSRbXN4u4fhJIMtvrcJ/cNBpix0ZUkt4ogZgNJcstEO9BufRytH2TVLZ7tI1jGFCqBStKc29tgl+USqntJvU1qwmXBp4cI6VSEWBKra88O0O6ATTxSLhEgWaoFF46O0iZPKJoaVbRm2Aj8XzQ9QyZU/EcmHOTyreYxjE86sqkQO1FmQheZtI9wTaVZUZwLTscPp9JZ7w4wbEBkAktQqdnlZ4T55JTAiQpCs7sgqgm74BGZHnRMaRKs5oDt0n19bkOqyg746uUaZdKPiiG8TRxHiW3yd28UKJUy9aNEOqUmXpyO3JqYzZjueFRRG5fMbYwMeuKik+rVpy0MXCDSEtYLFEvJ8cHACZbgMkBYb/NAS2WtwXo+g6btwMADh6sYGAS97OO3qP1ijN1Sa5kaGo03qDZh2nwbg9J0Huy7S+6dQ5QZ5111llnne2n5qA4GefWbt/ZfOscIGE/rrfghlGJJk771mwP14w3AQB+tB7JqzuWUK+GKZ3eXqLaHm6upZWwj2otzF4AoNiwUwVGZ2cNTkA6nAUiMlNSVobisLnUMeEZl9As8pWDjqUkKGup32vQK1ImA4Wy6Vyb1nCmm1YeVdTz8IVCYyiDKbZ1YnhmL0PVWTkG0qWpRQkOJmnrFM1aEOps0jiUrzgiQbN5AEnMzyGRgq3jmaQ3Cr5veB8A0Aw1JpvD75MDPJotYYbZ2xzCdLfbvIo7LYcIz10WfoqjetcBAA4rwrJDi3VGEY0CrovYxarrM/RF9pN6E346CffMStNnEv0ofk5aI6ADMGzkYiTG9sAZVq5QKIgc2niYGGHhqEzjRW03x33D5Fgl4EABI3JXty61RSEVFS2kHk0879rDjCmqkvZBy5RLUJaEf2TpAopymUaINk5ENGdCBHnLUKke14noTfpFfiosI/SLWLdoITB926US7TDe65VOGUCViAoJCIt3KRLlqN1+wpVBwnM4Bb0pi3SvF0BLER56PmX3S5hQRJA4GiSjSWIduf10KYhsX7J+VwnWs/GF50xPeR9SlM5s6CzLij4zGHLO+fB45JDpLqV90D0LjtjJ6F6KOObinaAxhMZL8erypeGIsOvplNUZMxPrTUC7OTSmv3mMg5fDYH2XpWsBAPdc+G8cVIRo77IeM2nYes3P9fZIhdiItIguqvKLYZ0DJOxbNx6B7TdtxepGGB2bcQG/Fh6AYiXc8NVNCgvhWUG14hMmPREvIPnC93MG6GheKZFNkRRKFQ9iGvVCyriJNKSQasy8gRTGlkUNfXRmKAtEZmWgAQuP0e/Wai5EaAqXUltFsVEuVCgGXJkGL4tBJmViBxUHNXYAleA7ZDCgyGiJfVFMXIJvWgcwvBMPZl2Cchb6aLaGThofUGG0VcfvYdXxgR5uWxjVD9i2ijsshQt55EJwcI7s34BDypsAAIeVN+DAiF8eGMuXL+oF6PhmWnEj2HjCjTfYagJPiLK9Jq7IiIvFnMqOBEUq4+Ep1Tg6N+FapxcnvdCKkZQCCJ+uEI60TjnaScVWQD4KM5mBGdSUCQYKGJEcoSapjCuv4KP6ONcXq9PvpnZpAlA7dlRZqmBcp5T9ugZImTtCor5pQwYbAN+2iYtC7S4LLk6ryjJwmQCgFTAg7UsrFr5rBkn5VzoU82Alqd5MJpXU56lGZynmBrnIKPJ1pyuVTy/La/EJbhKlqJepXapNgoHUblf4xF2qHLKyUHRLWvEs0vay1pZIIJTyANPnoGy6J2WmHEy6FzVlvLUCHrWeuUFaCBaaOCHUtYaekKhngtEpy7NZLDHZEtPcN2tMAs0Oky1hX+3mFr3NYXa2dXEDy5HXsxhnbENd49BiOwDgYJOgtbH3uCYOuqsuZu62S7h6vAX1KFeR35vWKUHvPescoM4666yzzjrbT63jAO096xwgYf/54wOBdhlmPdww5VgIcMXSE9UaOCOht92iXAszBprVyugIEXl3Zsp7hndcpZPg2SDqWAjpeSnW1Q7nSLRXDioSl6t+i6II36nyvPcKkyaWMKhT/SyK6nhBzPRWQZnUdk8ZX6MIw9Q61Via0moBwqyVZnzKCVETQyTYFN5WLsEYkhxNJStgbYqoiWiPWw4zsmbzAONtYYq7fpDGeFtYtd7q4JfDxauiuOXtFjdwaIz6HNRfwcFVCIVvi+lWm80GhpGVa73GjVEoZTXq6pQqVYtvvMaqCxjVuq+YBE3EZ608EydbrzG2kZgZdaMmdYkmVj33VsG3KUIDROhCEFZpNi7F7CxJ+WuV1YCSBFwgh1F2VpqCM7Ts/KiHF8IvtNyISF+1GvtotYFeD7NjPa6BcXyABBGbw55GKAoWRao3xno5Pun4aM24ExG2Xa/kKIDtFxxVzMQDRdYfEeBdJaJgknDNEJ1PmlW1iPyIDCorhEBnqqoLCEsulxE5iOsxDb1lUVGItmqwrtK8GmXhBzpmyvBiqEvAbZmpdF1YxFIDEfVJxzcpIqjalMWlJzFyPBH79+kcnShzIu/JdHgFxbBo3Nc4UQnMRMPUdIPHfRYyoUGhEVperO+zGCHVQYsyjodaeY7M3hQzSH5YH8CJL6vFDi5X03iNK5sDAQCXbxwKAPjuyiG4emUz7MZkTkd29vNmnQMkrPzPAXrWzM1UoJC4EYOjbj10zE7Qo/BAqNpyCB/AFARGacdpIKfCkigUD/ZevMw4HC5D4QWScFdUKTaVRRWdnUGvQWVE3BpAbQ0rslI2GAC0kzRq+0Y6Q/QSUknYMdbnKjZSinYxTjV3JLeDQ+HWp+wdgkAal6Au59nZUU6ks1aUBVZxFli7WGC8ORYQ3RraNNkK1Fsif2apQbEQLs6mhQkWqvB9WIbPvmnRj9ketSuwEsPbRBJcs330YsMNHGd5kVMzdiVG8fuGLTGKHKCxLbHRRoXp6ImsN1UaaNeHGI/DunYjXu+xZqdDt4oHdiqcaSai1lGDNPAboFkkuAtszAlzaV1OaZ56IaeN4no2f4lyWjQSByipGadrayYeBXPcCGoCZ+S4QQmohexYYWdz2jK9DhJ8Ff6j+D6wJHA5NOkl2FM5nDWVXelNcoaygpmyD2zip+hpBWmxDRTg2+Q00PkQQizboS1AfrOSjpKYLEzLR0ihRniwyoM1AlojyLRyQpVascgk96XxedHeOTAbm1g2TztGqTQZUk1y2tnBKxO0OOPATTngXrQrcITidRTQ44RgsYkSY27apxSDlcVMp3lSrjWYxMnGdgwY0qdJyWrTxzVV4Hr2dIsm7uDGegH/vR45oDdtBgCMbhyg2G7gxmPsK3PYw1pgHQl6p9Y5QJ111llnnXW2n5rfwyww3zlAO7XOARLWuwmo/FQGk9AcoU+uvD22UJGkqYgUNxrDT2I0SOkkNleWqbI2ZdlozbAQANazId2cohA1v0T4WlkFG2c0BIXZ0mAjEkJHhUuzOZoBC+FCGdUh0qOWpTbEdloK1HEpiyRAV2xAEJ5FvzVzSOFc3wspMqYVwxhQBWyPZvdRun7JcMRjsgzUm8OqTczqUMsN+sMY6enXWO7FkhS9MUd7KPLVOoOWIjReYRJngD9FgLIcFJc+GbcF1psw3V6vY9SnrngmaWvD9d8wNtBR8JJD+bVieMXUqYg89WGmmQLMZFvJaEgGhwnSLYtkGkFEb5GuPWXEFTmMwttNHxOzUSXeL5VWqBWTnKFUEg+MWYVO3M9ZZqMBpusn7GxSO6d0FGyVizHSMpk5xcHSLOuIPj2K+HxJwT++z0UfKOdnqsnL+nnTJmtt0WcGUU2R1pEkZ8L6BK2Ja5zX5YvLK5+SHqhMRZkSFtADl5eQncvffP4fqQfGS2+mfhdEv/me4jGlZRJ1GleygmY+tSeVDRHjjU+wrafaa8bzNpmOEEVFnYw8SV00DzeMY0MsZVP0LFep75UtTBQ9JBHba9wSrl7bDABYmfSxPo4lNMYFXCzRgbWwbm+HRrkK2Mm+cyq6avB7zzoHSJjyUVwtOjjFyKeaVTWl6FqolhR4bRLkMvFB6fVCdgoQBnxabgw85c6WlNKTRkndeoDEvuJLOgi9xaygkUp1fKQaL9cRMnOVmPncnOAaeBGKpsG7mF0fwNz05Tzbw6cXmxhcab/OKHby5nEVwv7oLQRWHq6XItS1RWEcYHjUWxz8cmhMRVDXcITFXqzVVU2wWIbvm8pUi4tIgCttHzdOYjprW2I9Fh6lgXBSF2jigNdOCvjIk1JUA61RDFEVtQjLT1KRRiNShxn6k+dKTokYk3KnI/FUqFaT7aXvvsjTz8NOxctV9jFLKHgBn87xulyC3rRFVttNprHTedE5FhuzhSeV93AkUin4SK5SmbzDPOPmiLRxPlfpgKh8/XBc+XJNjhtnVmZp2ZLbFD+N4FEZlRw5Ad1wW7R4fkR7pBJ0lv4/pWAtnQNv0stbTkB41Qqs5q4soKlQLGVmNgrOzHFWuGOk8yucHqWySZFcP+xfeJS3AF9SrTA4hPpvQFSSjisIiJdhXwlrNbmDDiBTsIYcr+T9MHX9aV8EAxK/USnPHKBekRygOk6AViYF1kex2G9jeHLpG8XPPkF0Geexs5976xygzjrrrLPOOttPrcsC23vWOUDC2h5gSlGBfSIJhOEjZHDEiIBxKeRLOismZaugMKmkgzFJxEuUeyABL9szqe4Shfj7mmfLtlT57GfePS0F2WYIpekcZDYIk0CnpOmzaM+Uzo+yIrrhxMy4J86P9utEDaY5mS1eQ9T1SlotNYmYbQGaTZHwuWBRDojoHaaPg6rBYiQ5D4ua9XbW2x7a2ElEUN5oS6zFqM+oKTGaxIysUSTt1gaIpHA9UVBMSBaheFHapBjRd89Vqgsq99H4PNJAUCZF7IwI+xvFUY+Wsvt0qhHVDoB2ORK9+y5UrAb44nmrQr0lIMy2qcNpZl56LokSptOx82NWn2oUEM9Vifpz8tpK9CrdDwquEvAHALj82qYOyCM0qV+oD4QAqIB/5L0zLaUU7p3Z77L8g6pSZCvd3ykikWXdySQDPlnMmgOobJ4WoodkIatOtptwo9ldeVFSwor+ofabiYjyGnm+01HXECma0RyS/e6QhYFT7cD4aXyKNmUlbgjmnG1/dl5iXAl1yfzMcjnuyCizEeRmANCFuA/0nEw7P3s/hH0lLSTKdLVWw8WIWW0Ni8DWNuysbnLNLkWQotMzES9vYkR2J12xN6yDwPaedQ7QtMl7RYl6NxRKbz10FFmDyBTxVezK0kDWs3I94uoYtKRM3Itp7n2VnJ2eSuq0nO6+E7hAOEDJKRIvNi/C7jL8LgbtGVjLIRN/y7goU5CDN4BTaVBUXHA1/C55IMqrmRdiVsDRJHjHlTnsA0R4gF6utUYzCh0yErhSEweysS1QxDdT6zTaOOiR2rX3ijNAlPLQsd2kmu1qk4nDGYKFGPIJMggAUK16hn+KDZtUa0mFubUilV+8+aJD7IR6bTswsIPYRkW/J6jJb4DvqRaAo5uSkNbCw8eGKy15GLGPLVh0zss6cPSy0kjCeoJM4zWywrz0ybyaFsxbS2rcPmW5TxIXzRsFS2q9FUFhSaFa8l4cKUpMKVbLtOqwT/F9Ou18eswXDpR06iV3qhCZi9McoZvNXJu+v+dIBkhTTsCibXJEWNiyVCnlvkT2fUZAUWSnqVbNcMgyoVIrYMZCZYVRp5d5PcdhlU5HBq3NnCJsla6z7Yl7TjgtmYMzDQ0rMbZhFvY1deoD1YrzVUhijlE93Wpgw9MEQzMcRmNB0xjYmGbvrU617BoFzRBYPNV2vjPe2c+ndQ5QZ5111llnne2n1tUC23vWOUDSYmSFojLtAJzdwuZETS+l4HsU1YnZQT0DOyAoS/Msx5Yy2hOX9RMB0pXIZl8AZrJJslkdfSdtEq+yGRsJN7Kg2yRlt0mCbjp3NRWh4d2KCBCRmf1UWD31H7U71ThLy/k0psiLrHcjIlNUX8wVgI1aL82CQRsjGXXciQxvW6dRFUnKfjqTRSmPIuojFcaijNEiIkWue4WWM7vSzJcyUIpR0jyqVixXqzbjFjrWqaIK7Kq2XMYhHjx8RoK8KjRHDZUts3Ig8ajccRLegdNwk5xM7EsBM3pkpQ3od7IsA0pmBooSCiz+uQGU8TpQ5lex4RnmMyMHE0sUJI0nC7Sz02NfGpio40OZYZJs7I1iCJhgUFemmX+IFsV90bIpCExGYrK6WnSO4vlx0+Tu6Urn07DtTrL2MptDGs6gJH5OfMJQRKSDIcDpLDK6D93scZUVpPSRRzmiSDWdl8+J3hFOs+VsxDYbbyRsRaelMVfcUI4bkoycSOH52AIgK+GRQfKsjYUM0tfzIi4yosfClYmwTMR+2yjYmKU5GhrUlah5gpjRyYkOCb6HVywiSTCSNgDq2b7Zm9ZBYHvPOgdI2OB6j6JIL3dtvXhg09udBNnkchrU20HisbR9lcE7Utk3/J5eTl6JB5cGrzofdHcmXEcma3JNp40Wk7zwJAm9MQRRJGfMT6UrpwFcclrSZ0rrp4ZMOW40EGa8iLRP7m+FDBKhfpkbbhaDFGH6rdPQEQ4z2qGhTJk4ANTWsIKvF4NC3SRF5ixji5yxyPuRsJeZOBb/g08QKKkUYyD6y3tgGgpRitcNLw5yTsNnue6g48vA1Ap2Iw7mVXphsShemc7FmxzWDF2UzssKVWmZLi9f9CXV92pEYV9ygMYOxXp08lrHxUq59labHFBozXw37gckOBnOw1g6L5071fGTEwwdAv4HQWVzgBd0jSxjS/qRyJ0iV6Rzl7whVkzWcxwc6UgJWEkJmQglXtjzoGfe3qps3ek0eiCHqIkfI4usyjGKZCjK9aSwng4mIOhCwZGj20tZeVqqSwsnkn6fNzFyBZIzRI54MbWudEJ1Gud4n3QhBedQ1h+b7le5PeT2YozQdeLp+SjW6sokoulKMwMjFsLpV43McM3bQ21RFrD7rhRYZ3vROgeos84666yzzvZT6yJAe886B0jY8KcNisLwtDODC4RxnaFKwVL2ziBFgJqo/t8OA4wGAK7nOaqRZlmSHJgOloWB5Yxviow8/X3ejD4JOKbogoS/UqaSmh9tEoRqCXVJfRYInZKZ7cXsLSOWqtl1tUsrtXEFXSYOsYT+eCbpEplxNCnRmDSNpmhPG6NCtjGcGQKbdFAog0pPFEqqA7cGxFJhnOGV14VS8EOa+sqwEUFJU33M4XrHy2SkY7radbEhRASL9L3tq5moZD4zTxEJmoGHiGHKRJKif/S7bCvPxm3SxFKc9efhIpFbOw9P0VCCUawMY8iDKD53Ml9q1gxqh4bFLxkWLlQGs8wbxxkemsos45IJ3FeCbF8BdhihjVhKBn0LHaGRorLoReE8IsxWxkJR1p1XHF1sWsMRRHrReB+gWQDwTsFxhl78nGgWzjQjlQmM0mdBMOQkL72TSNvpWTZRq6xcraE34sqyjEh8Jnyh4WKk0lVaREVSFDuJTCZciiJByufBLM5QpX7t+ZS0oZHGCxkRk2ObsGmx1QDTx99EFC2DJmncsDnkN7/eWWyKVvxscrZvnY+NMvGFo5USxvQebbvvQkCdA7T3rHOAhLkqDMgpE2H+jSPTdek7pSzbSoSEMxxfQSMPA2uXiorKFOssDDydjRL3O52hoXwOd1FYnAfK2mXfpwtTmlJzdprkW4TMj3l4m+CPUIYcc0v8fL6ESS9schxtL2UCZWFzmQVDApBDD78QX0hRCLFXteiXoRMl2W9cl2jii6kZpfpbeiMcoNhQzDPS9LKpU9+bcf7iCZ/ggdSVwmEUmVMMG6h8sE710GJ/T0Th19pCuynoAsiuEckpuMpkBXRDvyVuhzdqJp1cZgrp1kNHDo+WGVx+6hrK4yNAB7R/NnHcmLwWnATx/CS4QUAxJP3QN2jixKFZVEnpmeBPWTMLYrfM1RF9LB0gwSlhDlEGQXuupYWoFrywPMYBiwH7O3CwjuUykOh6seO08qwi7qA4s9B5xXILdXzw15oKK5OQyrk27mEU68C1jOumZ7VcA8roaFerodHVmmd+WTGy0HWEF52AUtl5dol3NqqBydSLuSrDX+xQzde0BEl32H58ToYKk03xXDaFwqIA0A7iuFWIawvxXNNETovvQKY2r+PEQ3ISWdW5DfXXgAQ/mdpnY9s0R06Lem3Keh6PggTCFHQnJSe0gNzpWZ94GMrinLhED1C5VAXvSyk4JQb3zn5urXOAOuuss84662w/tS4CtPesc4CEtX0NDSVKGHjGX6bDpuE/kpCaoDAiQdt+0vaR0SIpmT+PGDzXxCzI1OB6ZDyLaQTJeexSSDfOHs3EigylNmnU8LmoNLtXKsGAWrN2TfY7t0vM+GiXGiJKYUQ9KJrhK4YMm6FCG6pThNpO03WRKqBdCMewCxamH6GJMpxXv2xQROEy6zTGFPVpDVe695wVorPoAaaiB/IaeK247hJdMK89E9XV/8/em4fNdlTl4m9V7b27+xvOyTxBCJO5Moma3Msl+CMGNBDQBOQqKiKCIsEBCMELEcNonJAkigZBUESU4TogPOCVCCQIQdEIXu+Fe0WJgJgYQs4539Tde++q+v1RtYbq7hNyvpwcEtLrec7TffbXe6pdu2rVetf7LqOhQb2qnI+WmQDuR0WV8aj62ezKPkZJRrcKQgsK2qJ+isj14+ja037qGhSEVTC21HWkfS1H6vzAzcEkejVtojBmjCr7UQg8VrKfZkQCKVLKIpiqrpfW+SmgUoqM5v/OihCyno0qI1L0oxzJCKseZjUdjOrIHb26gxNHSeTppOEG9mSVy5UcsrAmYCcftIsOfb7YLliuHr7ZpZd90leSeN879JN8Yds5+rjp0BxImwYHgMGBdF2Dfblvb3ao9qfzm80dYJxCw7Ht5Fnl98sMBhLhGQ4QV/ek9lxJYVO/WqHPGlPdqkW3kq6rWzMc4Wn3pmN2R3usHJva4MQ9mzh2lEKkJC7aBoedPp1ru2uwk2vkFbWzsp4OWivRzrHlaGuddbSqcWIUAinaw2NXR5HSWLwrQo6gviPjVai0bpL67tR4u8AHEL/ASLK+ke1aUJbFP9sI14F1g46ERdwxKvsiRHBpyZYOkLYIuDagGud8jKmXnA1muZTwEbF//EoaGNy0gqN8oFapkjYqn2FBXkNRAFG9W0VNLg4Pg3d07OhIAVKNXbMz1wd2ekwfgC7PJkTVNrZ8xfKAY+qK4RdUubtUVk3O4uz41dwWQ6tyorRDiLwN6FjpOQDHZCXntSnqnMxBeTuhd8yGMtEwY4Vqdh3oRnzJvhdBM2w5uJ2cz0NKzppaqx1OGvC8zgVQbR9ooE4sqNTeinETBfLTCr0cNlcOo243ci5iNQ+7xqqkiAvcpc5Bu/io+kEQZ0Q7PYp5xc6v6sfs6AwcF6T1A8M5OuxIuJm8HIYWBPpjJ9ahYPrMOfh6ZFawldWXp251FvYtzlVLvl23BvRr+cfr2dFZm+Lk9QRxnbCyhWMH6fsxmfK24lrszdoRK3aK9YxHu9xh2ugwrVIjbPoRNnNn3sIA01xLjgrpbncNtqfJKWjHNbCd/l5tpoart0RewKr8k4LtlCFH0zSML5o1wzIKITs43d4hpkfnAr5HW0z35IVYfr/6EeCHAmHFLPqJKgKD1JCj9YQB31s5PWvVlOE/sm3fYKuTwsAHNlOD95tpW3XAYbiZzl/tKDmFibAJm810znrbw+Vx1vh5SD7UlmHXftWhJ4mEkUDntLicpe8vYvBp9lhQ6QrpXBDoLpqCgWcVZAcAzplU7+4ICiEuI0B3ni2BzKUtbWlLW9rSlnaPs2UESBmHPBclHhOTwjmlN2KLhE4gQWFFmFZVSueVhyr3oGvcUKXuIgGZtWBQRCoouku/9UPL4mJ2YCUC5KneU83RoCTcR6wjostEqVFWO4R8P35YcXK0XwCDhEqE67pVCq8DQZWyoBVoGEniKa0677t3A/deTXjAcYMtbu9b20Slu2m8jlt3EkY26WrR/Onz6rB1iDnqY7YdKsXionA76dpQmH3WinpTXsLyHA3Kn27qFVvLc1SwaDuKnoxq7hPtmkTBisT4gzHkgCL8bkIsGDEMx+pK5y2JE/YMezIk6YyCxQwzt3ydOqQfWPSruT1HFj3DRxLtKfqpXlBSP1SwgYawdJKyFh0EyihbNGDNnqD7Np0rSrQn0spdQV39aoRfz3DvnhbH7kmRjJNWkxjMCaNNHJ/rmBxXb+GYKn1ftynS06qw62YY4fPtsQCAfZnSeWu3in1t6oe3TkYY50jIuKswpmjPTn7Btyu4rfSgm7FhUVKqHVdNwCSFahxR75C2FCU7gyPL/XG1JCmvO7R70nVSpKdbl2Tlbj0grmdBzjpHe03kqugxKJE/9QypFt6/dUfhxmoPbycYj6qr99OK68fZsUO1nbbTa1tvSSL3YCOgztGeaqeHHadQin5niJ0Wmgp+NSeKr6V7bdesjCcrpoA3gdQXdCmNr1bvk/txHaXsCz1ypyBmbxBbFbklaLvTB8MRxZWWEaA7z5YOkLJ+xaAbOJ7wja94AJdB3RRwha6jA+QJn9gsAzVArwgERNTb0MSSNUFGkI8XimzsDQ9aVtFC2RFxKoprAfqxz56StRGG5W+NiJixgJ/lgdavOHSZktyuGXRreQCk69f5GpU4OD4ztLDi4XJ4vW56jDKleKXJhUzrFkcN0sSzVk2xXk/41t2M7oBWc+69RZsLmDLUNbGwk3St1ZZFxbW6hL5Og3I1CZz/UjgYlIvTR1XTSiBDjncHQIsbstPTVPCZEt+vp+tr1y3atZzntKpEJhVDUHJpBNYpWIFMCTbijCmBONtTP4kIuQBvcAaOCrrSZAMwjNmPHDyx/YiJ1xhVe82IwGKlJgnllGsnbVbhVysbF0w4q1LHZj+zBTrWgjpYsZLj+iYvFGoUbK5mNTXeUatj7B0mb4MK5dbK2/Qw2O+zU51n0S5WuLVPzs6X2zXcON4LALhlnB2grRWMN9MLYDYqhlfdVBhMq/S82pLtRCKSB8tpmRyVb+xoqgcH9TtVHHkEdDlfjvLm+vWIMMrPeeRRDdK75jLUFYNBYAcosjMTvUlFsqCKhkaDTulMMORI9bC8kXqBvTgHPBaosa8fGri8n/cO0Q2gLVRWlL9XHDs7BHH1o7IeYFQOOJD6JS8YK2Ah7VQLa84sGHk7AK38DW+4bt4cFT+f3w8M/EEYwneGLR2gO8+WDpCyneMMemNL9U+9IocMyLRtttCnzuXRTo8fCvU2NnmSrUOxwqWDkT4NppJroxOmFxVgTC85RRnmk3JtGwr1Zy7SShPjqkO3Rk6P5Wrs3R6VhEy5BMPA+QNu4HmwXc0Ozp7RBHub5NSsVLJ0IrpwiAYTL5Thf9tOk42PFpMu51HkVfVk3KDPORR2x6HKK+uBiu7QCrseC/2/mkiOjo6YSB5UFBq6F6eGk951YjJ9WsuORKxVrszQoV/JEZTsOHajmQR4FbUAZvIWogy01HdsL30nDeTSD9iTUGM+O0XBqYr1lFgqv9MlDNhUfk7Kg5o/Lv9U592oCMyiIpapurg+Bzlsco9zeUCY1ZWhpN9Yviv0SerNwXDS+36MMM0Rws1MRx/VqxjmMimN9RhmeWXqk/unQ3xlJzk7O5MG7XZ+UDlx2W1bDMY5v2WrpG3PKVh76XPRGJ7UdQHgotTMjOlxBZD29k1yggAVVa0DkMcTVwdU+V10VNHcgR0g7yMozOaDkbQbGm96w7l/sQ6wa6mNBqN0syvDlqU8pn2F7e3Utu1G6rT1rZUUdu0NXM69M50BF4imHJ9K61ypcVR1ba2Qzfk6jfSHg7UdRxqpmHFfHmu2CGxxrCqK1pGR4rD0LlLZDT+dP/fS7n62dICWtrSlLW1pS7uL2jICdOfZ0gFS1q3lUD2LxgGzCqSFLehXqYYO/UetpqOBZeok8Zhnl3rqvPmTIZFWfZ9RUc2HlzwM9Z2F4EaWoxRG1ThjOvqqkfD6mrC0/EpAzEyaZi3F9Y9d38Zxo5RYc1QzxiCvpml1OHItRnmJ7BCwL8frvzxJyQq3TFewNU3L/ElXY9pm+Kh1CNPceEqwcJTrYNWbos482B/yZ49qK53LTrsimsPUVhZglPi30TQOZl5ZRIKHFH2/YF0RXBZU1K+x84KYQwVT6EjgiKKAUWpPLZQbFwgC3sgqXeWFFYKAahvDL5OcPzaR/BPbxfmIhYKybC85I8brlXe+l5G6r7WASHBUvhfjYqraCcBWgQUSYxSoJXYEMWuKF1gZ3VR0rABXSRuFvB9TrZV6OoLh7W3nOIJzYEGEidoRyNAyANNZgbUmBiOKolG7qYKbhTBf0O0oJxOGoUSutOBnOAgUqq+NrlmzlmaZpFp80LrAytU2U+l673gC7KYV58uhtSqinC/QRcScO+RGPUYZ01tpcv6OAUfWtncG6LfShVUb+V3dljEqGhG2hJFpRqcMFN9J7FQxswpWLOH7HAVU70dUbRcUNDwjWULHElg1yqeT47MiRVA5QNxPDGwrkg5HwmI0Re3C3ey/G7vqqqvwmte8BjfeeCMe8pCH4Morr8T/9//9fwt/e+ONN+Liiy/G9ddfj89+9rN43vOehyuvvHLud3/8x3+MSy+9FP/yL/+CBzzgAbjsssvw5Cc/eVfXdzhs6QAtMB2iXwQBFIUUF8xhuro5dKh7JuwdK6WN0sxTqQ9WkDFItQ7+YhGlwrT6rSiYlknMrMmT4YZuVaizfhjhSf11T4/hWor3nrQ3eR+nre/DvYf7AKSE0kGRIZh0UkgbJUTLeTzj7B1MfIUDkxTLn3aVJI/uSPJonem0gwPAYH9OrNzv0RxIzla1ma7JdL6EqLjqupMirUTjt1qZuBIdEUpkH6hSAENbKhIjDai6UCI5l34gWkbsRK4C/VqGKQYBGOaJKUOHzaBn/aJB3WOU1ayb7ExWNqDPSTGTvsZOmy5mPG3QZV0ZnyEfM7EyQLeGoSby23Qyse2lpIdoHql+oibcbiVBoADQrWcYdC2wenKz0mE0SJ19LX82rucBt48WbaaIt95xeQiakJ0NXF4iBIs2T67sgNnIfSdEg+iIkj6Pk8TeADnnBJ20hyUJhLaEqmYp9baVorduqmBR7fuqMifFsQ7iuFDbFmVjQO2dvgeV26QhPp37xBIEVURU32kfcig71Nw3KJM8dqLHY7yRftILJFmQPUj2YOywld/L7SbnPtnIzzZ2FmZaZh77gdxDqIF+VfKkilIVQJkfFmNJ9gCKQqShkTyoAr4i/0ppeun7WVjaRz0vUSaPMLkMiq3Fa4reIPbkdOc8qOgAVen+69Xe+c534gUveAGuuuoqPOpRj8Ib3vAGnHfeefj0pz+N+9znPnO/n06nOP744/HSl74UV1xxxcJjfvzjH8dTn/pUvPrVr8aTn/xk/Omf/im+7/u+Dx/96EfxiEc84s6+pYW2pMEvbWlLW9rSlnYXtQBzh/8dql1++eX40R/9UfzYj/0YHvSgB+HKK6/Eqaeeite//vULf3/f+94Xv/Zrv4Yf/uEfxt69exf+5sorr8R3fud34pJLLsE3fuM34pJLLsFjH/vYhZGiI2XLCJAyNwWqoESvpnEOnfC1MGYABYMoV1Kv6HQS6KxpRkJi+sh1ACnBl8PuKgnaxMX1bLQKMStUc4RJQtKaXUMhZz9CQTWNOWG7WWlx3J6UcXzq2n4AwH1Gt+LkJn0/1m1hxZYZgTthwCybW/0qxjm+vZP51beMV3FgI/3db9ao9qUTNwfACrnDHPUZfqVHsz8d325MYMb5XMRos47F4eKwZvpwrKXYoySfinpstCkCBwiDT4v8IUZeLdNCOVSGo0K6uKYfapHHHCkZRcSRRH3qrGBNRTaHdYfVDC3sHUywVqf7avIDdSZiu0/tttEOAaT26r1Db8ssZuMNRzqqbYMq0/4bUt3diRhshPzdFwm6fF8ZCg3OSj8YlJGIdDMRhiAXEzHIkMtak65/Tz0tojakHNxHuzAXgaJcra+wOUkNOs1q3r63/PfifqkoKYys7CuJTphKR0MlEZaSw3XEoKDpc8HOuBBmpp1MgKpDpSJpGmZRateeVeLT371iOIUKLF8h8hqzN5w/1RjgKKoTVDhJwYjF7osRVoFgaZuTbbGKMDmhmp63cRGGCqRGINJ4whieXGwhe2AhNcgoh1lJIOg/MNvWynsZKwWNaejQFbvKNegIPd0r9WkA4MguQa6BIz/WBS5eG3srtH9VvNZNARzBJOjDlQO0sbFRbB8MBhgMBnO/b9sW119/PV7ykpcU288991xcd911u76Oj3/847jooouKbY973OOWDtBdxapxEkgl9oxmqdCLZxTeHAwKyjCgXkqkfTWNcsZPQBT2N0yQEHy9lU7QbElJi8RaIoaSHmzJqRHV3miFjkrS9/1KqT+kNYXmLKrBwUaWwq+ygMvQdli3Y/7u8t+JUrzfr+DG7igAwA07x+Hzm0cDAG48kPCUyb4hmi+n3w73J5gLSHR1VordJKirhT2QZXN3xkCXvUQq0OosMBzkazUwrGVk2fFh2nejlI1VCF2XxKD2Tm2bvxiZzDhXQTO7dL4C5QaoySV6g55yL/JmHwx8njj6aJkV1+Q2tiaizbPBZjvAVnYOxjsNQs69cJQntWlQJ7kbDDaUFsuBrMOy3aM6kDqX0dR40rYaVuiys5UgMNVeqsQAgKR9FUn52KLL+VsbO8kDXB1N2bEbuJ6dIWukH/Vqhm/zfbe9Y8eHIb5JxRRseDM/kRsUMhJFhRZyJug5RFM854UOkFpUEJuQFLZ1uRMTIf0P4kh7VVW9Jzr46jx13Y8is5lirfJP6JBeFOQ13by4dQ275TYIlcproWM6cF6PcTpZRhnT5I14+zYK2VDpCFFZGTNxcJllpXPNWLphKhXtrSoyrKEonU9H5qh7GiAQBFaBqelBLUDmJBpQpg2wYrgFIjm3lUB/NqvJ+4lFzCr3vZUmsr1hJpnT99LiiDpAh8tOPfXU4v8vf/nL8YpXvGLud7fccgu89zjxxBOL7SeeeCJuuummXZ//pptuOuzHvKO2dICWtrSlLW1pS7uL2uFKgv7iF7+IPXtE6HJR9EebmdE6ijHObTtUuzOOeUds6QApGxyIqKrISrXR6JWkfOoExlmFUqBcSdoFeiF6FSR1ZyILptGna6PUc/KKTWLADCVO2h0Y+d6oAqPEPppRneaVLy34lPBeKoyaFWerAb5SJX2UQSW4wE6OSa+4lhOet/LJ/mOyjn/fSS/aTRt7sH0gJ1EeSMu30a0WdY76DDaiCBZuBdQbqcE4YrE1BrYSphN3xohUw4xqlTkn7BtjYHLCs9GCTapoIgsSOnnpippfrKWkYIwFta/SH/J+OtxP2ifeIo41xpk++tzwnQEywQj7cNtmggjQVWMjhSXzZ6q7JH2HoVBOODUIQyqYWUuEZ0DaRQ5dTlht1w2y+HFKjKd+tJrZawPRnTFOlu6U4Lw1HqDNkZxh3THkV5kAl5lJPsNaAQYTqqPV1uhzNInYXKa1DD2YHvPsN/W9WPmrZHWCA+vtUhdJq2gDFAGSqI+dhuLvs4WP/Yg0oAxHexgGHSq9ngHgB/k5NArmyVGZUphPkpVZDLU3C++RLDqVGF0LK4/gK7jI9fPsTAQo6sgPkEPa6nuOvlGys50YVDsS7Sn00lAmGBeQooqWSeRqJr1AjW3p/5qwoMZcHQFaEHVPqujpO43jbiqR3VRHEYWlY1n5rpPSdcQKQozxLY6YHS4IbM+ePYUDdDA77rjj4Jybi8zcfPPNcxGcQ7GTTjrpkI/5rd/6rYd0DmMM3vOe9+Be97rX7fr90gFSVm9HuEEs8HtxdmQyLF7MGbxesxvmCkBSaJVzDVAo/GqohY/VSMdn9dhKO2YKnqFQ/EDl/qhBOdBArOAZCfEaGbB6cEkJ72tsThI97J820sH+deUY7Mk84UEtMAeJGI7bGtvbaXXht2tFk6U8FZm8652I5gAVSFRQzU6OMY8niD2NtAampoKs6dPUFUArmbrmUHa0RnJ8tIq3ai9+HrktQqXgTUXTZWXvmcGRnVcvNHMRxjRzrD9AJnHtHLspUBHsqgZqo0QZWc6g1tXY82dfqgyTkShjGFj4FZklRLVcYNKeoFJVqLZbi4n1BQC5enoz6lDlvB+iXAOyyuy9hc+T6Pa0QZtLsTgbGSqlbb13XNS2m1bMZuKJtxP4JzGY5H5TWyloolDOFqabbk9S+XbTWLQtkJzEqBwcUoNnurwVKHW2vchJLEozKKeZoVZ69jHBSUB+5fk5CqQkeYiKebhgLIlWKOT9ihPR0lwewzYdmpx/Vlee6fFAYt4B4PIy7bSGn+QLH1seAxwJQI5nnIsFaJrOv/Gs3C1OnIx9RgmUooQXUS429MKFP4coaPK0v5vId3J+BwcCQ+vVTi9q8FkKI1ZOFf0VSYtYq4LEM+N8r6DkO9uONA2+aRqcccYZuPrqqwuK+tVXX40LLrhg19fxyEc+EldffXWRB/SBD3wAZ5111kH3+dSnPoWLL74Ya2trX/X4MUb80i/9EqbT249PLh2gpS1taUtb2tKWxvbCF74QT3/603HmmWfikY98JN74xjfiC1/4Ai688EIAwCWXXIIvfelLeOtb38r7fOpTnwIAbG1t4ctf/jI+9alPoWkaPPjBDwYAPP/5z8ejH/1o/PIv/zIuuOAC/Nmf/Rn+8i//Eh/96Edv81p+5md+BieccMLtuu7Xvva1h3SfSwdImfExrWo4OU/BJAQVoQyb06pUoJUSNtMS6hRhYQ2+AFilxTFb48n2sQjB6qKdi0KznKitdTEofO5EeE8nXlIUIAwkSdR2poha2DGtCrPQ3L4GX3Yr0iAz0IRRAmK1YreJKGQZWeNiq97B5ExtSzXMnIWZEmXGc+QnDlKDh1GNWFNRRREv9EOnoAmCCwXGgJEICz/mykikT+uvqE/NPDEqksfPLv/Z6HISvRInbBW8qVhHAh1IortRrD7jpE9wpEIx/Sg6UUAqnMwpWfO6tlQR9ckRg34IBCqz0AQu20JRvhgNXIZUBpVHk2FR+rsPliGu1juOLuxMavSZXdPnJOfYOq7jZltJOKUoQwGz9IBV701qQ0iB31aRFzqBAZlEEIREkMQLqcHTR6wkshatwMkaBhWNqJm6eNR/Zt5J2jart5PGDUrExZzAqW1TZBRIydhat4kjgRS9GxmuoxVdSrpOO2ZYedhjZZgalLSmgPScCH5k1l1rgRwBchPLz4PuR8NORfRb3X8B1y2KgGrIUUVDZ8U5I2T/sKB8RXRyXtuiiJgJRJwjfm3g9ypaA1CEB8ISpWjxrFimFlgE1Dt15AJAiHcQAttN9OipT30qvvKVr+BVr3oVbrzxRjz0oQ/F+9//fpx22mkAkvDhF77whWKfb/mWb+Hv119/Pf7wD/8Qp512Gv71X/8VAHDWWWfhHe94B37u534Ol156KR7wgAfgne98521qAN1www04/vjjb/d1f/rTn8Ypp5xyu39vYlTypfdQ29jYwN69e/FfLvh5uGaohMdEqJAmm6Lmngp1swNkVWhW5wipyVPXTdI4ulUhfPrkytmdmmS7eRp8dDI4+qEpGV/ILDBiLQ0iQkNvtNwP51h0Cg5T7JsC66fdexl8uFBilONq4UhWVVU5Gm4aef9qHJh14yZ54u0CM24K4WwjAxbh9ylsLttnae6+EQdH3zurZtfynGdzvebuRfUD25ewJrWRQJ5RObfzzJd0DzPwpwXT1PX+JQMwffraFH1OoAM5Flmi/8v98n0TK6kSlfBYRWYVGao3NejRNBkOq3t2hsi63rGgYTep4LOzY7ddAbcCZd/RUA9XTR9Hxcicl6QoJ1blRHZRaOrU3lEWE4g61yt/qtpUSeqihAmTQywOp3YE+Hroy0yu2KzoYjVR+UjTKOJ/5JSpiTfVr1N5O/kaKGdrusei5WrwQLc3O697cy7dSoe69vm2ZSLtp0p1Peeq2anlZ6MdM/1e6+K3c++HckoK5yHOt4EJM+PFbCPOOlgzDtJc/TqSA+kU7KnkTCrK6VKLSj5lpfONysWfzlmSjQZ9N8Ffv+9SHDhw4Hbl1ezGaF76lj96IdzKbScs35b5nSk++d8uv1Ov9e5qSyHEpS1taUtb2tKWdo+zJQSmrNr2sAjCxojA7JI/WlOEvFmYLK82opWViSq+nIW9yqxbzTJzrVQejrTKUYwFHXXRxisUD1S8CoroKXyttDBkHwMu0kSidSqRUGsdaQFGHarX32erj1tfXquOeNG1FCXQiCG1YkGLXZtXuCmhVVZvc+ydmfpLnNRqBYZbBE1ogbqCRbKgvWcqffDfeaWp/k734mtJng6NUZolCopS2kFaGI/+LlE+oyJI8+dK+i/5uxKz0/2M+2krjDL6dB6sOxOtJH/HWunVVNJ4FFLveodJhrtIuyds1agOpJsdHjBSS0u3oe4PCxiTlBDuWlm52zYWERI+FBdukgiKCRGzGjMp8pUhuFHFsChVatdCoX6go6XIfy+hba2jNQvfANK/C6ZnHiN8DcFdVVTFqJdiIRMp6v5F1z3zbhM0k8uk9AboKdITwaUdzFQYdsRcdKRxgzSeSWQ2X7+KPHM7QPXD2ijCgbovryAjFeHh3y6KWmrUZsEYpKPMxX4OTByhZ2iHBmFM8KrAoxRtTvclEUOjRRtnARJrUsS5P3IYWICBuQO1N3ajBH1Xsq7r8NKXvhR/8id/gmOOOQbPfe5z8cxnPpP//h//8R845ZRT4P2hP5OlA6TMtQEYSVg8VGaeBaYmG834KiZ0GvyivPixB+iNVcKtRS0iciR4EOrkezWVXICCxbKIplsZ/rslWMsbhDyQ+Qbwkxl4aCjKx1q0UYfCmWXTo6jto8Ue0w0Ct/XOmVA6HVotWyYsuj51XRalLADSBMhQV20Ug09YPV4NiPT3WVkAID23hYVolVRB8QwoN6IyPJFSXk2nC6AO1Dn4MxZFGQlqotwNrehrND25NzKZ6NwSrepLishFjpLhP7HsBk1QGsZU7J6o72uavDW/6tATfBrBFGmXc3kG2yLK2GwmFWogOTPkIC8U9LRmDm6wHszYsT7Kd/KSQ4TpS0ZPusgoBXCJ3jys0Gfqerfm0K7l+6L7GynqegMRuVTFXqMqmMmxc9W2Zv4RJAkDhrRVzstsIVI6bv7Ui4Yyb4b2k79r9hmPJ/l5xM5IkdlgOI/KTg0LGGqnR7/r9OxYnmMcVN+TXDSu/1UZeW/rxczZhUzVAeZyfADVngGgOsaB27V8p6J29ukeaeycyDhcjSOqHWGEpd956Ud9AGgiDapfqbqCYaWB6Y+cEuLXqhjqXcUuu+wyvPWtb8WLXvQi7N+/HxdddBH++q//Gm94wxv4N7vN5Fk6QEtb2tKWtrSlLe0uaX/wB3+AN73pTfiu7/ouAMAzn/lMnHfeeXjmM5+J3/md3wGAXYspLh0gZZNja8S9ThIfm/lEv6CSYwGdrJg/AyRhrwdYKy4AdpFwF61MdoA6124iBoibRAnT9pGF7WwfYXtNl0BeNUpCJ7GC+o6EActVWJyJAmhoTycAaxRQh7fR0P0buQYNH2nYiTVA5pM8i8VJlNW9VcwXrswdorC4dMLqUMEYVLKhKleTdN1BwRizLJZoAU/P2acqG+ke8+e0jFBJorkwcVinZDCzmp2BsKIxsCq6GHM9KK4LVauoUBNA5Q6MiazfQuw7dCpk0BsVws/bbAmLeV3LBTniRwwsFXU0USIC9Wb6e7XjQLiuZrdxFKFg5JQRM6fKupDxM7DgZHdOFFf93HQBlrRXdNSH7tVHRcEziC4zzarMEBw4fif8QPoMafj0q1KrKwxmGJNIEKDha9WhHiBmuInLLQSdSBsBikpSyYoG8k7VUfqfjjCRBUhETwW5TD+/LVpwJFDD9BRB1SU2bKsinPrZUQL6VCJ2moUGpTfFzCp6J1VEL1QGHcGLI3kvuI2bFHUGcntTO8/UJwPy+0esOY44KoFIBakXrDqCfSdAk2vhDW7tUG2lGzbTLrdlEEgrCOkClUMcEOs017Rba9Cv1+i7IxdVCdEU8Ohu9r8725e+9CU89KEP5f8/4AEPwDXXXIPHPOYxePrTn45f+ZVf2fWxlw6QssmxFnZYKpDSXFNM/mpwmXUkoMfhvnwJi8Gefkt5GJOoqK/zgm22D8rBUDkONCApIbcYZQBmskrQkJDG7fNnpXKQXAkP6XyGdC2KvdarULXKVdBOT+EcAkVeA5RQ3MKijYq2HWorFG5yeobKOVUO61xBST6gfJ1VsrVdeS8aQqLrZpG0QckQmi3M6NQEU7SHco7ZjIL8nEwaMjlHhFEWbRt5mHyQmDtSjICZyIRIkIxAWcLsQhVlAqd79YZp1Ymdk7/3Zq6NNONGK2CT+Vpym7AmA/dCeCiWbc9q1jTxTiKzAd00cP6URT5pJ+8BKityCIMKfiXXdsqwVz+ymO7J+UrrYLVrhimHsYC7MDMhmyqqBjOF82l03S7VVum7KaFh5Amb2HVRxhhejFQh1e2asWIxQrBba+X8Cirlfqbp5l3p9LgZ1mnK+9GLrPL8oTJMIadmAPR7oAQLh4adnW5NOT65H4dhAHLBZVt7hrj0yijk9oy9BbJgpr5WXVtRi4Ky5ATVVhwrdXQjsGh0hHMCsSb5jAr9aoZKV63k8XFuXpoffHvkps6ouvlu978720knnYR/+Zd/wX3ve1/edsopp+BDH/oQzjnnHDzjGc/Y9bGXLLClLW1pS1va0pZ2l7THPOYx+MM//MO57eQEkc7QbmwZAVLWrgF2JP83etFHCIKqEF8whbSej2KzcKmLXsE7DPMElue3bYDJYVhdsZuWRrGS6uaFqcRR/VdH7BnWxBCdE+ONlEmgiIOOpOiK9kEqNEsysKz8bT9TJRszsBZUG3ECZ5SkQp0Ia5QOT47waLaUH5pCgA7I0Sxdq4tLRihoQUFZXG5gWiY8AzPCeyEKpJiP2Y+MRDesWrmre9cJ4VrWfw6OU/+PVj1GFUnifrRjpH5WawQay6t95wHDrD+J2kiUTYn8qeRrnTityzBoPao59pmCLwH17Dh6MQMtxvn9OAoxlarr1US0n7gsyDTAtukC3NTze0EJq7GyCMNccmXkONrjB5YjhPypImoakgkc6YFEdqs4E/nJN0Bikp3lJGM3VsnEqmQFvx8t5kTzUj8V2FZrOKVrcnJdFbjhCnFSFb1j7R4V1ZHEaxWFbgVarqbynccjpTlkgjqfKgehCSKcxFzLvVCkpx8BniKYWlyT+7dBpArv0UnbU5ZzZ2BySRQ3NUU5Dr5Xzb7UUWR67/K5upFBcBIJrCY1tPUjIxHlgbzjsZrv3/TdH8Fq8Pf0JOhLL70U//f//t+Ff7vXve6Fj3zkI/jABz6wq2MvHSBl7VGAa0qnRtfHAtLA4oowMuHkaZublNRd25Ij4tnZ4YG87QtMfS4eZwwyfSw7W3ngt1YGpfxix9ohNvm7M/ADmgwEPtKijrMTsukVdDczYM/BIFGuNTrDA7Qww9SgWhSelLC/Hkilzo+aDKw4H55hOuXsEObfg2UDYlU6JZRXwP/3woJZJClQ1FhTtbx4fzWOFDlEAAiV4TwRnQ+haO6a7s6Qo4JEdH6YnvhImsFODdwmObL5Z0pJt3A6aJOWUIhGPV/DH+wkhnI/qYcm98J9Z4Fjp4+hJRSMkkvQasHEyusRU7+GwE7OASbT1TtT80SrmX7stBeMTdXeitmlaexzKsa63bwiHXv10MlJbC0qzomSSbmoRUZw3ljR97Ol+l0Eec48c6BwFpP4plHf5RhkhnOn1IKHryWoRViUMagXdWSN7PElOCPnZWfNFjXSQk6S02wu3e7cZ4aRmWjsDPZWdTq5BhFYFQV54xfAdYo1O3vti95tXSh6urfcx9coWGhBjzezCtQZsg/63Hey3dMdoNNOO40VqBfZySefvGsYbOkALW1pS1va0pZ2F7V7ehI0WQgB1s5GCe6YLR0gZf2JLXxVcVKhndpCJCxtVBEivXLRDC2KCk087DQteWzn57LR4qCWhDxF4xMWjyRBA0BQBcmik6Q9AOj2VJLkuWbQk3K6FiNTENbsqs9EAL7YlL4vipQ4lXToE3wBAPV2+nG90cNNCa7wmC87YLl8RRKky9GqpkwsTr8tw9CzCdl0DQASC4dZdxJi1/cq2j1ApBD9ihxTRwlEvFAiMRyBqiLiIPB3TlqlD5WQahSTBzOlI9go9K/0YYqkcIqyBTsnvqkjdrr6eFE1XbG1mI1FxBdV5sE3khhcaOMMMoQxy9jhjkDXYgrGDr0/VsGTJLOiRQKNVwKNrGNkC3h1PnF/JiKo+4kWLcyfdA+xBgI9uzpHRFxUIdCooDtKxDVCtTUC4dL59KfWwfIDNXnp9z9/dx1gKDLmKXqjI8dKmA+Qsi+sc2RK+GdBtFXOKfsBVvTIgoLYVImZOEMttj7CUIma1nD0m9h1rk1Cm0B6rj1HbUSf62AldKzqE6ktUCTIV6p0CH1SlDlBzBIdlGefo4sDI7pOtepHijwRNHuT+nodOBkerNOVsMEwpglhaUfCptMpvv/7vx9/+qd/eliPu3SAlI32TNDbmtkHoarYQSEGCqIpCl1qfB1IuTeOnZ4gA+GoYqcl1DKISe6Eos4eJOTNKrCNUbTS+RwH7TQsqpejxe5m82TINHPLzky41SRy7ka9HdAcSAeuNtMP7E4L+DzBNA6xySrBq+miu7UK3VourLomFPJ+JJMvwwJmwQSj7yuoe1H3o02LDxbC3vJI5/edyV9hK/JbCONSzakdHHKcoKnp+W+qYGxiCsmxgHLCT78B/0FYcXnTjFIuU5lJoXsikORsblL6lP7UranCqGuBWTumyX26DrDqHmO+7pAVhiMs5xNFFxHJWTGGXyFud4iDFXRxywUMQ51HpfNjFtWGgpJmIGgxNBFxmO9lpUczTCepq5xjZAOMKvhKsME0s326SYVIcJhyzBIzsLyvBNlmR0DnAymWmGEWp1pQeXquETY/Y9dFWKpjFWRSJ4ciObyG2xP83lhpN1XfbqHp92DR2HOQMWKuLp4RJ67eMlyUdJY5S/dC0Fyl2WecExnhOGVAciUZTtQLw6FDIMhfFUGm98NZiFiqcnZ6du610yMLG9N4uEHqH1RPbdB06L2Dr4+kEOLc2vmQ978729bWFs4///w7pY7ZkgW2tKUtbWlLW9pd1JIDZO7Av6/1HezebrnlFpx99tmoqgr/43/8j8N+/GUESFnlAvoIqZczsai203cWKdwEBgdSjxocCKi3cpLyrEw/0motNPMrk34kyYNBMyx0/Z9Z0xELO78as14iNB5AZD2OmWPMHlaJr+lEWGYFKXE0WsnWPdBsZcbOloeb5DYgdk7tEPakE3d7B2jXc7RnPZ2gXxGIzo/USmwUpQ1Yq0ZJ+UcIJLEASrKdWRxBoXZzdJDZRpg/pglGwTrSLvJbIFL9LMUaohV48QgXndOqLGn9G0rkNWo3b6Tcgop4aUiTnpGbSuSHoz4aInPgftgpEUDSxenXArCan2cV4IgNRUmsqo2C1/S1fH0uSjkPLQjooohMcgRHfotaY5r5c2oVNGikT5DY32xEj3YPRpKXVRSPr8UbeHrHOeoDrmxvTOS8Cb7vKsLnCFIwQFuJIB+JSC6Ci/V2rr02BTO3knBkjj7k39sITpKO1sDU8j3MQsQ66X8mYgyUZT10knKoAF0rjq+ThoMILBovbrPCe19u08xIqSuWI0Q7AY71zoIIXhKE54OwYb3AgAzLVZbH1lA7+GFmeQ0Mj68H1STK/Z6iPrrMSawjTO6LtgqocuTHZUXbygUYA9jqyNUCuyfbt33bt+GEE07Au9/9btR1/dV3OERbOkDKNv9jDc4MUGVaa70pYdx6O326SUS9nUO3O5LjQ+YHDn4lC2mtWHQr5PQoHFoJn83mKhQWyrlzUYFFXfdGDz4hw3R0Ts18CRVkclfIjK5VRAyLagfcBs2WwF5Elw21QXtUpiKflEYZXViyV4MPUdi9grr8MIr6cSU5JZq6DnWNs2F54yKvcEJQonReq13TSIzSeDCXEzAUBTDU6VRhWXGGFOuoNnwPUkMqcK6MqYJcg/aMGD4yMKTYrUT1eJIcK6bRVD0bqq3lFTQAMIxBOUyhUjXKVpPDAwDdWr7mUUDM9b1MFVlo0dYBrqKBn6AicQ68twj5HiiXpm+dNKuNBVRjqKCqagtLx689tw2J/PnKCeykHFJWxQakb/SW2xC9mXNGjDcAoRbOwtdZKJHE+BoPm50x6wJsnvDolHXTo2r63F4WPRUbHVdcD40WDbZDkf/FjqqCf9gpVXRzhgtr6SRF8WWVA1cshug5O5UfRZ+NZmZpSDPA5HuMQV4qypsznRJYpHGhMwvzCPUCgaHYGVhW2GnyWY1zn9ruYLMqM7+L1oqzY4FIkB7Vdmsc5z/6kUWXZT3aNYtuLe1G/bwfgUUu+5WIOMz5icwSjQJdB8vOs9+pETbSC51fRWzbCEQgjI8cDeyezAL7l3/5Fzz96U/HysrKnXL8pQO0tKUtbWlLW9pd1A4SjDuk/e+u9q53vQtPe9rTcPzxx+PHf/zHD/vxlw6QstEXK9SwvNpuNlWybw7XanE2EyIn33Ukn77m0FJdqOEMY2VG8r7QAFFOeqFXolZ8ixKadSXnolyBWjUCgiQAKewuVehzlGEC1JQ8uyM1nKqJqjyvav/w/a6qOlgU1dHihOoeWXxuEJMUPgAMvYScXYSbCS2HYBBJBLCX78Q0il6iPgiy8jdBQSYEW+lwmpeq6kXUhYTkdFV0nZC9oNRG0rPJl9BQ8ncQET0bOdLBxBoTJYHYWoF1Ms3TRCvaPzoiN5bIj76+bpVYdQIpsvCfKvMQBgGgFbC6Jo4ERgX9mQib/1DlaIG1ETpYGXKYyefoS+ysQMi9OlaMAP1GPRfaz+9UEn3rJeLAZRxaU8AvAMoE4h4zUCVKUxBvtFozSMpnUPSuq4Mkwio4RNpFoLHgokSkmL0mGjZaTFLXrqJ3IVaADwdfoVsvY4Cd1Xui5tBjiCm3RYMCQqb2jL0BfNYNKzSi5BkURAOQzlD+3ipIT+n1aOFMgmBdq+FYgrKEDGIbh8gil1TmxLOmVqwqBCptsppetG7NMaTe7jEc9UkQl+rrAGITJfroIsNZdPzgDSJFD1vLEb1qbIuEfGlDwE+XU+eRsCc/+cl43/veh+/5nu/B6uoqnva0px3W4y+forLVf49oTGQ6txt7uAmNXpTvYdDtyYXxRlYxmLITMBDhPs1+0BBU4RwwtTeqWlw0GUboumHCoDCFci+QHCxNi2aqdHZq6i15iatxRJ0dOzeVgUmLrxGttB8Z9KqIJJAmVi3sJ5M/DTwRgWmjUVFJZTKpefKVPA5rIudkkDkHeIZ3HCuw8sQ4VlTr1pQ5CDTY61wgBaHN0sm1E6nZPRpWYEhRO0C6vhZBdxa8zTqhiTHaFiw7c1FDYAS3tUqwMIL7hh+iENGj69JFPf2IoK18gEEQNssCeCefIl+f4WdgILkP/LsItF26mOm4hp+k72ZC4owiImi8WXwS+nsvz0jXTiNnz7UCvxaK44UEgkzufBprytp9QCmaWJewUdpmSiVmcowoX6mJc7II6bpECZo/p7Iw0U7LomLDXklKoGzq9FutGO4WH2sh3KUXG3QPg1A4vSITIQsAqDamhYHcnzybwhmiT1U/zHp5NrbTz07ajgUWR6q6Mt2MNXl7quc23UsLzLRPq9mKK5H7PJog+WR5HLVqjAEE8mMBVW+BrDqNzkph4F76n1Nil7YD/BFkwd+TITAAOOecc/CXf/mXOP/88w+7A/Q1ZYF95CMfwXd/93fjlFNOgTEG7373u4u//8iP/AiMMcW///pf/2vxm+l0ip/+6Z/Gcccdh9XVVZx//vn4t3/7tyN4F0tb2tKWtrSl3UkWD8O/u7mdccYZ+PCHP3zYj/s1jQBtb2/j4Q9/OJ75zGfiKU95ysLfPP7xj8fv/u7v8v+bpin+/oIXvADvfe978Y53vAPHHnssLr74YnzXd30Xrr/+ejh3MOGLxTa8Nek+UPTD76kQjksrEl2uwavFii4LAKBgYugogq6wziuyOottASlKQt8Lt1QlzxKUE4FIKxaT7tH0hqG7ehuSqM21loKqrO15dcaRntUK06PSsaZ7LdosudCvKuYIVcuulBheJVCPzYwJHeFxNjB8UqloQlD30vV5pdc7dNPU3tEviI50stquqDbQtlqlTaHE0dSKXsNWKqLGidaqxIeGKbj+FS2UFYzZr0YVYg+cUEmJpa72sGoFSvdLCb6htwp6wPwgpWASYEbzh/I1lUw/rYD9MAIr+TlkrZvBsMMgJ/A2zsNZeTYA4IPlVWKIBj5/b7sKbX42k1w/KSiIC2MHx6Kh8/CPhqiKBHJdUoX+Dswl+IYKaNepjVQ0iYJtQSJIunZVERWZKWGgj19YVJCO50ozqiSL0uxSkKFR7EuO+qh6gIXmFplFEaGaYysquC4sKtuhvuvSDX4URewxR//csEeTNZyGg45ZfN5bTHMkjxK6Q+uA/N10Un+LCCDVWL9rIkQoNdDiXN2zdDEqAqT0e0ibp28c2vUqt0fuh4ohWwh1KiKQRKDkXY/RCu2bBTtVaC0YhmI5Ub01cGNi9QnhQN8vi7zupJSAvutxxOwORoDmijPeTe30008/7Mf8mjpA5513Hs4777zb/M1gMMBJJ5208G8HDhzAm9/8Zvz+7/8+vuM7vgMA8La3vQ2nnnoq/vIv/xKPe9zjDul6+pGFX3PoCM4agenarJo6O1nxQCm/K9SEuU6O5GEg54lAMTFcIxMmWfAmUY0BRC8vwWwxRiC9qDRQ1SqHh0LOoTbwXCuskiKRmR3UrgPt3rR/tycgruVJdNCzyjE5MM6FYkJtqiwql0c/q/AIPfmT+WDR5Rlg3NUY50m23W5gNlKXrGhAmioK7Q7QbKRjD/anczUbHtV2GglNF4QlMnScY0BFYH0t7LTohIrPtaXq0qngZ6eUkfu1/Oz2dBisphlvddhipUnfBy61xVrdosoOxqSv0Ob73T9OCTpbkwGmGU4I0QGkxktt5CKsIw/NcN3SokglfWrBxFg6WUByLAnWcjagnoEZayezlg8WMT+PrneYbmcvbJInxqlltmASsyPHB/xZODh0qoPkT8mFK2dFOS1SG25+GVs4W6F0XnVdMAAFbIVoMFffbja/ZXYin5WeICesF0eZHaE+Si7MAoegcHBqgZbZ4Z1ZRMkNy3mLMYj6Ri9ikyFnagVv0Lb5/dqplRNpeBFl87tWTQyqnez0bMrkL4uoKIKFfalWnT69FFPtgywKnVDWSTU6DCx8fi/7oaoBSHlW6nkmJh1dg+p7BGFP1Ds8MHwOv5JlC2or70hvWJBS2q2EX9kB2lZMS9W3gjPw5uvDqbin210+B+iaa67BCSecgKOOOgpnn302LrvsMpxwwgkAgOuvvx5d1+Hcc8/l359yyil46EMfiuuuu+6gDtB0OsV0KkqeGxsbafvRFmbFFKvGOXxeD0647dVZUq+VqAk7Pjkfww08K4w6F3jl0nNyokWgxNHeSnXk1rADRC8ubMLFgUy5V8qnAIqEaj2xUJVmv+rhVtLBRist1oZpQl+pWwyyg9PnnBVrIk/ua1WLKo9E1QIhlD5a9Hnyn2QhmK2uQZfvcdJW6PMAjbFDtZXOMfxKbqp9EcN96bjNgR5unK7FjrPTE+Scukis6cN8bdkOsNkpCJWoL/c0uDlT5PtI5Ct/DuQZusZjNEjXsHc4xlqd26tK2yo181lE7OQIis8dxnu5OlMFSdDOjoadOFRb2bmdAJYmV6/mQBVdpAhMrIw4fhVpo9TocrRqpwnKYVoUGzesb2SmFpYTQnOkp5NIidGRDp07oibpMhl3wenorFGOpZ0XmXhUBEZJJOjI2MxtFJ/RQEX/IpMCrJKl5oicUqDWRTilqK+cxupEbFVd3VFB5P4gxVApyjywouY+JEfclIsoes5qjGE/ZjKz+GLVZ8p/s8UYdTBSBZAjqJzPI8cl5fHkpFKeU0RFm3PDRS/5Y7GWhx1qy9EeyqU0AXAtaf8siO7pSJFqL5abcKbI49LjHRdPJmerKvuMLhED5HIkSlaEcyEdeDFshjOLOBqzjoDd05WgySaTCV73utfhwx/+MG6++WaEUL70f//3f3/Ix9yVA+S9x1ve8hZ88IMfXHghH/rQh3Zz2Dk777zz8L3f+7047bTTcMMNN+DSSy/FYx7zGFx//fUYDAa46aab0DQNjj766GK/E088ETfddNNBj/uLv/iLeOUrX3lYrnFpS1va0pa2tDvL7ulJ0GTPetazcPXVV+O//bf/hv/yX/6L1Oa7A7YrB+j5z38+3vKWt+CJT3wiHvrQhx6WC1lkT33qU/n7Qx/6UJx55pk47bTTmBZ3MIsx3uY1XXLJJXjhC1/I/9/Y2MCpp56KeitBR1owkLFngkEUtb1geRWwF8FOKPJEbI78VFxfpmdYKUaDkA9GuRnRqTwWbwX3d0DM4V0S+5pTTHQq8gQUgoHGAKC8nJwnsjrssGeUMJmjBmOOZBw72MJRmR9PUNZGP8Q2V1sVyKtWUQ+CuEKwHPn58k4KUe3fHmGylfY3Byo0+1PjNfuBwf50rOH+jLlvitK07YLUAKKikIfwckcnK81QGV7ZLqqnFmrFpFG5TxQ68NMKWyYLQHqLjTpdo1PPgXJpem85qqcjPyTyF7druM20vd4SCKKmfIuJsGuK+9H9lFfIOv9FIgpcf65SsJCOKBykcKpV0BZ9LoImilyfUK7e6RolGkRRgFhGixgWkndOnoHkzSzM4ZmBh+YZYzpsq6I2euVP+yhYiwX8uvQcgBS5kKKcquDxNCtotz1M/g7vVfXXfIOVS/8AxFrVyhvkz8ZKFM8ZRKUEPRtFi1ailr5WEUxV/0tDj/r7bJ6UH6h6gjrPidqqMxyJdFODejuPVzkaUo0d9xfbhjJixtBW3jaNov7chwJiYiPmrTNFe6STGiX0qRWyTZnzB4L/8zEtCvgy3UuUSKbqh742RQQTyDlfbQTar5Owyt3I3ve+9+H9738/HvWoRx22Y+7KAXrHO96Bd73rXXjCE55w2C7k9tjJJ5+M0047DZ/97GcBACeddBLatsW+ffuKKNDNN9+Ms84666DHGQwGGAwGc9tXbmpRVVYG4JFjfZXWCE5fTDwDcXaA7PAQ7OUibE5AdFVkJdkma900dY8m518MXF8kpQLA1FdoveRjULJwiEaonMoBIHl/yv1I582OTu2Z0qxzcsgBS9ci+StHNymhaOQ69HkUoAl9ux/g1mkaKb8yXuVrnKWwA8B4WmOaE5v77fTpNisM96VjNQeS3hKQErcp2bDazu029TC5sKqWxNfeHCvGOsMTrgnzod/QCNzQrRhR6VYU8n41Q4KjAJBWEdH3XUCW6YGrPCeUhmj42bREKV5A6QeArsuw1FYNl/OdBgcM6oTCoqGSK9sB9U6GVKZBSqwY5VTUBJ1YLmWhnTw2I45MVFATm9pm21ScEkgTvhT5VbkfnOehClayzovSyerEIY61UxOa5WvVkA1VOvfDnBsyMgxB9CtGoI0F6uaz9zw7eUPljKTcpfxdqRXT5OwU3FVSvGlb5ILHbtyn4r8ADFUIb1uAkmTbDrFXzhBZJmiYqoLJzpCt8nA8aPjvsXLi7DeVlH+g4p8Dx+2l5Ss0BFjkjKnkbavaJh1T5cCpBHxqo2hLGD0yHJa29UOHKjuJ9U5avABAvTHlMjlcrT7GUum5zvebnUA/lPvyjZ3Lk+qHpqD8B13iY2ZRGpooDkwUuFjyfoz0+XEqQAukTzNTVsZNI6odj76ffYnuRIv6Ie5y/68Du9e97oX19fWv/sNDsF3R4JumwQMf+MDDeiG3x77yla/gi1/8Ik4++WQAiRpX1zWuvvpq/s2NN96I//2///dtOkBLW9rSlra0pd0djHKA7si/rwd77Wtfixe/+MX4/Oc/f9iOuasI0MUXX4xf+7Vfw2/8xm/cIfhra2sL//zP/8z/v+GGG/CpT30KxxxzDI455hi84hWvwFOe8hScfPLJ+Nd//Vf87M/+LI477jg8+clPBgDs3bsXP/qjP4qLL74Yxx57LI455hi86EUvwsMe9jBmhR2KVdst7EoNP8hqo6sW42PT/U2OTb/pjgrwq8SQ8qiJapyTmetKaMYxmoLuPciRn2GdVg/r9RTDnGDc2L5gTwEpsqATiAlK2ukabLdpKTTJ0ZV2WiUaK5ASaTPTIWQobNqICBpMSuIFBG5zJnKC7i2TFdwySWERfU2UBL3dDrA1Seff2RjBHMjMrS1K0i7blXQQB3l7NVa11aZxIVXfdhKyYMjEAYFgBFrlVbaIInB0xBmJJFCdoFUDTyrJIx35UVEfpg97jtgx+80GjvrU1nPxzMr5uWfX9hUmfX5ekyYxcABgO7fVtl0MdzF7T622K1No5FFyZxHNygJx3apS5GYGYyySQNkICugMC9xpiKuaAtVOvneSUOgCs39MF/g5mVbgH1Bdp7YFeoXdUYQjRzxQ14hNlj1oKoRR+m57ye7mRG6V8Ez3p1l70cVC4kAUpPM99uBaYEkuIV8KCYK2AoO4NqiEZkrUVYrofZDix9OO7x29l88i2pNlHPT9D3Ioo2kQc10y5ChIGFZc5yoMnMBZlSlgTYAiIRIdEdacagsF+VDUZxE9P9Rl32GFaYoazSQQzyYTV1OJ4DabPer9qcHtthBOKNITBjU8qTqvV2j35AjqOvVpSE2vtZjkHQBEhqMDTCYk2ErEPa2L0MxDABxlB7LURhbvjFvp/NWmRdjMEekg72Kz6bn/W3rebYpI217uaWlHxs4880xMJhPc//73x8rKylxx1FtvvfWQj3m7HaDZnJsPfehD+PM//3M85CEPmbuQP/mTP7ldx/y7v/s7nHPOOfx/yst5xjOegde//vX4x3/8R7z1rW/F/v37cfLJJ+Occ87BO9/5ziIMdsUVV6CqKnzf930fxuMxHvvYx+Itb3nLIWsAAcD4pBX0x48wOTq9DNNjgPaYPMDvSW/5yvoU68PU+Zuqh8uzBbGirIkFG4ryeiZ9xXAVsaoa52Hz21qZwBTqJo/OA9uzAzQNFbZz/HfDDZWTlc8TDWiqiVXg/BKS7He1VDYe1D2GOWeFnLHaeobeuuAw6dIznfYO42k6r8/wjW8dME7f3ZZFs1/gLAAYbEjBWNsFYX5o1V7OD4gqHyMwHZzNKBweRmAUK5OCrgTNTs8QqhBt2t+v6MrzUhjSjlJbJPZberajpuPnUVn1PCmvR1H5fbAYZ00VggPHU+X0jJ3IFmxnVtVOotkCmeXVEbSXz1OrfCUvCtehErYQTRb9qHQKeLLgXLVY0oup1ISu4q1p25SPYSDOJU280fLfbZRyHrwQ8gHo8ozYdYgZCjLOcV4a8ngR1kYIq6lv9asVV/Em2Llbtcxs7Pao4q2kdTMI7F2b2ktBSyNwMOhTM9q2DSv7VpTH0hmhsXeGyzuI4xfZKTI+8oQYhk7gv1l4NjWMgiwzVFVbhvsKp4ZyWhqDnp0a6f/AfG6TCQqS9ADyfWkIUEpxxEK/aNZRCLVAjn64AF4MKm+mcBgFHiL4KFqDbk/O81tvuB9Nj0rPfnK0xXRv2r/boxYhlDM5CDB5kWbrgErB0EBajDDybYOwz6LhcYweg7MCRxsDeNIdo1QFK+3pOtH8GezrUB3IK4N8sDCq4Vdq+F45uHe2Rcw5q4e8/9eB/cAP/AC+9KUv4Rd+4Rdw4oknHtkk6L179xb/pyjMHbFv//ZvR7yN+Nxf/MVffNVjDIdDvO51r8PrXve6O3w9S1va0pa2tKXdlWzJAkt23XXX4eMf/zge/vCHH7Zj3m4HSKsxf73avtMd4tFGViMrAonYWiI8LCqnIj0ED/loOaoDJB0cANhpG0zasrnryjO8Mqo7rGYtmaOatOpYryfF7yn6UNnAjKuVJi3DnI3oG2FGaegN4MBJugcbOVJBic0+WLQ5wtN1FbpxDh9MLexOjvZkLZhGRS8G+yOG+3JkaV/WwNmYwG7ma+86uQhL0EfFsEB0RhI+nQFlGevEZhZtMSosn5el0RilAGzQ50hIt2bQUTFQLg4a4VfzM1vpMVjNUb2seXTUcIyjh2kJvaeeYDSD5U1Dha0unWB/O8S4TW20NRmgzc+W9EFia2HH0m6sYJ2TnKsZzRUyjnZFaQNTQUWGRDhPq4xrTSDWMaEoQTQMfdhWIh1c16nVrCdu2lzUNvePnHRruwibtalc52Cz3o3Lyat2WMF0FI6CiOFZJYaX4Z1+zaFbETiPxTmJcbkqLMd+VcQ5Sa+qrj3qal5Hq+0qTjb3hogDElzsYRWcJgmxUmDYqPZw6u8SadEJ1XRiXUsPC1anWsdIiBRljTIgszyV9g9HbRaJLnpIMVIdPZ2J7tCx6F0JKrJEbEjNIIx2/hAmSv9MEFz5i8Qsy78eWdWP5NlS/a52XTEuB5GLCHOlXRP5YqM3DH0HVUBYr51ZJDYYYVf2SkONhurOwuUk6GGO/jX7gMGBdLDRrQHNARKkiuj3ps7Y7qXIlcN0r0m1wP4KSzuC9o3f+I0Yj8eH9Zi7ygF6zGMegz/5kz/BUUcdVWzf2NjAk570pMOmA3SkbXpMhFlXL+NAlJpZQj4Y7OSJzwfLUBTlgPhgOfej6x1PjN24YpE7lmI3UV74RkK+o9UMwww61KpsgTg1Bh2JEuZBKKgilr236EnNN1ctjp2V83rDcABXkx9LJfSmZ63GojyEFkxjtsdWKMLeABBGDTsypu3nvTBjWLE5WivfK9le0IArxRTiEgKSC0ETSILA6LswWbSjQDCMcZGfHeUIOBsYvqytR52/d3m2CtEWeVgEE7bTGp6eLTk9UynSWk0AR6q6Kg8FCsYgk3ILauYyKJmHihpOnwVln3ZVcIdR6rfchnkCwqikg8t+RvqHzt+iAqVeS0bQ86yBlZrvgaEz9RxFBFDlrwwUq0flobAEga66voBdl5I9Df/d6mrtAEwVEUkVPQosS33DdkpVulHtQY6QKtxqtdp1AE/UQhePJb1+QZC7KNuh+ycwx2jTxZN14VMA6INROTra21Gf7MGofqScrIIOr6FSVYw3HR8Cf/bSp6QavBTw1XRyfd3sMik4LTrAZgeHnpHpBPeLBvCcyJQ+vIvMtoWJAvl7I9XcqYjr2PCCzWlnX0kd0LOb7rVo1wfcHlTsmliWqRgx4Mu16Z1vXycw1h2xX/qlX8LFF1+Myy67DA972MPmUm/27NlzyMfclQN0zTXXoG3ny+FOJhP81V8t3eKlLW1pS1va0g6HLSGwZI9//OMBAI997GOL7aT75/2h52UdkgP0v/7X/+Lvn/70pwu1Ze89/uf//J+4173udcgXcVex0ESYkWf9F1t71tGxWrAwR18mnURdaFvXO/RUg2lSAwQfKdaP4xW0Xtk7hMyIaVfTCmQ8jIikRTNIhVoBoGl6Dv3TSncyrdHmaI8fV7Db+bw7KumWC/sBzVZOvs4sh2oalUaHhLJDZdT3tH90Kim3Mehy5IcSN62vgNjwPbJ4oYKqFgqeLXpPVdg+WpUYvKg8gFq12h6IrRyD9udoU2PnapTpZGcfDXzekaJ7bXDY6TPsNW2wvZ2FELdrbmdOch6jiJ4QZMERBR/LlXU2/mrlHvuhwHhaUFC0p6QIZgrZ5a8EBUQgjojaFVkck9utk2iVndjyWmnhzUJ1EnHzjVWRDEkQ5uMKigETYkpiRwkFadG6hZo+HMmQSIeO9OjkVoamY0Swsl1fDx8rQylF5It/qK6RFpkWopejy18UpTDkPAyHWcBzpEVBTQuKJHNERj3jBENK9IO7LLWVjjAV0VqJ+JElIUSKqMkxqM6afifTuyTwYDqXKRLnaXtQ8GkBpapEbD5Gvi83UdCeV0QHJw9M3pWZiFu+70X9RJ+L4cKJiljvSNSSojvTvYahVt/IcTUjUr9zfhARjiS3fJkEDSARrw636PIhOUDf/M3fDGMMjDF4zGMeM/f30Wh0t05GjmsedqWXfB/FHuBq2QBXUUZUmDSLEDp2euxYnB7tgLAjMo7FhEFwANGbU/FOEoerGD8f7/EYD4kmnjvExHJF49GmkVwTrhAfWViv3gmcu8GidVEgrFiZVFcLgHGLO5xMYkYmCTXB6QmFq5cr5VYN0xAEpCcTGdDi3OSkzYgfkRgxnZn/HaUTWRlgvXOYIjkwt/bk0FbYyiywtWaAYWaBkaO02Q5w607Cjba3hwibRKN18pxV5WzKsamm8pwXKToDyhEmerMTKKjbqxhQTZRJLDvHxkWRONCQRzbrIkz2YLSjQGwY31l4ysMyAM2MVUDJGgIKJlHK+SDoi56hPK844wwxikFdtwMsK53PQ60F/KreNYI7us4VytpkvnfCjiRpiNYyNGI6U+T7AJkirqvYz0y4iyAhupfZOmR+IPlnSTAwX8sCBydW+tnTZ0SkSuZW2hYG8wrvhTOrv9NKAShUAGf+PLc9/yG0loukopuH/gonUb/LSjGcxTshY0tRR40clB25HpZrUMKVtlOLCS68qtSbw0w/UyxGII03lOs1PsbO1fibkw9QcLM8p/z+1RGxiQhu5qEv7U6zN77xjTj//PPx7d/+7Yf92IfkAN1www2IMeL+978/PvGJT+D444/nvzVNgxNOOGFX9POlLW1pS1va0pa2yAzmVjWHvP/d197+9rfjec97Hh7+8IfjggsuwAUXXICHPOQhh+XYh+QAnXbaaei6Dj/8wz+MY445BqeddtphuYi7jNmI6C1riHhNdNCm2Am8eMqV2m1nmC2ltV7qHXAEhoX/djzrvxgf1cqDEoFVBGigSzeIQByvmLws5NxUVkzVVM7plIAdrzoHdBwFS9lSm2S2/pDxUaJFVoWHq/ltBYtFrw5VjSXWB/KYSxg1QUFFIc7BZaGGsKUsFMxWQi1yvLyabS2LC3Y5mXNjUmGrGsqxsgUSlZxUvCp2OxYjivZsi7AjQYv1lke9TTWiPN8jJ3k3Fp4SvRsDTww+SmZW1ej7IYS9ttrD5QglCTVGlQAfvJUIj4qOMDtNwT+cONpauCndl+Goob4v6rtuqiI8CqYzqj8wJKSeffSRaVgMU1pJoA01OHpHz8tNoZLdATOhKGsmIejnGiFsQUDeWy+RB372KuGZ2VRTVQNNsa046dfHuUgPgKL8RFwA5xX9cEHUJ5r5fqpLTmhRRwDCJ6B79RGo6cKMJIrnCIWpI8OAMcozjwUlLG/rLUx+BjqJX9fJItNJzFIuRaAmN1XlU2bGCwBFxXttRdkOdS6JDkoklb/rZ+NjYo5CIPnQSBkVqD7JkKUXGM8PJfIa3UwSPoDYxKSzVh3BCNA9HAL78Ic/jH379uF973sf3vOe9+CXf/mXcdxxx+GCCy7A+eefj0c/+tGwdkFnuh12yEnQdV3jz/7sz/Cyl71sVye8S9uOSzP+V3OYaSCNYCaEYXqqDq9rxoF6SSNNhoapyrpOD6nP2haoxmm2CJVFlSffbmyZ9aMxc9rftSKORgOS7SMMOVtBzqGZrDzZOAPLCQIqbE0DqZEdo1X4PYfq1eCmIK6inhQ5Q11gxywVUKTrojZywghrrNQHIsewKYtBesX8IuhhcU4J+DkaYsf14hTBg+neVXZo3QQsoFfvgMUem82IejM96HojyxJsT0UhOESm/YeVJrfVABjS5K5rHRm5fv1O6/wVynWx84Nw8BZ9ZtCEqbAOyXkwEEeD+5uifWvn2fbzkOTBTJ63EUhC9WkTZcKKB3Ek5BwKxqRr7Iw4/SwYGLnwcIJkFpxXn4v7cZRFjMo74uMacez4iqIR1t4szKKcFfq7/m2MM3/3KHPXyElk590k7xE5X0jln0QutJx36o2oRDQBhnKxKK8nGIYO0VtWiEdnYEkQc0pjmFnIBlyUE6PHNob2xxHNVmatjgNchtmND1z/jfOgBlYtslTRZ13EVTHWyKyXdqHzB28Kx6ofym8WtaHeTvdXFomlsUf6FPeTkBYMpt3dhLu03dnRRx+NH/qhH8IP/dAPoW1bfOhDH8J73vMePP3pT8fOzg6e+MQn4vzzz8d5552H1dXVr37AbLt6ik960pPw7ne/eze7Lm1pS1va0pa2tNtr8TD8+zqypmnw+Mc/HldddRW++MUv4i/+4i9w3/veF69+9atx+eWXH9KxdkWDf+ADH4hXv/rVuO6663DGGWfMeVzPe97zdnPYr7m5sYWFVVBQxFyiH3Q41nDkp2Bd0IrO6ZWF4dUEi4UZIzolEcAMs8DMdF6u0TSRaJJR+0j9IomwLCo5ka4tr/pUoiqH+xERAyXYynVjJowN5IAZwRgMganaU14lLhIEFxTspWp9hcYiZuE6v5Ll6odSwb0fqggPl3lQTCLFbCkSGIttKmGSGSXUQkwn3gABAABJREFUFoaTYt1EEsi1cCDDl9MIR8ySbY9qO3UEO06NYTqfykIAKSLGNZCyCODIoV3LEb0VI4nvpGM0KoXi2HYcfIardkBhQHUPneEVfaMgDA3psP6KSjjlNunLgBm1cx/LVTXZIuaeo6TZXtrIAQBHF6g/RH5vjDdwLem3SHSRo3uN4TImxNghFk+6LonqwAqczH1X3WMwpozWAEX180WRL50cGyOKqM9clExDNgFSUVxBvVxZXjHtIrPrpBRGvyrCkH4EeA33Aik5N9+ErfxcNKow1feh4TDqBwoVW7g0VuORZltRZDeVx1CwFEVxa8sQr5SqMRzt9I0RTSslBklWJMMrZhjdbIra5P2UThYnO9cCZRUJ5kpHSCBFI7DpzP0CeZz3BpgewQjQshr8bdqZZ56JM888E6961avQURme22m7coDe9KY34aijjsL111+P66+/vvibMeZu6wBVWwY2zCiz0kRLL4tTLxAgL0v+9FAvS2vgM3ziB0BNdYdIoEsxGdKgWw5IRS6BooBDnSOCBh81qHaqPhDnXUSp66TqE3H+jBqw0ANmEU6h6nCRE2dqW+T+AHkQIicvitouO4mayhiFVYSo7pfYWnXC8IHM1phh1MzlByqYYdZsD35emEp7G5XLQM5OtaOcHVWslZy41MbCpDN92V5hVAMmw121YyVlP8oO0KoTx25FiQAqhgqH4mcKfRIrx6iB2rTSt8hx477VqTwONTlzk9nF7RmtEljME5KJphhPdZ0qILVx4PwzFBMx/1RPksRG7AT+NFRgddrBtPIgw1pqHFLnnRzbYHJUaph2XajMc8KQQEn9t1H1GemnnJvUanE/+pzxKLQDNAtx6Qk7qHdQOYAa9pL3Mm9TuWwpXy5vP0g+Ed9CNIgEcdFYEkyRGyWQvWEotIDz6CHpfkLrn16xsablYiBtEwfIBLWwcVpKgxxpJYI5TMKlgDgvcRYCXmBfdV5fsHhNF5qvkXKnTJT2Mla6Rm/mpABslxa9fvL17VTcFY1qhc6aMQbD4RDf8A3fgPPPPx/HHHPM7T7mrtzYG2644aD/Pve5z+3mkEtb2tKWtrSlLW3GYrzj/3ZjV111Fe53v/thOBzijDPO+Koix9deey3OOOMMDIdD3P/+98dv/dZvzf3myiuvxH/6T/8Jo9EIp556Ki666CJMJrdPVvuTn/wk3vzmN+ONb3wjrr32WlxzzTX47d/+bbz5zW/GBz/4QVx00UV44AMfiE9/+tO3+x53FQHSRsVMD7dA0dfC6o28guWoj1FQi0pOZS2OBQdRKydKMkz7y3EZgvDgaIiGFmi1XYSG1UpPwxh0jlBHZvJUCCpZMa+wpx62k9U0rzpV9rwJEhqYW/ECM2UqMqSDAJPj8bxadgIxRAsVBdDRJjqoOoGR1TRX2A4GkRhj05TonK4f/LmQPWNVGN3Ip46KMLRFTLlJZPil2vFwE2Jx5YrmfShHk0V9noQWjZHlhY9wU1/sk/rWfCK3TtKmZxudRKtm4ZV0XVLGxLZlEjPdq07Gn4VHopUSBqGegQ5nGEoFS0+hTrxJlVjwKloUKoNAEC3rykiFdQCwXFNLw7L5JrsOdpoeWHMgZd02N9VYb9LJwqhGv5YasVt1aNdzKZg1k7cJhKbfW6OSvzn6N5aohlW6M4v0qKLRz4P6rPqZbiNqC2cKdltQYwv9vajzpiFeHSVCGouY5eVnw6EAgpGoTycQr/GS8KwjPPwO94oVpxhzVPld10PT7zpHfmH5XYm1lTI5KoLrtQ7PQL4DKRJEUbv0jtPAkM+lxgrNlEsaTmVky0Rw8rcx4CgZXROMtJFtrWhDtaaIeFG72B7wUxw5U1Dcrvc/RHvnO9+JF7zgBbjqqqvwqEc9Cm94wxtw3nnn4dOf/jTuc5/7zP3+hhtuwBOe8AQ8+9nPxtve9jZ87GMfw0/8xE/g+OOPx1Oe8hQAwB/8wR/gJS95CX7nd34HZ511Fv7pn/4JP/IjPwIAuOKKK77qNV1wwQU45phj8Lu/+7tc9mJjYwM/+qM/im/7tm/Ds5/9bPzgD/4gLrroottVSB0ATLytcuy3YW9961vxmte8Bp/97GcBAKeffjp+5md+Bk9/+tN3c7ivqW1sbGDv3r341u+/DK4Z8vZFA55XxSj9oMSZgXKQQlQTrgqrM/W2jaqQIWRy5dCxOq5VA1ucySFAGphocHKTwBOu5Um8ExaOLjrqZBAQ9bjSGSIniVWUa4tAmH5tGaLi8HajITaZGLgtfFSTginhlwUQFu2f6n6VDpCvZwZ8GiBVbhDTXjsJ29fbAdVY2GepLUPRRgxJ6LpkzFIpabxzDp2C7YtcAmpiB5UXYRRlN9/XjDibdkBmRfpsXw7QPEnpCWCB41bUhVJFLj0vABQMoQXsFjHDFsE/WlAwxkKlO31Kn9UsRWIPuamHHbf5voK8F7mYqh/V8MOcK7ZiVZFYUywiAIIUwfc967gZVVTUTaQN5frkndNtqfPZ2FlqPWw3L0zJxWAHFn6UHbQVBQXVcq0sgbACdtz8KCKQovcosw5HPZosh1BXni+NhFunXYVeF+olMciJTPRUHNROy7w3ZnfRomCipDQCStYaZiAr7cNWphgzgST2qmu/+dm8nUalGgyCUP11HhOfyyASnNdZhrlpAWE8iveygJbz/vRbt2NEwHSsnD/lDNo+wrcTfPIPXooDBw7sqgbV7TGal+79ulfCjoZffYeDWBhP8G8//fJDutZHPOIR+NZv/Va8/vWv520PetCD8KQnPQm/+Iu/OPf7F7/4xXjPe96Dz3zmM7ztwgsvxD/8wz/g4x//OADgp37qp/CZz3wGH/zgB/k3F198MT7xiU/crhJa97rXvXD11VfjwQ9+cLH9//yf/4Nzzz0XX/rSl/D3f//3OPfcc3HLLbfcrvvcFQR2+eWX47nPfS6e8IQn4F3vehfe+c534vGPfzwuvPDC2+XJLW1pS1va0pa2tNthlAR9R/4hOVT633S6OIzVti2uv/56nHvuucX2c889F9ddd93CfT7+8Y/P/f5xj3sc/u7v/o4Tk7/t274N119/PT7xiU8AAD73uc/h/e9/P574xCfermY4cOAAbr755rntX/7yl7GxsQEAOOqooxbWKT2Y7QoCe93rXofXv/71+OEf/mHeRuqMr3jFK3DRRRft5rBfc1v//DhVAFeLDK5gnVk8fmjRZ4ZSuyYS67xy0RGgIKtlCp0CUAnKM6ummfC1URGkxFTIX31Uom2Rtxm1QuUQ+5DYR6JvlKqqq8gP5LjpmAEsWqdD+KxNIiFtOBGDpOtzE5VQHUvNHzKOqlTgZEk/UJpCGjZQy3WOVBCLLcrKO7FQpO34voJEGagECK1kqT0AoFutEJS+kGjzyDkJPtECdoVQmxIJ1JW1taBeupbytxIly/fVo0x8Vm3M0T8uFSDJp24WqgFFkCSqKMKV+ZjGzCEnsl/+jTo/RXI0/EOm/+46FfXxun/l3/pYwkYcMYtyfmLPDWsW7ezWiElnmQnWD41Uka+l7RYJ6/H5oCJUeptiTOp3jtu9DQLR+qDewfxjzYByhiM/FDXtVxz6lcwAHAk0x4y3gTAA+/WIsCc1WLNnimPWUljmxNVNAMAxg23sqVLIwpqIPoe+tnKn3ehG2D9NDbMxGWJrkgaqyU4Dv03Dfy7d00mNP9fq+ln0zkR+h/XzYu2eSkdojWJ0qe8qgis15STyGYY52jsIQC73YwceVZO+Uw1EAFIaJRquv+g7h5hFPwOJ005NoXVEJBQNkVsV/WPx2jFQUfmg7XTf1TjATQL6/vZPsnfUdBmZ3e4PAKeeemqx/eUvfzle8YpXzP3+lltugfceJ554YrH9xBNPLOp/arvpppsW/r7ve9xyyy04+eST8f3f//348pe/jG/7tm9DjBF93+O5z30uXvKSl9yu+7jgggvwrGc9C6997Wvxn//zf4YxBp/4xCfwohe9CE960pMAAJ/4xCdw+umn367jAbt0gG688UacddZZc9vPOuss3Hjjjbs55F3C6v/YRGWnAg9VDjHnGNgcdjehYkcgGitiYGM5jmZPFHWT6IUn2KZWOQhdVDkb4tTIQY2aQGTCA+dNlPcSVa5J+sF8yFqbdsZCZeXcQSYpotQ77yXs3QrDAw3NliggEc4DGZLTY4q6Z0Jzl1yAojaPMj1QASlMzxOyMwjKey2KX+bzxhUSUlTnJYhBwySVDJDs9FgVNp9xgAS6o0bWE72GQtPfq6mIKrpuXrZgVnaBpATcJMJNCLIjp0kJSCoT8Tlh6qEx8Cr3ge4rKBgjqomJ82UUNFkRrVuxy8ixK9iIbfksNCwKALE2Kg9DnmM/snx/XDfMyuTZkfOwYthR0E5DaIAwW/DVm8Lp0XWm6P6yAgOskn4o+h+9BxHKcYuF45O2BQUbO3bc6L76kUFHfW9V9T9y4AZAn5W/43qP4Z60Uj9p7wbut+crAID7jdLnMdUW1vPLMAk1DvjUCLd0awCQFnR8+QatT9cyQSMMVs5/UZD6JLLjU2+mxqp2emblmRAEEq/EwWO248AgGoLMZczT98pO3mqEJ2iPClEPe3Z6hk3Hjo8jlXNVIT1EA5+vpa892ipDfi7nh1krtQ07eQf1+0njSa3U+wcbge+9zjIXbmsKM+lhj2QS0GHKAfriF79YQGCDweA2d5vN66Wq64fye739mmuuwWWXXYarrroKj3jEI/DP//zPeP7zn4+TTz4Zl1566Ve9jTe84Q246KKL8P3f//3o+/Q8qqrCM57xDNb/+cZv/Ea86U1v+qrHItu1DtC73vUu/OzP/myx/Z3vfCe+4Ru+YTeHXNrSlra0pS1taXeS7dmz53blAB133HFwzs1Fe26++ea5KA/ZSSedtPD3VVXh2GOPBQBceumlePrTn44f+7EfAwA87GEPw/b2Nn78x38cL33pS79qOYu1tTX89m//Nq644gp87nOfQ4wRD3jAA7C2tsa/+eZv/uaven/aduUAvfKVr8RTn/pUfOQjH8GjHvUoGGPw0Y9+FB/84Afxrne9azeHvOtY5WRl01QIgxQS4FXciuPESq3BQRZNmbisK0EHBZ8AaV+KUvTezCXcuakpE1opkqJCzWRaW8QEFAKIQBlGNbpKs0rULZgalUBMCwUa9f1SMi8lnB4sMVnBMBLuV+HxBTL1hQx+r6JUJNA3Ep2hdD2G20DuV66D91uRFagfUZJ1ALgKd5R6YLnuj60CqlowE5tPEAEETyH4HHXpHWIOwZvWcFkNYpX0E6MEFoVtMssgousvBTXzfq1EKUzeaEIsEpZnTUNoi8T8UsQuJ4QaIWFRP9YRPS1y6fj6pXP0K7YQqRTYle6l/M46hiqxX1cf5ygCRw50RCGK5k8d5d4oujGz8tdsJ2qXUig0fdJ7lvoT4Wqh0IchDSjSL0II0rSdA7JgpdT4A3y+VtOLmKqGOen4ceIwrVKn/bJbAx15o0s3fmyzzde87RvcOk2N85WdJIq0OR5gspP1qHYq2J10Y5Wu+ZaTnevtVNYFAJoNj3qLoh5pYLLTDlB6V4bHSXrmlWK3CeQeKszpXHVrQL+WozlrPaq1dI6VUepIa8MpVur0fU8zxTBnqNM71/oKG90gf3eYUH24YDGt03W3OXrfDir4cfoetyvFfpNnqMVOa9b/ClyKyI4zE7TtYboextNBjoAdYSHEpmlwxhln4Oqrr8aTn/xk3n711VfjggsuWLjPIx/5SLz3ve8ttn3gAx/AmWeeibpOz2ZnZ2fOyXHOIcaI28PF+uAHP4jHPvaxWFtbwzd90zcVf/uN3/gN/NRP/dTtuj9tu3KAnvKUp+Bv/uZvcMUVV+Dd7343Yox48IMfjE984hP4lm/5lt0c8i5h/fHrCMORYPYDiy6HrSnXQNNp/UhN1OTcVIrhBFEYXVRIEUAhwmeZhqvgCGIyBUBmC5VLQiwVrxybBZhxcAaxppwWO8cuS4Jsci8sXObk2nUOk1Xvv0BrdEzliMQ4F76NzpQUWF2osGSLw0YFHQ4iT35a6VcLGhK+b1uBN3QtMz7vCOjXMsywlgf6oUedGTWDpsMgh92bKm0bVR0PwAAw7dOFTX2FcZduYpoZN10X4G3uR9YpLTpJ7IkKgso/ZSaS7SCOjFE5T1UEBso5BRAqV+TSaKg0n0rBmIzgFuw4/ZBonvcDxeajy1ZjsWZ8yb6KPadyiJLTnrcz3buEmObgTm+g2Tuci6UmU59VsmMVlbIvxPFRApEa8pgVyoxW8lCiFQaRVX1aHBQlOAiwl8i13yYtqOCZaURi2mVnyg8MjPLsZ4sF2w5oDuR233DAl9Nvp80Q/zZIK/gvMGTkQQVQ0Vsg13xzO1kSY8dgRI5OoW4ueYTEhnSTmbp8lO/DdHbH6QFpB+XsIEGtxGzsh7YYM7v1tAt/7vEwe1JnX1uf4oT1lNN00kr6PHW0D8fVWwCA46pNDPPL7HPn2gwj3NyltjjQj3DTJH3f346wb5wGiRCySGY0SRASKSeTID+WPdhO0FdqC5FmKBaFOZcy1iMgAn1fA0dK8u4wQWCHYi984Qvx9Kc/HWeeeSYe+chH4o1vfCO+8IUv4MILLwQAXHLJJfjSl76Et771rQAS4+s3fuM38MIXvhDPfvaz8fGPfxxvfvOb8fa3v52P+d3f/d24/PLL8S3f8i0MgV166aU4//zz4ZxbeB3anvKUp+Dqq6/Gf/7P/7nYfuWVV+JlL3vZkXOAAOCMM87A2972tt3uvrSlLW1pS1va0u6C9tSnPhVf+cpX8KpXvQo33ngjHvrQh+L9738/TjvtNAApD/gLX/gC//5+97sf3v/+9+Oiiy7Cb/7mb+KUU07Br//6r7MGEAD83M/9HIwx+Lmf+zl86UtfwvHHH4/v/u7vxmWXXXa7rumKK67AE57wBFx77bVMhf/VX/1VvPrVr8b73ve+Xd3nrnWAQgj453/+Z9x8880IoVwGPvrRj97VxXytjPQWHv7Dv4C4d1hEeJihkVdcYRCBRmlSzLZeRLlE5pWzYAe0ejRadGtilT5QPvxUw2ES7akmkfVsdPmLRUZJ0JrV1A/m9XQK+KmoYaYSj5WmUaFFo0LdtM+imkRlqYy8UWuyqCiURGoi/Cppn3jYDEFR1evoDUKfd5qqque9UdcokTOuCTQKsHtyiH1vWhYfv7qFE0Zp1XlMvYNRbvw6H6iLDls5hn+gG+GWSYIZvry9hu1JumDWXOmsgsCsJJpmNoprDUf8nBIvLMTddGSAojXq+6JSAbOVyNP964egfquYTsT4cdNQ1pTLUQDWtQlR2ICVYWjYqwR3XdFbSh8IhEUwyGy5g6KmW/77orp61DdiFZVIoMBeJqT3Kd1P2jYXEVRJ+rfVNrRPNYmoFSOo2smJwVsd7HY6icmaRbGp0Z2Q8hImxzVo10pouIDDXfkdyJDmAi0lq7V31LvEY0yY1xpzWtxzLAn0bhrgJrl+HZUb6QOMnwnBAiKW6kyhCUZMTinzYjlBvV2zEu1ZA7r1dA39eu5Pe1p+7+61fgAPXP8yAOD+o/R53+YWnOQOAADWbQuXH84kN9JGGODLPp3gi92x+NfJcQCAz20dh88fOBoAsLmZBm+/UcNtZehvyzDkJxEwVc9sGvl7etfyOEPRLpfGUd9OcP27jowO0KmvffUd1gH64sWX3qnXeqTsV3/1V3HllVfiox/9KN75znfiF37hF/Dnf/7nC0lZt8d2FQH667/+a/zgD/4gPv/5z89hd8YYeO8Psudd27buDWAPuPhkaALj21Q3xrgAM6NKCijIxgU4lwcZF1C51Ba9d2gJHplkPDoYhkaiCSwoSINumBjEDPGbHnBa+G6mwKjtAucYaNozMVfKWmFWxMBm4BAyLVS4iGmmoRHJbZIcD2Z+1fJ3gi76oQz2+rgpfyrnBZDTs+pRDVODDAY9tydZCBY95d+sWg51+94iUB4FQWQ2oh6mQX99dYKjhmkAJqfnhMEmjq1Tg++tdgrHBwD29auY5psJ0SDkm+y9FcdnKs6YnVLejxGxOQVxURHZovimajddF6zPeUp+JSBmpkxRUo0nTKPyXoT54lo5PzFemPmyLf3JBHDeg5v0MBPJfUjH6oE+P4PKIa7k+lx78udqzc6Qh+F3JUFK9N7kZwTwCKTlIxaqfOvvpBBcQRSCoaCNiUxy9Gl7KCkCdSkLnEwN29LfnXKggjOY7k0XPj6+hq+TI8zFPWcEUjWLkD4LuHhGagBB9RNVfwtB5Vp5+SSIyviyLldqC5XHMvFcrNdOO6DNJ8mMGmhH2Rp+TqT0jrpCzMV8Y1Mh2LyKyc/N1wb9UPK0ulybrd0b0e/NbKrMaNuzNsHJa0m75bS1W3GvwX4AwDEuvYsrZspwc20i6vxAfH6ITmGvXXQYZ/XOia8Yhu530me14VBvpuuqN4XFWDo6uQkUQ7Wk7+d7HKRxzE/vQE7OodrXAAK7q9qLXvQifOUrX8GZZ54J7z0+8IEP4BGPeMSuj7crB+jCCy/EmWeeife97304+eSTvy7KYCxtaUtb2tKWtrS7jv36r//63LaTTz4ZKysrePSjH42/+Zu/wd/8zd8AwK6KsO/KAfrsZz+LP/qjP8IDH/jA3ex+lzW/GoHVgEAr7MbD5ciPIyaQC6hyKEZHI7QP6LL6oUWEz8u7XgUubN4/NABy9CLCwuQEXmKAuAmQ8wBR70jZDMSUZAgAYV0whFntmFkTSCjy6p+SqU1fCujxsVTJBwm/i2BhSpTN33OUtluVZMd+LYikfaWW1VEl8vp5B5p+a0KW8AcwCRYuQ2B2NmSF9DxM7tF2EGFoBZmTmVebFnubdON7mglW83La5hsf+wY35Rv7t8nRzLg50KZQ+k5fY+LTCSZ9jUlOgu56J0Q5Cul5I8nZKpk3qJU/lAzHLLstDGISgwOAkYcbkCZKzxFGinx10wqBIk+9lQRegtuU5lClhN6a7XRRzaZHvZkTwXc6jhKYtge6HB1gaMQCWfclDipmTPJ9GImE9CMjzMBKkowD3asq+VDUbluQGK1hK4KN0aJgY2lIY7aMg+2jipoocU7q/7F8fwi604KFk6NyRLEqf6ujnUCGzldzxGIYUykHQKBz3d1VpXauhOMNQ7m2NUVi/xxM3kp0z/ioSuZIAjyLmmqIyweJ+PBnkIvogiQ8U4Jq5SDaUVaVwJBPhjxnYHRK1LYqYtfmB36gG+FL5igAwE7uJP/R78VKvkmHAJ87xb4+hZVumu7Fv0/2AgD+fWsPvrKRtk/3D1FtpOMOt9K1VNuqb0zmE+B19DE4qb/oVVpAVOxU35Ts1DvdjjAL7K5kB6ss4ZzDxz72MXzsYx8DkJCnI+YAUQb3150DNAqwaz0sOT21R5UdnyZTKwdVzw4QkGiXAIoyXnpyNhmbcE3LE3GXc1am0wr9ND+CqYXL7K9B0jjD8NaIZosgLBEkJBwakIFaFwfUg73V4omU29EGyQthCG6mzpVijHFYmz5XJbzdrYtoW1jN4lQrPZpBprXWPTuHdN8hGPg8efedQ8gODsZOcqIOuHzf3JTwDdCt0LlyLlAV4PLEUjXybKwNcHmwHdXpWlbrFk12Wivl7Y1zUkkbHCvp9tFiq0vbx5liuzkZYNLmgpuTSq5b1R/iwpNe6M3RRXjFEkztDXYMYSMLLFJuE1yEzX2PqfcAjIncdgvNA6YrWS71hrBc6q2IZisL3G0R1OU5D8T4qATuGhgqDKaM8yEGtcoBklygbiX9vV03BRSkRSaBGfjUCZtvkUCdbRX8w+KhMpklVpPAP5Sjw6KiStAzFbXNN5Mnbz9y6Fazwvsex0KFzAxTEFxwSrRwBHTrxCbMrMH1KfaspMYf1R0al3Nt8guaFJtzroyv0GVxQq8gVepn7bhGnyncdmxRbRO7K1/XWBwN54wUajbSR4hxlqQw8o37KM4Qmfeie6CNHCBdWlzB7CUrkBzKmUmXfazc3sFgml+KjVbyW7Zzh6mMR8gNvu0b/s2Xd1Ju1a2bK5juT9vqWys0CU3DcEecQ+5HynlO11YunvSCrsjPUk67hqPDICLY8hh3ph0uJei7o91www136vF35QD99E//NC6++GLcdNNNeNjDHsY8f7JZjv7dxdzeFvV6ZEdlUPdYadLbNMiD2ND1HBnog+WBrLIycNBAF6Lh79O+wk6eUAmjDr0Fxjk5b8NhmOu3rd6U9hne0jF+D6gBpzKI23mi5UFdFGl1JIcqjodGylf4gS2So4E0cQk1Xaj+/SgN8ul7vq9hRBjlyXOlRzNIbbM6TG01qjusNaKUSm1AVPE+Wv6+sTVC2Erf3ZZl+m9OCUCzISUI/MCgW80TR4589auiJzIdBEyyM2SqCJeVZLczFXlrMMBqfp7HDB3W6nSNNNBqp2e7azDt0nPanuYE596hHadrjVPHibamM1zCQmu5kKUK6TQDSD4SaB9jhMqczXihk7dVnPt78dup5ShAvWVQb+a2O5A/twKaDXJ2pFAuq/p6L0VgjWF6c6wdfJaEYOdYVblP0gqS/Ey/Ex2fMhdGOxN87ZzXZoq8FiAnp1KkY1wW5QSSc6Nz4ShR2/SqVAVN6Mbwu9CvSZ5Sz59GldUQB4eVySuh3IdhRMwOeLXaYj1r1+wZJW/t+NE2jh+k0O1R9Q4GuUFqFXqgSMdGP8R2n06y0zf5s8b+aXrp9tkRdqjkQ2c4R44Sqk0/kzjvlTdCm7XUBeX1qILIrItQVcrBsUCTx/U65/0M6qIQbU8lSXIbditlaSCO+DWRcyhpQTmoPNbz+7enmWBvPS7aKOX1pONvdQPcOknU9n3bqV3afUMMvpz+3uwDmtzn6x0ZL1ghXpEyUkHj/F0nousID0dhVfHWHNELKx4YBoQxheOXdne2XesAAcCznvUs3maMYansu2sS9NKWtrSlLW1pdym7BydBv/CFL8SrX/1qrK6u3q7fX3LJJfiZn/kZHHPMMbfr97tygO7ssNTXytbWJxit9ix8t1q32JNXKWv1hH/X5qVD6yv0FD3IkaAQDWPbnXfYbvNKbzxEu5OjB5md4LYcRvvS0mT0ZWDl5hx5ujWtKKvtDmYnfTe9Ck/HKElHnI9Rw6+kc/lRhW59vv6QLu6plZTTPqJOG52IysVB4Po8Lkd6hk2PUYa4RnXHiq1NTqxonOeoz6SvsEMQUptXupMG00lW2N6qUe1L7VFvgkPZkqOkmDjTWEQHgLQS5vuqnYhY1ionKUeupsOA/SvpHm5ZXcXaKD1bp1bL4ww9TKY1uvy8MMnFIicWFYXXp0bySFShW21SRBYoEz8ABFMwfbi4rSpyy/XBdDFYqxW380aj6NpjRXsmGKiXQrkmRo6EsNp3cFL7TUGhfuCE0TUQFWPJM4EwBFXEi1bgzWZkUU/fSG4Fr7atgm1VDk8BZU0kwkMifXytemCPcWGon/pDrKROVT+yXAdO5ysxrLtH5fCoGlUmRxSrxmOY+//qoMVqjnYeNUid9n6rX8GpGcc+xm2hMZS3lo616UccAbrFruMr+UZoLNnpa4bWfbAoE45mbtBA2G29QH62m38usbaI1B6xlmfH+H0PaMZXI2MLAPiVGv1KjvqsOI7GEuTpRyl6BpBKt4whVc5ho1QCIEVZySY57ELMyq2uwWYu4npgZ4jxVg7FbOaxYsNxLpubCqOrGke4aQnjRSuR8Ggl7So4eY8o6uOHEvH2w4h+JbdNhvfdqMdg0MFXR7AW2D3Yfu3Xfg2XXHLJ7XaAfvM3fxPPfvaz71wHiMSQvpo98YlPxJve9CacfPLJuznNEbfVZopR4zlPZKXq2PE5tkmg+8B0nJA3DRXToskp2u4b7M9Js7eOV7Cxlb53+wew22lAGexPL15zAFj5cs5T+XKH+kB6qVhXZNIBHXFv1chXVYir2dnZm0LD02MaTI6mHAYZzLlAZJ1eaCDBMZzYnBM0zcCjyg6Os5Hn67ryGORBi/NnVPJ3jIYHqi4P2pO2Lij/pIdDMvx2alAtoGXb6QI6uCvRn7kq3X2SYwKS8xCzYxSdCl/nAbpbs/C5y0+RqPIA4Ls80Hsjzs7YoaIk4kXFPztFoVb6LMWEo+nNJB3FWkoRNIZWO6IrQ4O3bb3ksVaG83J8Y7n0iC5qKxRrSUpv9+b7ms2BnC1aaACt5aR1fCRHLO/qlZxCj6TWzDeZadlMyZelq5lxVgAU6uVu6mEn2VEgyv204zIT8EEScLPTj9oh5HIHWpcmWoOYHRvO8WlsUYC3XRMIFUhaNe2e7PSsB5hcmmEwyp91XxTkrGmMqFusVOk3TfaC93cjAKn+0VY1ZCIEySls+QHTtrf7AXaocnteIGy1A+xkRzzofC9TwofpZsu2nS3hESoFi00hMHnbw0wpwZ0eWJngHlaztMFazkdarwqnh6FxnR+jS9k4ubCQ37WdcU4D6Cps2VwiJFiu5h740yFmiNhMHMtIUEK4m5S6TnTf/VC0irhNrECGafEn36mNBOpScNcoABnqJymO0bDD6qBFH46cA2RwB3OADtuVHHmLMeL000+/3Uzz7e3tr/4jZbtWgr499pGPfATj8fir/3BpS1va0pa2tKUtTdnv/u7vHvI+ByvYusjuVAfo7majqsNaHTihuTKBE2QpujFwEqYd+xoHclHCm8eJnfCVnVVsHEjbwmYDt5n2G24aZI09SVLdChjdkqMrt0448sPU4xhTYiIAOIc4ylGftQHavXlVllf5070W071593VJDCZKP5oAQxTyOqCuy5D0StNhlKGsYSVh6klfoQsUNieWisM4JwhP2pqhPaJi27FjCnbdmULVFlgAGamoz2yCoq6BZr1itVHdSUW3DUp5OEY5Btcw6wyQWTShq9HbsvubYJiJZ7tSqTltE4jKBLkGQCIw+v8lLJQ/Sc17qmowdZHvi6IjobYQxWUrxW+dmUfTHFhE09dKvE0XH6VIjqKbF2J8WnxQHV/qv9H1C8Mnhqjq5NJOUUWIohLqFBaWFtZkOCtEifbQQZ1DdBTFUsnZVFR0UDGsFSrDif2LFLJDLey0fsWgo4KqVBB3oG6hN9yX8+NCX1UF09PQGFEFDJocJcoRotp5kWCwElGm2nGTvub3Z9rWaDMTNGSBVIxtIWLZ5HepGgOO6f25idqooNSolONF8dlOKbIWioKtcBSilMhaHGWiwvoAbRZ7nOYxpl1XdRCHiiG1SJm7iohMSAgsRsoRHm9JrxPBG8SewpakqWFE4kDXbqMuZJXswBBC6hhgASFB5AF0Mr5XkKwUa1XyEwNRnicZlLryaKoetlqAed9Zdg+mwT/jGc+4U4+/dICUrdYd1uq+oLFrjRgghbE3uwT57G9H+PetJC2+uZO2tbcOUe3POhT7Dev4VGMUUvoAUG/0qDZzjk/nU7FBAKCwvhr0w6CCz/h7t+rQ7smOz57093YP0B6Vdu/XPLCSX9xhHpSbngfqlabjEP4oh+/31FOG+wbKO5mGCjs5xr0/U1Fv2VnFODOjunGFmGm6LkN89aYpYKNZMwGStxBkcCtzGKjdIiqGhSJmValDY4XJ1gjtuofk6ARiJU3BGjnYkmrsGk4rGDWzcJtXzpDSTYpm8aQrsgQCG5le7x95/36UqcpD8uYiO0C6UnqoVIFSLmNiCqdGCtHyrRQ6Q8z202rFdP1qrDQBCLrkSf6UezDFswOSbk3VUA5SlAHGlPc720aLSqZEq/q/k9yjorwG50Npxw/aHxOjNtClWLK5iegLubFB2Ml9iuj9teTFmTqARJC7KJIOO5g331tBHIkGbyPDr2HqYLayYnF2zutt0WpKBUx1fgtBhtnBaQM7maYL4kTyKaOSt7AIw3zhg6oskZL/7kfk7FSYHpWh/r0EIQtk6FcCl5WJinnJ91iJIr51kbXRyDGM0SgJEaWjRU5LJzpagH5eOlct/61SlHe1uBKVbyP9wIJ1qPT+LM1Qg+UpjIus2UZzgjMBo6pDXx1BFtg9OAn6zrbbEBRZ2tKWtrSlLW1pS/v6tGUESNlqNcV6JasOqyAwigRt9wNOeN7qGhEs206fZmIZ/tEFGF2rChFSraWpZwZGdAawwlhJ2yThtR869FmorVsVzRKd7MyMlVFAtZKjPVmjZKXpMKyEuUWMrZVMa6pMYDbUNFR8321wrH7MIXwFe2GrgtvJK1etvMr1duYT+FKRTdUuuvgmQSa0qlXQiOkC7CSHYAg6cQZhSNBgjXZPFo1bc2hRQiIa6tHPhiScErxD37VaMN1LqTtTJPvOJAvDGJUYrbO41TUtiPBQ9CYaUya3quNKfSIK+5sCzprdR+ucaF0nTop3kBv3Wo9H4DaquZUSunOkpFXRINVWHfX/PQbVOOtkqSKTixLFdeFUZuQMjAgOqppaWr+FtvlBlIiEagO6Ljs1HOFZpJJeCuVBFa/Nfw4GMUfxYm8Qxo7386E8rgnShq4Vtp/WOap1dFEx4dI+8v5UExX1aYPUr2JoUTScTB/mRf6c4aTxFC3NY0ttF6oZz0bZ0oHpWJBiwoPICcKWojtu5twMlYITwSlaFjrLQqJm7FRys5AjtKbWXKSxl75nQkme0MWV0wWoewhgoptZAGGHSjSLXOPRNOkkxHpdG0yxUnXojigEhmUE6E6ypQOkbLMbYjJpCgiM6O20rQ+W6apb0wGrG5fYQfooKqxXYEqxvKwGVQ5P69A1U7kbWwi2iVCb5DB4+hyJ5L5b6TAaCk0XAIZVx2yVykqeEzlzk2hw8yTlMbXe8YAFAJMsWjjJeQvjnQbYlkKDLLhHYmTbEfU2he1ViJ7puqEctImFsmBiMjFKkVUL+PUhtx2QcwFIjbixBfuFGTMKioIKlbMRlOWQK3RmJlF2XCx9+sjOa7XTl6ylWe0ra6VMhDGFUwuka6bJCLVdmL8iTpFMTCmfR33P+3jVz2adoaggH12cViaKCF2Sgc/vhFouhTpNwYSzc/R9YbrpCcbXMzlJyKJ02gEiJ02pLAtMF7kcAasdu8hMo2hTrgndGBWHDZ3Qn6ldTCdijcK0m3HU6VJpWydOUewlr01PTrqNpK8rJ1E5OOX3yN8Bov/nd3micng67Tmq66TisiuVVGhvRADV15Tjo+BDhznYVjsS1ovgpMnK9NXYoM3QYLfuuEwIOaEwsSwnwuk8kfN5qA2ricDk1VhkLXjh1MXiHmdZliaUbESdoCVwr9wrQ6UN0JPTrPoZqdljxaMayeJxSAzYStS8t7oGfXfkvIp7shL0nW13qgP0sz/7s7ebj7+0pS1taUtb2tKWNmuf//zn8YEPfABd1+Hss8/GQx7ykMNy3F07QP/0T/+Ea665BjfffDPCTP2Yl73sZQCSKuPdyf5jax0uDoptVJ+HIj1tV6HLujF+6gBKAN4SGEivbGilGY2Bp6Kgq2pVWmXorAtFYi+Qoj6UsNoPjYi3DSQZkVkZKwE2a5esrkxxzEpKyVzPIm26VEeIhgt5khjZdttgJ4s2dp2Dp7pd3iCSFslU9HwGG+lamv3A4EC68GaDkry9wH1TLyUXFtUZAiSaY6S0AuveDJys6Borqzqtc8LMEFOsFCnpnFbWxd815EHlRIJKoPQCPQiMEkRgrle1lDoP0+YlvZd7NMSyqRwiJbY38spRZCsgwFIERsFiLNTWaBFLSfTWkv0U1Qk1MMsSiwaSAOxUBKVSkRTufKZMBOeVe768oNrbqERSZgJJn9QsORMkEbWIvnByt4pSKX2WQOUnmsiMRjvMelWVJKkaGzlKa0wU+CWfys5sI30djRhxAMdbjhxRZCwGYSVB3Qu8JNvT8U0XhSmnWXUqEZ0iQOm79GWAojM5guMMbBahTPAjNaKK5LBwpSkIAXTMIiKoolW65hqQIpxSbiTAEvmAmXpgiM03kjBN41I/1OeX+4U6VwF9k3jhRKDvQpCzknddJ7uTCWwFSZhWETnLURqDmI8booJ1VQRVSACqn0TD43+bx8tpV8EYwE+OYPrsPRwC+8hHPoInPOEJ2NlJc1pVVfi93/s9/MAP/MAdPvauHKDf/u3fxnOf+1wcd9xxOOmkkwqRImMMO0B3N9u/NQL6URLEQx4kaQCkIpetLcK41bbkvQCkSpq/t2XVaYYTdJE+9cKLQJcMaAQHJPZP+q4LM1JuhqY6W6uE2mZE2oCkOEvQF6vPdo04eeMa2JECjPUO4fNp/2pHQtaamcJMJzVQIkpxzUAia5WVGmUFfdnM4fca/tF5IkyBrSX/JeVu5DbwmqWlJgOdHzJD8XbTKE7T2HMdNjvO4e+2TxXSgSRV0CshuVkIzBgWlUsNRQ9MnjdPELoQLU+SItzXrSyuzSawlhK21Lp5Gqahr6ouWcz5HBh4sNpkNDz5m6nMmNTnYdTxjEzaNJIUeUsBMASXqevRFd61wzqnpm0gDqFBql8FcY6j9Yl5hCTO2RBl2YZC3RsoqemATGgE63a944kvuEhIqRS8DYZrt9newBBjrBW5BA3jcBFf5eTxp4ZvvHJECGrtxBFwnXIO+vmZzFaGF0zF5M8LL/mtzkWzve4f+lqUg09+D7V7UxZM5jyh/DvXynVXSv4tSUbk+6GxootFoVpNb6fr1hDwrAUHcTKtHF8LcbLz28lYYDuREKBxrR9JPTM/atCvZOX60YDZtDargLs6pILEOwsu6s6ye7gDdOmll+Kcc87BG97wBoxGI1xyySX47//9v3/tHKCf//mfx2WXXYYXv/jFd/gClra0pS1taUtb2tIW2T/+4z/iIx/5CE455RQAwGtf+1r89m//Nvbt24ejjz76Dh17Vw7Qvn378L3f+7136MR3Rev/YwTXjDiKUHdlwieQ2UMqnCsieQK36GrEDK/E+ZVMSuDNsFIf55ISEWMZ2uXtmhWRV62dQdhI2MH+qcPGRgoTfL6RVbHW4CAjVkY7rrkMhJlYuMzecdMU8aHv1AY6iViYI/mYQ0nqNQPHK0xdzV6SEsuwfZjpkbqMhPVRrZbzNXURkSAIFYkMzqSK6ygjDpKcKqJxpMuUSlJkeGWng8mMM0MClW0rkZ4ZOC/ORICMc+q3EcYStEcZyPJAE5Q0A+3pZGUt9liD2TtewUNcbV5FAmUJHAGCb5z0M0oatgNfVMegoCR6gX10zS5dguCrMar0PWpIhEzrtzC0RvfXAj0nWRv4CUHPlODr0OYVuh96ALkmXQ2m9lFfb71DyO9S7y06ivxk8cE4dRzVMV40aBxHBw33eddK0rdmbFH00KoE2eiURtOC6Nwi8UJd9wxBhCURy8RgOj4xpzRxSyfKM7PQSQQn1EbVQSPIXSKNmsDBZAIV/dN6UHzKOPN3htYkgmhW6e8SHdf6X0yOUFCrbi/+u9IBAgCfxxbjSkg77aNqhXlhbwqJQKJZ/dCgzxXtuzWHbi3DfKR/NIqILiKMjxwEdk9Pgt6/fz9OOOEE/v/q6ipWVlawf//+r40D9L3f+734wAc+gAsvvPAOnfyuZitfsrCj0kuZE8ObfbFn6ZleQru2Lws0ajE7IL14XqkYz3ZU7UxVNhaFG/U1AIDtrMoDsZxb1GcnoDNQEJxQgmkyGyo6bil2NwPZARmaSF99bRiGYxpzbwrHTxcdTPdqinwPFuYbqkFX5w8oKrNWZaZPDfWI+JmEvSt6Hsohta1Q2rkO104Pu9Plc3WiyM1qgAao8wU6C+bTWjNfqybGMl+DBBwzhGZVPgZGlRqMs0PcmWIyYKejNyrHRgZtzpUZBMRM4zU1nUBwGGMDkJ1ek/NnohfoMXrLary2NXBZnI8n+U2BEDTLSzPSOOdEOz0AdNFOui+mhhf0bemblEYFo+ARDcNkQcEYPAIL63n0+R4J1p1Oa3hSWp5agGA+dq5L+v+sennBeOvK/CbqsyRKGq0p22MWAlPnrcaGhQ7phyZa6TpdKFTRua8zXCbqzqbz7PiEYYa4Vxt0WRqiXbdo1/I7OlRsO/VJUhqxjlLLiyQB2lLiY7aNtBXPVsF8VrWxdn6z4LfAiF1g1XRd/LlQPOc8JzOTz1M67ajAiyTbSrFdq8QkOfcIOqdKmG66Blo/MuyEHxG7BytBk33605/GTTfdxP+PMeIzn/kMNjc3eds3fdM3HfJxb7cD9Ou//uv8/YEPfCAuvfRS/PVf/zUe9rCHoaZJIdvznve8Q76QpS1taUtb2tKWNmP38BwgAHjsYx+LOKNx9V3f9V0pEhgjjDHws3mYt8NutwN0xRVXFP9fW1vDtddei2uvvbbYboy52zpAa/8eYIczCaWLEvF4m+EowqKEU/1/o5IhaZ+oQj5mYSdXqxKbEi6BtGpstkrYqR8IOyg0IiBH4Vw/jCIkp2T9ieVgPIqokOHVNmAo4ViHpOl7UA1SJOJKCJ4hAMVUCgTZqIjBonCZ6QFMKXQmkCOtLqtJlJVmKMPfEl2gv0de6VkPGNJVUbpMVI4kADCUuNxkpp4qKwDnJBm3ssBMBMj0gaM9pu2BPq/Se2KOGZh8Ln1dtDrVfaMaS3St3hZGWE/MsAHg83PuVx2X1eDVfCWr+ahCMoE1qKxEZ1oLS5DKDqR+Xf5sNkLJjpuFN53AmKEyBYzHOkCKvRMpyd8pMUf1KccSdhjf1zDAZIjXVoEhru2dAXxOXqaaXpq8YHQJD05sVrDWFAXrDcjdkbqhk+iJhoqKaNd8QIvHENtLRE2zsZjh6MHkCxOM1L/rQvkS5nOGQa4ltj5Av5KJDFS/a82iS/Je6Fek9lk/ilzygT4x8CIC6KTOG7VhMFChWTOXtK4jPRrSNF7aQ0d2GYrFYpiGGV/GYFagUVd1T+SG3P8WQcAAJ0zbXjHdxvLOMvyoyokYH2FzNLTKYq/NwCDUFr3WZFranWo33HDDnXbs2+0A3ZkXcVexaiegUmFkoGQlAIAW2opOMHmZhBW2rSZZ/YLzxOMMh7LThK3yOPj8emJVDlFVDghVLTR5EkwEFOzkDdOf9X0JKwkMx5HzlG4izofwFzlCyoIDwDkpsbgf3p8G+E7lmShoTpR0S1VpZqKxOm6UXASjJk8roWy+1gCeBEMEkJkf4tyWkUyp5SX5CYaZbnGhY8Wf1gBU2211IJMgM+IkTyo4I4U+F4Tyi3sxki9BasKuBQI5LWNd5FGE6opaYTMKwBpacTsiZFiNAUeTBSsQK2ZjEJgRuZ9Hp8L1RsEJlXLQWb0ZCGqb5JzQbAnpG2q+YUba1HKRzX7qhKXVWlRaURipP5WOsmxPx1TwTFA5V/ROKFHG5DzQdSvnkp36yENIjIAhuI0WMNtWaPBWJnI6fnIk1BiUv/phJc9U5/IMBJ4hmLxbT/t0q2AHyK8GxNXsiDcelq77IBECTiPKHS0Ew2xYqJwogq0KOBryfdHisYA0rUg6UO7VVMtfKBHNhUV7DUr4fuZ+9PnL8ZTMFWM+CU4aHzmPkCHsYGC7UIpS3sl2T88BOu200+60Y+8KyHzVq17FnHxt4/EYr3rVq+7wRS1taUtb2tKWtjQIBHZH/t2N7V73uhee/vSn43d+53cOeyBmV0nQr3zlK3HhhRdiZWWl2L6zs4NXvvKVd1sdIOsBFyJIR7+IymTTpRmik2THMiqiIgZeVhCzOigpWVISJ+NMFKCIRM2souYjU5iPtAAMbZiIEqKi8AGFwV1MCbIAbFXeM636SBAx9ibBJvm7nYFerQevzI23wp6hBMpeVuZQCb7Wl8nN6bcRi5KcyXxtABLhU+KBUWn+FJCMqoSuyyykk6GARub0XRT7Ta80i2rw6hnoKvMEbcmnrDSL9iOYpRYRzOhKgcfZKt7FOVUbcn9r1YseVXSN21qYSLaP6h4l+ZQT+Gtpw6JemVqBS4RHC9iVK/7Z69Y4iEClAdDRKl2jjNpiLMnMXL/rIAm6WkerOC/KZxhURfCgYCJOLnczUU1V5R1I7w/DR50jcppAcEr/KAy4OovAvl4S3INziLbi66cIBkV7QyURXz9U0R6CwFcDsJrOUK92GOaaVtYGThDvc+iXKtQDgDE6iiU6Q3QPrlXaZwqWZlFINR5FJcao649R/4tGwYCzZVqQ+pkWkQQWRIDUODcX8VBOQHTg0iBl2oLcOydR+8D3UJSEORj7cWl3il144YW49tpr8dM//dOYTCY49dRT8ZjHPAbnnHMOzjnnHNz73vfe9bF35QBR0tGs/cM//MPduvSF7UIq7MeMryihT+3g5LcpBiMhac1QIcqzWRx+LBwgdXyjoBb9CaR5iOGR2vJLyvkgSim6H4kaL0/4w5gmFCA5PfSdily6yDT5pP5McXdhBbH4W7eYMQM1GZZqwulrkb+joEM9uHC4n+EGcTKLplQDIrNYBpBijSrPKTbCirKVn7tf9mGjEap0b4U6nnMg0M+oJJNEgXKG9CRMbCm3A5Cgn6Y/i3MhE6qv5bl6NdgHBefReedkE5CZUzNFR61ywGwvtOrZ/paOaRDzNfQDi36FvgsEwbkbamIRpyoq5xWSy6SLuOrCrCvp734YWJjRNOSIe9hKZhphfBGMaVSfk+9RKTWzejPAsGuC5qTfpwsP/J1hD6h+HIz4PAbSd/QzIAcuKHZaJ32GnKreAoac74mZm/RTHpViqSlYlPqHbkOCEf0A6NZy/6fCyCseVa4LOBx0XLi0946lAFgVO0jbxmAR6b3PjKd6x8DlvJnmgOSFCetVrlUrVBckJkWpp/t2QdhdYaY/pWaNhXQHHZNzr4z6vYcav+nHJbypFy6pLQ0R3RAqxws1NzUsQumo7l8XEnzdH0EP6A5CYHf3CNCll16KSy+9FF3X4eMf/ziuueYaXHvttXjOc56D6XSK+9///njMYx6DN7zhDYd87ENygI4++mgYkyi/p59+euEEee+xtbX1dUeNX9rSlra0pS3ta2Z3FMa6mztAZHVd49GPfjQe/ehHA0h6hK997Wvxute9Dm9605vufAfoyiuvRIwRz3rWs/DKV74Se/fu5b81TYP73ve+eOQjH3nIF3FXMTfuYZparfJ12EdBAfRdl6fgKswiqhXqUgStiPxk0xLuvEpXOkK8cumjQB9dgDGkF5P/3pQrJEnQhfwu7xN9lNWdhjCCQFS0ck7f1W9QRmp0pWaG2ByK4uJWJSnTcXQIeVEkQzOFSPAv1rJyFwaLrNxhYrGKrzNDaJArOteVlENwpqwdNWsxGvh8E60XqID0ZXxvmWkUOwfk2kCWPlsjzCrFSKN79QOJ2BV9I/ctoyBT44FKlRnh/kEMRB2JiZiLWppetKnc1HPER6rFW2aOdasO7Xq6hm7NqITq3DCmPJeUHsm/a00Bg3AJD5WM26/lH6x6uEF+Rk3Pz4PMmAiXozFN3aPJ5V0W1fzywaLLOkC9t2hJ6JDq9nVOrttGVMQeI9jKBtYR6nuLfkr0StU5HUWmAkemjARxpRaYjXD5+Gg8n4Oii6GzCRpDqinlMsOIkqFTza70vRor8U8P0E1I+ZjZ5GyCc/OnN+jHubTDpOLjxs5yX9XRMmKa6nIerH/UqxI4O5EFRK0u0cFlZww6zpCQkj48RmiIzAJd7idFpF1F30R3LG+q5VjaQdDsM30cia5BMVzztXpdukTgYBOiJET3EpKy1gJ9h6UdWZtMJvjYxz6Ga665Btdccw3+9m//Fve9733x1Kc+FWefffaujnlIDtAznvEMAMD97nc/nHXWWXP6P18XZgx8I0wLfnFIZtWIAxOcpmJKaFrnPTCMoetQKefEdRRuVSFhoqP7yL+1nThDiFHO1wg0wbegJyYVtxe2lEAEC0XpFGNmUfhVC9xpqnIhgKexczXQ0blmmUj5Etlh5IKYwyDh/EGAzfWeKp6ABD5K95snehcwbNIgNarTZ62SbayJRYFYskB0WeUg0bbtruEaUuO2RiYapYmX7ndBzlWvipXSD8sCjBBnieEjU9aUI9HGcUS9ndtgm2j2nvPOojGITaYt0yTpLA/2/UqlWEd5wh8ZdFRod6hyoxr1bBflOHmIRELeFg04hyedI3+OAkJWbXajdN3NsMfqKN3kejPlwr1rdS7gq7zkgethc6et8nMc+5pr2u2fjriw72Y7wJjUogmKUlBYjAbtTnlDsTcM9djOwFHeG7//ETGPC6EJiFlZz6rcOe1IszNkIkNMPi86YuvgttMzcjuGlda14jrX8rLC6tMCi9Q09Y5ydL1Bz3UM8/VPlRhrlDHA9GbewVF1slJNQ/Ls8rYOvAhz0ygLNlKtVuNEqAxLDPQrllltUtQXxeKQ3w/lqCySEtBFf2XciWW7LBD+K/IIZ941NxFKfL0FmKAcQ2JHMgssn2v+FHee3cMjQC9/+cvx4Q9/GH/7t3+L+9///jj77LPxUz/1Uzj77LNx0kkn3aFj7yoH6H73ux9uvPHGg/79Pve5z64vaGlLW9rSlra0pSW7p9PgX/3qV+M+97kPrrjiCnzv934vjj322MN27F05QPe9730XJkGT7UaR8a5geoXF23gVLVEfCfOWldvTpyrtMJDVsE6I5kgLhV31+QEWCQy1Yv9EUySaznr1GkIzaqXHiadtuTLXDCUgaxbR6nBRxAcoIjlFSNnLdzp2wcDS8Azy//WxOOoTJfKTk7TjIMBmmKQaeFRV2bd0cmSIhqM1JhhM2jJC2VtX/JZEIPVqvc6r+abq+VjEkhl3FcbTdIF96xCotEJrYToFXyDBEdQ3TMR8ojiA2WiK/rR9qoUFZEYaJVd3gM26MtU0CzSGWEJsnIgqkUiOGGpGzgKRwVCrSJ5KeJZPjVGo31LCea1Ga93pm8CwUD1MHW11NMVxKymT9rjhNo5rtgAAJzRJ3n7FtvD5Zg70K9ifMZVpvvCtboiJT98PtENsTVO4aWt7gH4nP3uVyMuwbgdJVlfwkpQbUREYjlKIjpEfOviVdF7fBCkZQX2xl+dlVPmWRpEA+L3r5Fw6GsvRjYEponDctMi/hfStajtFMOQvGVKld009jhThoWsQeFVHMKUGmVyfZp9SQjZD512E7YP8lnWZIioVbQRIuFXGTDoHRx9nmHqzMDwiGA4Pg8gJ9LFSzC3KbPYSjTKtwHwFaUNHbtV5efwnUVSfhHJnk7KXdufZ+9//flxzzTV4y1veguc///k4/fTT8e3f/u04++yzcfbZZ+P444/f9bF35QB98pOfLP7fdR0++clP4vLLL8dll12264v5WpubetjomYqQ2FYlPj9XvJPYGKqezsFqIUXtACA5JBSZD1a/fDSJlxMiqwR7w3VyKIfITUWwMFojVGJSTq01XIdiUORzKQetyNHRYWmUTotKOREGhwGzPYzG+ulcMw5U0V4qnA8AZuwQM4zRjSt0zF6T6+MLV+wfAMxo2Xbq74qpwz58/rtmHVklcsnMsKk8sOhNwTDi3CSnR1D6o5kfYL0p25uwSko9WRFGWxHi9waGQ/fkYJniec0WzNTbEkRQ/j0WjsxiaEHneZETFKsogpdKOiGq+6LfGiv5WVXOpXEmspM58TVuaVOS0EavlTiTbXVDbPXJS9uYpr+3vcOkSy/gZFqjy7ku2HJwOb+l3jS5rWTCd1Od1zI/+dteq4tTvpQp4Bv6Hhon/Vq9q4tUkLne2YyPKEWC07ZQqe9OUb/VsyEr64PJNXCh3wm41li1E7nIqm2FDUhOi28sQqNyGRlez/1fwfhGOQBUJ6veDnAk+qhyFrUzRc+AtnMjDGij/J0XbIvygma+S4Orbawmb2CzXEI1xhzkWI0jF7KttwOqDDG7qYdpZzU+Unvbu+ki/+5oj3/84/H4xz8eALC5uYm/+qu/wrXXXotf+ZVfwdOe9jQ88IEPxDnnnIPf+I3fOORj78oBevjDHz637cwzz8Qpp5yC17zmNfie7/me3Rx2aUtb2tKWtrSlabuH5wBpW19fxxOe8AQ87nGPwyc+8Qm85z3vwVVXXYXXv/71R84BOpidfvrp+Nu//dvDecgjat16Az9qODnUD3VyaF4FzSaJzmibRCe1xLQV4WUVfl9UbZpWbzoR0bWRV3L1toed5iRMDlOrFbiT2jnCTrNyX4MyigWk1VYRlVEJy3zdLLAnkQFdw4nboFXwSlMeC8j/pxC3ilxYGIDE0RZI1scqIlaZeUVRCAte/RlvylXlDOQII20frYpu0LXYigqlJ12Q2VWlimbp5OvowmwVDegyDoggbU2urG0MJHMYpeAekGDQOMzJtTYKK64VKCcMBO7T2icSEcj9TEUhNGuPYKsUcSjPn/6gInELBlHTG46yoZMfGP1FtwsxtnIjT7oKPiZY68BkxD8jaNIiIhCUEw0mba5qnj996xBaqfVlKYl5KgnkJNZXbwP19oJISAH/5O99TCJ4+tKdlQjqwApRwsh+RTSJXkur4EeCJBuBtkOtkBgNCyuSAfVP35RRWLlu5HuBvJcEKWmByDqiZhZXj2qrpQYHALjaIYxyFfmR47pioikmDNfEVJNrAAATLeKEIqgAs06VgKOwCg1DaLFCyegCWHBQt4u2BBcKBBfova5UiRoqPbJjUOei4c0mMNjIMPf+9AI2Gx3cVuowZtwCnSpyNkgdOAzThYeVGmHk4PsjFwG6p+cAAUAIAX/3d3+HD3/4w7jmmmvwsY99DNvb27j3ve+NJz/5yTjnnHN2ddxdOUAbGxvF/2OMuPHGG/GKV7wC3/AN37CrC7kr2Na9a8S9NcNZCYZI35mKXc3AHQoaYFtQ1BO9ESVk7eDQgDQGh2Er+pwEuAkJcfWwORxrOl84Dnx+EmCsLAs0Qokq0gA9O8ACqVYTOTC+QZG7xLfFNc4EconqGAVconIF5mjyZmZQo+9Rvs+KlQGJWm6oiCRNvBEKrlB5HCoPSZ9HrsXMQXswZuF1CfNECspGF5WYnkm5B+qCTQA7IKY3c84vgrSHr1UOg1IbdjXdGFiczxtJjmBWkna2gplTTA5RroWuXd9f0Y+jmjA7Eb6TfJGyvcWZyveiFbYHETH39Tix8IMMtVTpxxOn6OQuwjnl8CE5PeRg+V6o49hJn3ZqUdM7pa8rqn6gcqtYPsDHOfXygrkICH1SQy7ECJoEWCqeOVMfjo9HNdAaedk03Mys0ijQGNf/Us6x8dL2la4+pC6vyNmaYVGWZoXuHao5uY9YiWPnh7YQWU3blJilyjVj2QNrUOcxqNoJhfggnYP7kVr8pSFKOVZA8X5gkbPVy9hp1P5QbFdy9uqtiMFGzj870KHenwYRsz97ReOJNNFoiLhnNV3X2hB+NTuE2RnsVlK7+PbrwKu4m9gTnvAEfOxjH8Pm5iZOOeUUfPu3fzuuuOIKnHPOObj//e9/h469KwfoqKOOmkuCjjHi1FNPxTve8Y47dEFLW9rSlra0pS1N2T3Y39q7dy9e85rX4JxzzlkYYPnMZz6DJz7xifjc5z53yMfelQP04Q9/uPi/tRbHH388HvjAB6KqDiuqdkRt614GZl3giDCIwkaYWTWn74sTXiW+DWYfuG3LYVhiajSbEc0WRX08qnGGtaYU6Qkp2gPAhAAEWv4YSA2xvEyyFoGqjxvw35nB4SOQw/4m2rmk3KQ3IiHpPouYpWhQLI5lgimiAItezqIq9AxKp2sGFfseJNQrTDh1P6p+mFEsGr6vEIvyEXRfi6AgvlYNDzkUmiPpUyWXO/meksIl3E/HWqQVwn93iuk2DLDEkBqQvkxAQ2U7TESdv5PAHwBMMxQUgklCfwBib1XFbgoxzZRxCOWFGS/PU0N3tpVyB9R3BxsigGe86FH5YTpXu2rgM2Tia6OicIbbiEqXhCaVaEnbAnpKcKfLCyq5uzfMfrM5+TvVU8u7zCbtq34NpOdJEQ3dx5iB1UYRJY2RMaQySkLvmpAPTK8qg+v96fi1YxiaE4xrK6UfKlOSE5AgZK5X1ZfR2LnI7KyeFH1VzEyKaKfIb05YHkoVeR250vpi/UC+Ayh1zWZ1egBgIO+18QY2R7JtG+YrwzthY/nWMOOxJ+aYSi8oosCU5D3VEfOAaidHyqcBtpOyFakNA5ATvU0IEvE6aj19Hn8U/Eo6WT9y3Jf9wDJrrcslYbpVwI8AP13wct9Zdg/PAXr7299+m39v2xaf//znd3XsXXkru1VdvKtbvxZhRqr4ZjAibsYzo/zeBIvZujOmN0UtJGaeTJTjsxX4s9pKI3C11cK0Wdguv8DoeqAnbqwF8gSCukLMzk4c5nyIYcUDrM6f0UKOWol6Fh6aE0KkicEBnkLsNCAhKuE+I4waVq1WE0sH+a1yVKQ2laLv9yqnCXTMOANLmfK+LBbmC6V7JsgvD7S6IOdMgdG5fS1gydlhleMS7tMUcZFLUNs4dyMiEIRKjrSV6zNVhM2TP9H868pjmBWsV+oWzgpTp8te+QbSzDbtKgRSd87Xpq0QxtT9U9O+FWRk/fx3ykXTz8dE/RyJOSPQSVB5ZUWdtyFNKkCfC3n2Iyf9dwZS5WshgVB1rQU9WjsFM3lp/QhSxFj1GYKYTQCLJZoQ5vqhiRHopKNop8FQXShyfFRtNRsCYn5ehl6kAJRFxOR+gNJR941h9DI0Sgm5kd/qfqyZbun+gGqS4acWqMbZUWgj17fSteCoBlkYWC54LH8zUnjYgdvba/Ya5TlVhheSrjUHldig6yanmsbIlNtHz0OKSlM/rHZ6uO008NjtKTBR9LK8CI/D1FhxUCOspgv3I8cwnxaxJSfP1yXbj/KfuN7gKNdVnNzNvYqlAbgDSdD/8i//giuvvBKf+cxnYIzBgx70IDz/+c/HAx7wgMN5fUfUYp2oujRQ6qRZ0ZYQrN5N5imVzZYkKyf1ZkmsZNopU1E9DK1SOs+rFB5IrQWa9OLGyiGuZA2a9Qbdenp00z2UqCg6JdFC5evk69bFBcOsvH6Za6B1N8IUqJTKNYBc6FB+W+TwIE1QLCffK0l9uv9OD8qRJyHbzq/e9GRiYpQCixz5ksRMWKMKMMrKmpyTfiiJrF4n+ypniqJGOpeFIxoNxJFRk6zOhdF5KGxWoipkMUKKbkZIEUoqgeACOz3GRAxdemDBGYS8Sh5mheugcmVCJ3VIOJIzsZwMXO0otd/8aXtJHNXq5n4AZOY554G0e0zh9M9G8opkYi9OrwmysHDswKjcKC8aMRQVKvLtZqQTAHGIgJkcIJXDqk1LQ9Dzp2MU+kaLzMiiocgviwA40pg/q5n+ORMBCo1VRU3nI0ChUs9AKR6HWu6BLQCOo1glgSJ9imKza8XZiQbsCCwqcROtPEdSq4878oOglJwLFWZyigYSTbJe+gzT4TvJyXJTGQMqKjo6VWOj9+JkBok+ghKRYwRyVYJYV4hNjoxSVGe1Rr8iUR0/o57vVWRXK1SHGlJolpO3Y5FrdSRsmQR959kCvtJXt7/4i7/Agx/8YHziE5/AN33TN+GhD30o/uZv/gYPechDcPXVVx/ua1za0pa2tKUt7Z5p8TD824VdddVVuN/97ofhcIgzzjgDf/VXf3Wbv7/22mtxxhlnYDgc4v73vz9+67d+a+43+/fvx0/+5E/i5JNPxnA4xIMe9CC8//3v390FHgbbVQToJS95CS666CL80i/90tz2F7/4xfjO7/zOw3JxR9rs2MJCKRIGgQsKBdW8cq63gcGBtCIZ7MuUyn2TFJIF5kW0KE8kh5lj7RjK8msDFd2g1aFFIAXVoUWX4YJ2zaBLRAX4nKvTDyEFQh0WQ3O8GpeQdFEPaMGLUu0oFspUVvZl1CT/WEVBHIv1BdlPCa+ZBTkUppecJ3gVnteUZIK7VBI+tRuMQXQZGqxFrbfbk6mstUFLkMuKRLQ4clbpfB+BrUiQEIMAk5lZMRpRsRw7uCy0xgrAWgxPXT5HmIYRIYcRgpPIFhXv3EaDqcuKx2bADCmrxAO7Pv12MqnhqXjnZoV6Ox23yWRNHRmwrURlWLV6RYqeFrXdtCiiyjPRTBxdKBNAZmAZ3kbwoYZYyQqGoYYhKTBRRcSB/IEL+CoGIL2f1Y4U6kyKxPPHlecRpf8rGjzfoxX1Zepv0RjFYjQlPEdwmM5DonfZGY5A+gWQS1BtzDlrOudF9SPbS9ssVDHWEgh0SSoPK9SAy22YFKjp3vOPVcQafVSSEfmCBhIp6VdUbpFWu9e5hRT18YBlSC5HOCfyvIwXVQyK9NhJJykBfRBePzeMAUgSwxmO+sSmgh/kWngDio5b9COBvXQNMvoMBYxHFyPRRN4UEuTvJ+ohfR3aO9/5TrzgBS/AVVddhUc96lF4wxvegPPOOw+f/vSnF5a6uuGGG/CEJzwBz372s/G2t70NH/vYx/ATP/ETOP744/GUpzwFQMrV+c7v/E6ccMIJ+KM/+iPc+973xhe/+EWsr6/f5rUcffTRt1l5ou8PEvK9HbYrB+gzn/kM3vWud81tf9aznoUrr7zydh/nIx/5CF7zmtfg+uuvx4033og//dM/xZOe9CT+e4wRr3zlK/HGN74R+/btwyMe8Qj85m/+Jh7ykIfwb6bTKV70ohfh7W9/O8bjMR772Mfiqquuwr3vfe9Dvi83yR09D7A6yZIGVDeF0uOJrBpa7Uj+DmPqbQu0+QAhgGjqpk7NbkZDhNU0+4ZBhW4t65zsTa9bu2bR5r7RrQPdWjquX/PAKOdc5KRZV3tUuVp25QLnLlGF62lXoaXyANuVKKNu5wHJlNAIQVSDAwGDW3Mx0a8kHMVs7pT3NWtWBRZ1uWy9TZv+Ox1PH4O+G8P4PjtC1gImb6stwijdox9V6NZzO/KnkYrkKwrX52rzMuFGF2EURTudPgpU1VnYXNCy3rQMgRrdX9RtzdKTTRAoK0wMD9Y+5wX5KmKiVaVp8vZGKO0EcfUGFUEfE1Pkf6RziZPa7y11rIA86BMEpKC9wvQk28v7wZMIjUERwEjtp66b4DKrfstOUFTbaXIJAEjnp5dcM5o43Vj6bDWO7GjbNpYTMZ1CyTAsMilFY/kdFgV4WzgmuiDybVl0upSF5CDpYzFsRZXeVddfKBeBGaiVkNSZ46aNCnpWfVKXsuC2qowUtVVOoHbm6H79AOjXcxvRO1OXYwHlEJnWsONDsgphRyAyGMDkTsfPyxjYcZY7aPv5ccYYhhlj7bgAsB84BTXKQlIWl6UzP2dmxjGfLVHTZWdpOrfnnWZfCwjs8ssvx4/+6I/ix37sxwAAV155Jf7iL/4Cr3/96/GLv/iLc7//rd/6LdznPvfh+f9BD3oQ/u7v/g6/+qu/yg7Q7/zO7+DWW2/Fddddx4XUTzvttK96LYfiUxyq7coBOv744/GpT31qjpL2qU99CieccMLtPs729jYe/vCH45nPfCY3krZf+ZVfweWXX463vOUtOP300/HzP//z+M7v/E78v//3/9hrfMELXoD3vve9eMc73oFjjz0WF198Mb7ru74L119/PZw7gkDt0pa2tKUtbWmH2+4AjMX7H4K1bYvrr78eL3nJS4rt5557Lq677rqF+3z84x/HueeeW2x73OMehze/+c3oug51XeM973kPHvnIR+Inf/In8Wd/9mc4/vjj8YM/+IN48YtffJtz9TOe8YxDuv63v/3tOP/887G6uvpVf7srB+jZz342fvzHfxyf+9zncNZZZ8EYg49+9KP45V/+ZVx88cW3+zjnnXcezjvvvIV/izHiyiuvxEtf+lIurfF7v/d7OPHEE/GHf/iHeM5znoMDBw7gzW9+M37/938f3/Ed3wEAeNvb3oZTTz0Vf/mXf4nHPe5xh3Rfg/0pwKATCLmODgmfTQOzJ2wb0uoEkHDtpBNGQtsiUqTEeyA/ZJPZO6g9R0P6lQrTo9PjGB+Ttk2PAdqjckRgT49qNR1rddhhJVdWrBQ0wkyhYOFz5KfNzJNpJ3QR2xpUO+m70JuBZkPgvGZ/Pv4tm4i37gMAxO209I4xgFksMSBmuCpS8qtzEuVqGpgmR56y1w+rGG3GSjQHkOiQ/uSVswUIPqRPFfLuVyr4HOruVi1TVynS0w+BnqDDYZRk26y4jDoAVLsKBjHTrrGVV7JTiyrTXzUUaqdAlR+51GKKRaJsmE0kV8mWiaGnogPADHOrjETOQUmasu9Usq8StuRE7mFkdg7V74ouyn1XEYaSr23kpGxWfO4tkCNXvrUcDZKVsn6WEuIpRCoVQ7Com0qrbFUElg81U0CUD09M/4EokpuBmRM6TFCRRELm4B8/XxsLkCTvGJNYKJAhk1qiIrPFgrWcQ1BsKc2I01DVLNHUeInC6cTkRRBXuoZ8rgpz/SjtSDBeeY2LhEL1MYMWIsz7c//0YDkCjq7Uqf+wFcUBZyyWBA1+DkRRN/KOB1SFtID+HV0fX4OSFWBRx6Z8P+R56BtW16Kep4YX6e8wgD+CEaDDZbMCxoPBAIPBYO53t9xyC7z3OPHEE4vtJ554Im666aaFx77pppsW/r7ve9xyyy04+eST8bnPfQ4f+tCH8LSnPQ3vf//78dnPfhY/+ZM/ib7v8bKXvewO3p3Yc57zHDziEY+4XSKJu3KALr30Uqyvr+O1r30tLrnkEgDAve51L7ziFa/A8573vN0ccs5uuOEG3HTTTYVXORgMcPbZZ+O6667Dc57zHFx//fXouq74zSmnnIKHPvShuO666w7qAE2nU0yn0oOpY6zcFDAMPau8ummQkhOaoq41JWZeXIQgME1Vwawr6npDqqKZzbVWo80srulRFtM9Obdjb9qlW1WTFYCQQ8rTaYU+lxOgCcr3lmm8oXesAWNyUUg3thhmJlCzCQz2Z4hrPzk9HepbEgfV7N9C3E6YTux7IDsw9vhj84NoSojqtswaqaRM+TnOFA4OD0SK+soDuRP9GJiSpgskmrDQVnWJD4F4mDpca8hF8ruiiD0x1OOmhh0cdnQ6yECpJjFdsoTZbdNYTGw6BJ/uaxbeIEeBcqM0Y6ZUG2amTmYXdSOLdj33nXUDkgrSzh6z1ypxdiTnRQqVwkRmp7k6iDpzvhkfLGsO+YlDnGSnnqptK0elLE2izkeTkWIrpnvLnx1tmGkjzg3K+xuZ2FL5CeUwsZRF3tULdFdNo7AUWVrrqy+TJZfGLITTCDYrHCytOs06QbofqPISGqYhp8apor0LLjE65Ugr2ErntzFlXsM/6n40JCSq0rHoq/PnVQ6+gr4iF0M1sHnsqbYMQ8REc6+3lQbaOLAGGjG/bB/FIXNmDkWPVvSV/MDJwmfFMmMxaGkK5Vzyc2LnV/qO9ShyhIqitsiMtQ5HVgn6MEWATj311GLzy1/+crziFa846G6LxI5vKxdn0e/19hACTjjhBLzxjW+Ecw5nnHEG/v3f/x2vec1rDqsDFGc7y23YrhygyWSC5zznObjooouwubmJG264AR/84Afxjd/4jbfZQIdi5Gku8ipJ9Oimm25C0zQ4+uij535zME8VAH7xF38Rr3zlKw/LdS5taUtb2tKWdmfZ4coB+uIXv4g9e/bw9kXRHwA47rjj4Jybm0NvvvnmufmY7KSTTlr4+6qqcOyxaeF88skno67rAu560IMehJtuuglt26JpFtZvuVNtVw7QBRdcgO/5nu/BhRdeCO89zj33XNR1jVtuuQWXX345nvvc5x62CzxUL/T2/OaSSy7BC1/4Qv7/xsYGTj31VKzcPMWg7WGmaQlqpr0UxtOZ5hSRqCogQz2Biuat1Oj2pAfZ7nHM3PIDxT5QLIOgVrWclKrC36x10lWgxxUggSdJzjbC9FGQDNcaU3CemwYRRBv38knRl6PWEI87Kl33ngEnZzOToilFBFnPg2ottZG1jkyIEs3RRSFJMM3NRHsOkphI7aZ1erhdF9QnKlZvKlJjVXSB9XJ0lEAp1s7VMNNBL18efzZUHp0pilxS1EIrFxdRAm47ij562GkJr6ZrsCJ+mQtXRluhHymRvZngnAmAJeXa1mA2CTRatZp3ET6v6EMb0VFytIqcERvLTiz3T44AhXLVrCMxOho01276t3yvCrpoRHCP72/2XFrAkaJQ1HQGqOjZA3MwIp0PyEnQeRVJEceoYBbfKJYYVP8iyC4KnJbqhs1oWxnRqwqVnUvaTQrauT2jLP6LGn58rUqdvBK9GoY8B6KrFBqJqpSRNfWMjWyjxHhTE7QdODpoDeawu+ANolcXOIOG8jmQ2pJYZIDl+6nGtJOHVVGW0JTMLt9Iu81Ffmd0lXR7oS4jhXz5C2BGrS1FWkrVTkS95dHrgql3th2mCNCePXsKB+hg1jQNzjjjDFx99dV48pOfzNuvvvpqXHDBBQv3eeQjH4n3vve9xbYPfOADOPPMMznh+VGPehT+8A//ECEE2Iwg/NM//RNOPvnkr4nzA+zSAfr7v/97XHHFFQCAP/qjP8KJJ56IT37yk/jjP/5jvOxlLzssDtBJJ50EIEV5Tj75ZN6uvdCTTjoJbdti3759RRTo5ptvxllnnXXQYx8M+7TTHqaLHKqOlQMGQq8EElvL5wmoX3PMMJruTW/Q9ChgenSGC9YCsJKdKScjWSSF1anjisWmNwsHZWHMlKJxnC+h8iKYatqVeUxpWywm526V8mZIpbbhyRfGMDTXrptC2Tf9ffFkI8UmJcxclChwcpyFSs5G5QLwbZsiFE/XoJlMRTFIdVyW0leVzsVxMyqnROCb8uT57+RkKuHLeidyG89ec9o3CsVa0Yt5EgwySSJK9XFmEPYBxqsEFIIcK8uQoEzOAjW5LjFs0g/y7jOy/bPQRsn4iQwtRKtgCA39Ec29UzIRRT6H7CMTyDyjUqs3ayiHnZ6BUuJdKeHgdC6taq2kHdQ1aGHAmhcAkZ+D5KEoCrh2IimMH0wh4kcXHt2C1bmRPh2cgSH5C/ZkFIypaPKSJ1ZWXS+cHu7fAhsX79IsxdvN7K8FH+mZch6XKZxikPxDzgmLUMr4qkuFDL3H1gGZXeomlhlfWg1eHDfpG9Gp56ChXmoPa9nZ71ZzbuO6UhxXJTr+f/b+Pei2rCoPh58551prX97LOX2jG0hDUEk0XmICEZqEIkbEksS7FYMJMRVDiR2j0FEimigSq/kFKUMQ0EBRQWJU/oifFctOAfksiSVQXjFK+CX4BWk13TR9Oee877sva83L98ec4zLX3qehD32O3bBG1Tl7v2vvvdZcc80115jjGeN5YqfFeMtrIS/k46uxnL+gqBsGgbybtdzvMo4j3DYi+ZG3/hlmd9xxB1784hfjmc98Jm677Ta85S1vwd13342XvvSlAHIQ4U//9E/xjne8AwDw0pe+FG984xtxxx134CUveQne//73421ve1slY/Gd3/md+Imf+Al8z/d8D/7ZP/tn+MhHPoI777zzUUubuRL7FBM5alutVlyF9e53vxvf+I3fCGstnv3sZ1+xJsfYnva0p+GWW26piBX7vsd73/tedm6e8YxnoG3b6jv33HMP/uAP/uBhHaDJJptssskme1xYehT+PUL7lm/5Frz+9a/Hq1/9anzpl34p/vt//++46667uGz9nnvuwd13383ff9rTnoa77roLv/qrv4ov/dIvxb/+1/8ab3jDG6rq7ltvvRXvfve78Zu/+Zv4ki/5Enz3d383vud7vmen2uxa2hVFgD7v8z4Pv/iLv4hv+IZvwLve9S68/OUvB5AjL59KiI3s9PQUf/iHf8h/f/SjH8UHP/hBXH/99XjKU56Cl73sZbjzzjvx9Kc/HU9/+tNx5513Yrlc4lu/9VsBZJXYb//2b8c//+f/HDfccAOuv/56fO/3fi+++Iu/mKvCHokNxzOkZi4rMUWbTol1fmFYKHQ4BPrjEu05zstPezhgcZCXnUfzLeYl7j5rPHzBu0jLad232Ax56bIdGoQSGQrbslzZOq64qRJKg5Aa0urLDoLM1SYrSduobbTY1SPAlDBzY3jl7RcqkqL2zwtBVZVEJIA5AlTeewiHjD6mXrXuSQaWJFBUVU0SqVC/ZxgxMWlhmkeg8JO4Wbk2JiGWEH0cnBDrURQuGuG42RiWjyBCwe5S4iTnrJNFERzFtUJRMC/Qh/GJCd4M8ZmEJO9Twk6WZ4RILDghzIytE/6UYtYLvIkkVVRxLbAA95cK91dRAo6+mCoyNDYtfjuGsPK5qtW8Ij/Ushk6QqQ1uwi+obEXW5XAuwdOMWqcGY8qaV3zAwE56daxBE2SZHMemyKBk1oVrWQBU4Fak6u19Fj7mFMbpLGhdSzDUEGACorS0BpA1Vz1uQIluZr5aHYjEJWEhhIw1UnW1SgbzRdV8nVrlPxDGXtdYrLVqJLpURKf3dZI1KfHjkxKboO0la5ddDmRH8iEr7ktrSInNDz/ErQ3HIhgc2whkikOiF2JWJW5wHSRKxsBCExXCkXs2gEqOqkjlVYlruc3WUQ2fKpFII+C/VlJYdx+++24/fbb93729re/fWfb8573PPzO7/zOw+7ztttuwwc+8IEra9CnaE996lMZdvtkdkUO0A/90A/hW7/1W/Hyl78cX/EVX4HbbrsNQI4G/ZW/8lc+5f381m/9Fr78y7+c/6a8nG/7tm/D29/+drziFa/Aer3G7bffzkSI7373uyvmyH/7b/8tmqbB3/27f5eJEN/+9rdfEQfQ2ZNa4KBT+SXYowWjMPVFFELCeRGuXPQ4mOUn0KLtcdjm950TfIWYfDdtg7Mh7/h0O8NqU3S/SNcpGhUzNgA9nLdCCsfh2o26cXv1QFawDoeJGyOK3ErfqyqDVVATl4vT50Y9eHR5s4LlTDWJ1J/nSV/2pfc71nvSbMR5oivbGaJQk1+lHZX4dNihDJb7EN5kjSKocwmAW1EfixI6OZuhE4jAejAZmuulSstRvsc2wBITuI+1swNkB4jfx10HyBgk4p+1UA9nK+rirBel8qBa/RAs25q630TRvmwbl2pXDml5vUzEf5wnZQzEQRrBYawkTgzCTrVRa6+pijVdqcYPUnKu1dhziuHa6twg8gsbMPM2zK4DlKwqm55JfgpXFKlS6p0KPsj50jGZDiDttkXDxtCl7XpfVLI/qArDbZQKVaLiGCLDp/k8yVkpDsXCMcw9LA38khZyEJFjegqoIWiC0vNjh9cw4WUyouZOeYZjlvEKmqaqNLf7eR6vpmpLVOO0/m557WScxC7VizOibNjKvc7abKFe5AA5P07PnTxmnDiSdD2SscChReivWEZzskfBNpsN3vnOd+Ls7Axf+ZVfWXES/sEf/MGnvJ8ruorf/M3fjL/xN/4G7rnnHvzlv/yXeftXfMVXVElTn8z+5t/8mw9bsmaMwate9aqHLdWbz+f4iZ/4CfzET/zEp3zcySabbLLJJntc2KOUBP14te/7vu9D3/f4d//u3wHIqTC33XYbPvShD2G5XOIVr3gF3vOe93Ag5pHYFbuxt9xyCycqk33Zl33Zle7uMWGrmwzMkVplKKiGLJPG0R9AKquJUCj7t66BLeHWxgX0ZZnd2IjGCGkhADQmwpbRGZPJER8AkQj4BgtDFTcqvFzpc5EsQK+4TYJoIUm7AcPQSeJ2s7J2HEVXVFWTKGbL5/t0rvTKn0P1msBOhcSrPqYgl45C6QRFdS5JVWkBKJwqpX0eTL+fvMlheqh9RrmYNgj0VekTlaRHt60Te/OxRgnaOrlUVfUAgPGRoQ0TIar2HFIXyAXztiJ2BJB14FpZzUvCp/CcUMTELyRioSMpWteMI24qCVboj1L+B9STpeqvSnuKo39mB97MEQ0V/fhkuaIEHzWJIQu+bk7tOBoFd6nIHTVbB0sdAIbOJEom5IdyXkyk2BmpnOoEhtORUOZScqmG5PhekUgiV8f5ulAByFEG0coTmEUXExCU2qyEI8etB9bKY+LVIDpyadYilqINf5BfN+cdtufz5/0R4IsUTJgnLg4QDK+G6XYkI4yK8o6iJkC5v9V9yXxOei7S+y/jb+hUhIiuW4NaZmQ0D8dW2p9mkd8bp+RqeEyq9AGXhLKNNACdyA36pamiihTdogq9TGoKhFFhwdW0z3Y1+P/6X/8r7rzzTv77P/2n/4SPfexj+MhHPoKnPOUp+Mf/+B/jR3/0R/HLv/zLj3jfUxxPmYYCAFQsrFUJNOc9mPohQR+XXJ9V36H3uYsvKQyaWJpDMgx7bTct4qZcDhLWHOpKCi08uaNR1gOOSPiGpEK21H4D6+Q3NAFTxVBsTOWUULjf+t28m3zu8rrTlkGcHl19xhVORvILwswwNALlDLGpydO6ug1sGuagEtjWcNUQs+oG8PWyg4T4tdPjFKzF+Txcul6TG+rj82Q9I5il4ZJdLZ4ZFWNt2ksFUHap8kxyeXN5kM9FhHIvq3WbkEoOhCnl7LZR5cvKAeoKyWHbBLRNvqBRnViMZodw03sHXyDF0Dt20BkiS6Z2FDX8o6BG3kZmoKCtPQ8X5UxVOUR0+BZVThOJsNoytjLspBwfui/IwWk1W7ZAqZwj51QVmpXPTYQ8cOm8VKWRTYbHsKZb0FWa7YocoHL/bqJA2L1AXMlapEJ3kA7z0zvMHIZDIVPdkLNzXd7/cJQQ56VhsyiOLsadjzrSkBT5oILAGEJOVvqT+j3JfZ9hQDnvsTMVlYMTO6XLR+NY69PtMwPEwuBuZgFtyfNr28BjnBaUw+B4gRp7xw40j8fBVGLBnEu2RyCa8uX2zkOTXRW7++678Zf+0l/iv9/97nfjm7/5mzkh+3u+53vwwhe+8Ir2fe0yuSabbLLJJptsskdm6VH49zg2a22VKvOBD3wAz372s/nv8+fP46GHHrqifU8RIGWGE/zya+hGEgJAXkF1tJSD6CaVlVUIFtuSILcdGoa7ALCsAFlKeXUCAFFJWRheLSWJxAzCA2QHgb6I26RZRybrMkOsOU1KWzU3CUaVJ6Ez8At5z8iDWslVqx5a4epoFEeCEkeYNBxnFPxDEIHrVZKzioTsU2reRwQHnXQ7itgxNKCjevSqqpkk5C0wYrNOaDYlgkI6cEOsqm+q/Y8kPGJrEEryqZ9b7ttKQkHDfZyELMmgAnEpPpzFHg2zWYCb5YvUtIH14Siq42xEW8apU5HItmCSB23P4zQmA18aufUNtoV9cFsimatkEHS5oY6GooaXxrabDGyqaM6Yn0UnyycNh/EO1ZjU323UvpSKt1MRVI5OaGJKpUUmCuhln9agTiSXRnI0iKC5KBEezStD46zZQJGSyjjj+yMkjhTGw0bu0ZnBUMYRJzPPVfRkAQxHhcTyoIyHpcesyzfgvBu4GGPRDXAjSD4mIxWqQ4uTVd7xUCLTqVdVqSoBneVGvIrSqeIHQOAoqaST6Fvs6u35NXG/Vg9wupldgmnKmG4jZiUCdDDr0ZZzPN1KdD2t8sGaE4fmTFIJgHyt6H2zEX6uWlqE5sbSDEWqedXt03ViHucO0Od//ufjl37pl3DHHXfgQx/6EO6+++6qeOpjH/vYZRmqP5lNDpAyGzJCQk5POIzAQWFK7uQB05UHizEiFjn4UmkxOJkwooLIkhEHR8EFFFK2igiRJlI7KOK9M6C7mNs1vxDRrEbsslAPT2cQqFJon2ZWuzvB69Lb5OSesVE9JJj4T010SshQV3txXkOf1INFJvqKMVk/M+k9O2vKqahgOto2go9U+TDlC2nohGCt6oE3qIcRMWRvwq4OXO85BwMxcRtT6zh3h8rVSaAVAKwzMNz3ysFRkONYwDR2ukJK4Jdc3l/aVSCuRoX9rXJwtBG0ZZLhMcvbTMKy0DVEGBbQHaJjx2e9zQ0bBsc5aiZIXg5X1CgB02RqZ0az7QL5IVmRJo6anpq6+IzGrPydBM6AODWuV3DxnvJmPT4lb8jAdGo/tAhSD2a6NiZJW4wHMwtrWJgWKCaKsyOM5EKnYL2wpmtyxDCnsnDD5H/DYS7/BiCs8eq+jbOERNQPizxmDw/XuH6ZJ5EnHVzCjV0W43pCd4JZ6RCqjjsJc3x8m2lMPnp6PcPzPRG3rhxf56rf1UKCq1P7VPmrPO4JNTP1HDLOtdEM13nhI9AYkB0qorEIAM767KydmbkchHQQTxzmp8XpOZVrQ9egPdP0FkkWI1ZgaMq7s4PBcCDVcJNdffu+7/s+vOhFL8Iv//Iv40Mf+hBe+MIX4mlPexp/ftddd11x/vEEgU022WSTTTbZY9TMo/Dv8Wzf9E3fhLvuugtf8iVfgpe//OV45zvfWX2+XC4vy1f0yWyKACkjnhldMUPU7wRfzVqPWZPdf4oEATlkDACncYZAycxbC0NkW72BK5UDmsNHSys0Cs4CsmYXkbe5dYApvDImRua1iAtSmG/UStGiP6RQed5/mKuETtQrbwCVurlObEaUhGnS5nH9w0d47JCYA8dtA5MAIu6JTlgrOj+dQ+TIlUBKFH72c9H8YaX3ET8L73YEeeR2J5EG2UqoW+twWaqy0USF3GnCJ2IQAVZuV4x/9NVW1hZZ8Tu/5+uhVLrDrK7YAlBzGjkhoDMqoZmuXfAOkaBYY/nzUBKYdaQyJsOEm7G8WpcYyrVW1OBTMky0Sd+NgwMo8bm3IiPClVkyjpJa5ZdOq9qtoSgNA1bknBryVBVhwC4fllNSMGa8Qh9FGQliMkmiMzpaRNE3RmGMiuqoKkcNL2iJj2Yr+6Uxx9GPkBgTytVrI3i0MQoKVg1PMsb9osBW88TJwHbhcXSU8bYbDjKJ1a2HF/CUxYMAgCfPHsINLkeAjtwaQwmnPFBKw1axw8UhY60PrA6wPs0YrDnJ33MrK9CeShYmvqzuNKFZlbb4JFHazsg1JzjdyjWA3b2Ho4Vce9VHMh6MSMFoOM1BKh1pnCgZln1Vq1lzjqKh8nFVYaqg7dSAE96viX2WQ2AA8PznP/+y5MY//MM/fMX7nRwgZcMBgGWqiNjsqBLBB4um5ECEmDingvD0pgnwBS4L3kjOiTdCWpjnIHSnQHtW8jXOIlzJBXAcEs+sowDQHzUMYfm5wVAcHAqJ+wNgOCyT4ixCC0YCuaLMUrn3WkLOTuXvaAFJJopL2M3hUZNIsgaR7rBCvpaaxNCbXzbCpqp0fipRRCJqO7Cc46B1fjRMp/XIyCrHTel2UXUNswGvIlyBDt3GwxT4kEuLvWcnEyGIAC5pfaUkTpwxYH2u+QzgknXSjHMIC8oBkvwqZhE/EKg165kRJEMeqTo/b2CJtXpwUm6tSPzod5k5e5S31igNtISq+gUAsDWqMkbyNWAY5eO8NDtaUu6rzKocnH2InH6m72P83ufQejBtgYa3atbesvtQOzZjS4pYkvcfEmvpmZBUnpKcF1dWKiguzOThLtQQSagfvGjCMQVCFAjYDnFnv37uYOgaWoF9g8r3iZQHduTRFb3B5WKLG4vj84RFnmSu784wKzfFxbDAtuxs6G/ExZJY9lAZlH98dh3uvng+f/ehA5gL+bvNmS2vGuKqIUUgXzcSTDYxSW7dVhwjDYtFpYHG1ZuK8FAqJvc4SE6Om5SzpCHx6hqq49YEjDkLgfLtxhQJnDZA+XiznGsVtrhm9tleBv/ggw9itVrhz/25P8fbPvShD+F1r3sdzs7O8PVf//WsDvFIbYLAJptssskmm2yyx6T903/6T/HjP/7j/Pd9992H5z73ufjN3/xNbLdb/KN/9I/wH//jf7yifU8RIG2mrA6oqsOAOT58qdYK3mHLkhVgaIESnBENQwR2a+FIHXmrVkzlcGEGDuP2xwoy4SS8ujoiqqRAraJN2zQhW9IMcbRfqlSbS0VL1BACtU/p9JggcACtjvLqTPpM958cLL9Yr1aNlICswsexye0BgGEhXCysAVVR3EOtrMs2P4piqSRMxzw+FBJPEkJ3RpK7KVoQUo78AEA/yPugGkwSK9YCszIO5i3iIl8oUq32C1fpyIWRpEpYKN2yRi+Lpd8ZXlI8JW5Ty5/weasKRlaLp8jBrFbLTqO73qhrZL2OZMh3dKJ6lbSuyQfL3xxtGsMNugJuZBaStM7wK/2O9jWKtFTVc7b+zRjy0BxOeseSuKwiNeq448RtAEAUkku6BtqMT/x7N0SOnBJEDEDg7BAksb6Mw2beYTgu8FNsGHb1M6maI0i0WQw4f5hx9BsPznD9LL8/oAED4FK5qZoYsDLC97Qt7IOUDD93Hssud9xq7tGXCGYs4zD4+sJxUnhp0xCNwOHeqAiRingxJ1KCVE6qyA/DZmr8Kk24qopM6dvpZPsdAlOtE9cLNE5Vs1ZzlUUdZTJIRiJxAGALx5SZILBrZh/4wAfwH/7Df+C/3/GOd+D666/HBz/4QTRNg9e97nV405vehBe/+MWPeN+TA6SsPcl4NZUBJ2+U7lIJ4drEM3hSVTC6mosfJv2ILZUi4ST6uESlx7RPAFFbVQJOEz/NSVax/SbswCTjhwU7U8qR8nRcXW7uJC+FJ59Glag66Q8RRxTIxm0E+tNOlVSBqTL4VtqoNag0k67WG6PP93G68TmrbakxTFJZlRdZeRF9Lo0F0X4U7NU0dcIATaDkTKmfZsijjA/Ol5KHmfG2cujoe3oir6ruRvknWcdKHiaV8Gn5njx4UI0TIH+mieC4Qsoo0c99zotRl15BEzrXZ9/lqNiCH+ZBskN7MHac1IMh52uo3znZPraqP6mC0YueW95gqjZXor1NHThnuNjv7ssOQZW3SxVkBcHRmCHB3LMtWk+5NDMQrXVonSIMLE7RosW2ON19cNgUT5cZ5mHQlZKlpQPa0uEz62HLcQdysJLlCsDgrYgEq9J37QhqokOg9DWtNdQiyXrDzgbUb6hfkoKtyNHxC4H3wwLwByUtoFS5MRUJgKTaansLu63nPk382q6k4ougcbsV4knN0B4bI1B+gbCHpYEdDIL4l9fGHudOzKdj9957b1X19Su/8iv4hm/4BjRNHvdf+7Vfi9e85jVXtO8JAptssskmm2yyyR6Tdnx8jAsXLvDfv/Ebv1ERIRpjsN1eWVLWFAFSdvDxhNgDw4FUL8iKl5aUkFWnWo3bfSv3EYQwVjQOFsCekG7k6h+BR/LvackOkQ2g6psBauUjK3dJiAVDLno5wftsE0BQjFVZdwawROBY+sK5yNCfMQmR5BIIIts6hgQzgZ2pfg8z4jFREQlJuJZtvIo3At8EGrkRLHvgFHRiVORIR0IezpIxvLo3XQc4xfkzNmuqlTtxBWFF7U+whX6/2Vj4kxJJpEiNkRB8swlCtqiiL1o+IzIZnlXkleB9BgUR7MCTGn7SETedUK4iKVzRpaMqe+AGs1v8VsOUqhqrUkDnyJOSFtHcioqjqtKno/5QJJjMgWPAt2j+Xf4yk4MGgWeqWS/JuepNabw0VH1UvS/nkY+F8iqq7UYL59H+1b5T55AO8sWjysma/CjxvpqNRVtI/LhK0rS4tD0CAJwezNHOhfQQABatx6LNDVs2w17Cy7XPk9ADpwdYn+W2xNMWrvDoWD2fcTRVAqQcbfPqVh9Fbrl6LEhfEPQdWlQq9UCOjnPF5CKyThxrfgHCqxZMrcOmyC/5+FyxltCeFk434lLrA0fn8k4I6reIRfbFlmre9szAzyz8oC/S1bXP9iToL/uyL8Mb3vAGvPWtb8Uv/MIv4OTkBH/rb/0t/vx//+//jVtvvfWK9j05QMpmFwLgI08yfm52qo50WH784ADKJKAmdQ3vVAKgQBGuLBNSl2S/yhFhllknd0EaLGwhXjRcaWEqh4shLvq7E1LHbjlwWT+V+adkWKMsBsPaOSkZRE+eWXmxDvoJkEi8tTxM3EpC0m4tkGAFUaiHgH4wCqnb7nc103NV8UMpFJrM0Zgdh6d+yFmusDOls3IpNMEUUarWCJoYAkypDDNeqAjgRNeLtnHZLnKIfUZsvwyTSG4I4m7JfXJCD5DmTibliiGanCLFUKv12qpqPflcaB7UZzxQILOCcm54X9oBCtjJydL0A5lqoLzv68qovE8R8ozO7NwfyQmBHqzkMVFFWlQ5ctjntEAtEMII5hvlf11OuLVigtb71zBfIIeUHCGLhs6rs5KDRscy4jwnK46uKV59dqCoMjGhWeWOmzUGMOW+JwLKlWHh0zBv0C/ySWxLWy52ESiMybaJvJgxBrt0CsHyIiYvUurPTTAIBBcPuzlAutoqjwMa63oelOtNixk/V7lxVOXWgSsbTRTiQaOYTvkOC+K0u41BU8ryW35N6E7yvtqVUIswJBmT3IsK9nY+Cp3HhlIhLFzn4KhC9FqYghav+PePY/vX//pf4/nPfz5+5md+Bt57/MAP/ACuu+46/vznf/7n8bznPe+K9j05QJNNNtlkk0022WPSvvRLvxQf/vCH8b73vQ+33HILnvWsZ1Wf/72/9/cqsdRHYpMDpMzPLczcCG37SB0eqEP5eyUcVOBBh4Hp7/zD8qJ734AjP6mhhGu1SrNJqWmr5Gva5ShhVDhmSvJgF0GsRMOmgafIkuI5YlXrKHIJ8BYo4V/miqkSfI0kAHPo2VQJiBzB0Srgg7RVEjul3cKLoyI3CRzq5hVhkETzMTQhoWyKlNS6ZMLFUlbug0QkbG9gQatwancSnqAYgZKIGucdE1KSBEZsjSTSqgRaTXrHq+HGVNeOXwnyUZ9HRfxIq2adybc33K2iG7rvdZJ1JWOi+FsoamIUDKhV7CV6UZriFf9LH2W/Q1RtEJiPI2eN4SojjhZ0UoUTZnXEACjjhWFAKQJgWLecO5DHJMt19AqSoeRZHc0aFFTD0Y9UjV8dieQoF5NsRrgCW2VJlRI1ZL6pqPAjfTMZ3sbkoT7w9vahDrMHcvhvOC4yFUeOIXu/zPBM7g/qCx0SrOFPqaySKDRHn01CKnMHSWUMVs1nxnAfVv2mIoIMZY6KKoBCYKqqFDWxaf5e4nFtAmApMkXcVUaN38FwNZ5bSWUeEVdar49rEEufmMI/ZiIY5tbJ2TohWtpvEDvLVcHXwj7bITAAuOmmm/B1X/d1ez/723/7b1/xficHSNnqZguzNHUJtgrpAqh0gi4XdtcTZVU1Ud7zQ0U90ENvJF+nlDGnJqkHYlI4t5RwWyXAWOVx8GRfYJSVrSt5uHxZORx0LioPxPYGbi0wAr1WIpT78iWKxXbUd6gfILHTYo6Ry3ulj1NFMWA0dIACo2hHgyZ47XySg5RkcnO9gmIUUaMmpYultImuR1g2wHWLcl5WHIHOcPluVfo+p/cK/qScL12tpZwdHi+q4qYac6hhLqB2xE1UENW+fSXtqMorlYC7HoqQM/GDnCEZde2SU2PK6gZSu6U/kVS+jzzD2EmMwYqDP7P8BUuivTN1nhWcl/h9VMSPXJFITNb8H4CoSrTV/a1FcZkdnB/uci662innTAmsCWRnj8YWYhL2cL4IBoZyxraDItyk0sgkzlDXIS2y05O6ZkcPLTs15OjrSs3yqiDLvEG1+5OlsYyEbU1SDqNigt5HoaCrWqPBDsllmKn7vpOFT7Xg5AWKUeLK6ljK2dK5g3ReUjIvDPI4kGPI2ES1YJM8POlbdqAKEzSlCFwT+yyHwH7lV34F3/Vd34UPfOADOD4+rj67ePEinvOc5+Cnfuqn8NznPvcR73uqAptssskmm2yyyR6T9vrXvx4veclLdpwfADh37hy+4zu+oyJKfCQ2RYCUba4H7IGqjgFkxaR4a3jV2ahEPfKygwq1q+qDHFaX90CRazgB74t1rpTiuY607NNQ+mSJriKBYBSUo6EWtXrsZDUNjlYpiImxvdGxdFI4pI3jdtGKL59rOcdZytIdADCPsnLnHws0h2gQCYaj1ZszMopVIrgJsjLVkhg66mNGMAeQk1aBDO9UWkbIRHS6ckWTAI5D+HEmSbGJEnVH57VvZVYp1xMPi0rgRQRLNmg9qirqo+Eu0Eq2vG9q6AHI1yoUaMH1gCt9kBXUS/SiJOhmHSvZP0dFOGKSKgihimyV6B23G6N7bdxFKkpWjTkN9TI5aKohZD5uDWGQjaNgGu7LPD70+12IKmucSZQgEWTeFEilsUgqGuQoekYQ2EaRbDYO6TDfGPEgX5D+XIf+XOH2OTIK4qq15AC6f8q+5gG2yPA44i1TOnApQaR9DLgiTNNCkU5c6B1iiXIYL9GOKlJS+qthWZ16QGtyQw4mUXAv1vsyKvmaj0OfB3lfQbkaFt5DnFpV66r0A00YS/vX/FvUCE1Eq4lEYzNJYVxL+73f+z38m3/zby77+Qte8AK87nWvu6J9Tw6QMn+UYA5inUMwKic3XYRx4nXo0m8AsGvLE2V1k4+I7Wib7Ee2J3rYXS5MXd3EZZOuOGtUCFzBWmwGfOWrKjdFnlhVTO2L9uqHEYln6lLruWwjJ5FKWU0bYTjPKaEp/dk0kavTSGMNyPprAOC9w7DNDU4E7Z06uDJ5NWdKmPFiQndaHJ8C6dg+jR5cZbKnXJxWNLuGA1OV5AIl56TdnU0y9FC2E0Fbk0RJ04iobiqVdsmL/pbprRITFYhBO8pSWaUgxSgPs8hwnKqo4YekhuDSrsMa62opTaKp2wPkh117lo/brhKa4lw6YjseoqquUZ2UEnYcPpMd89xFCvahjzXNhCqlln1Ku/1CEC5Ys0N9YIOCu/bci/lYD/OkMKigUoHIpJqPteU0LUIQIkTOJ1l0iPM8jodzM2yuz+/XN+QL058D+vP5N/5QqjfbhUfb5veLtjg6NqIt98q8GXBQSt47J4zPffFE+uC40nOIDgORHpbO6ocGWxLNtbvXqyKQVNprDJ+qqj8kVeHXgokDLS3yVB6WdjR01SwvnDqo9AD5Td248hr1mClj16t7RjOdJ/UbQiybeh7lds3qbfEaImCf7RDYxz/+cbTtvlVktqZp8IlPfOKK9j05QA9nFkCJThhVSsocN9EiEUtqpfpeft7LzVgxMSsuGx3J2WE5VqZ5hHYYccvvrY7w0D5U3k9Sx0+jSSCpVYbmHKoV5OXA7NS0o9wLIDtEvC3ClVVp15Vy3s7zBG1NwlAm5RAt+pJcuOnzgPe9Y+cSG8dRkWaVf9OcAd2l/HF3kvjh3Gwiq9eTBIFJiaVHwsKiP8r73Z4rD4BjoAhjwx8kxIWsrAHAtlJGnFSHWhvR0fgoD6PGxUpIN5a+o/MaNg0iXVCbYDjZkiZ6I7lL+nrrSAiLScoKNenkVqWQLY6wqaMasrm0RY6XJMWnGnv7HoLC5GvrcaKSSHXeVn6VY8XGcD4QS0cYJfehomAS9TE1FxIJV85tLRsD1MndvZJ90Y7+6Ph0XCCPIU6W3wZ2duyQqTPyvugpqjw/axEpMX5WnJ7jDv1xGefXW2zP5a/25dUfRqSjfK80ywGHB3lCOT9f49wsd8hhm7cdNRsWO7UmYVk8VdoWYbCJInkRyxU99R3OfH6qr0onXtjO8cAq0y+f+Hmm3oA4H7EzdKrQXEW1wywRcb6OOsem9GfwgDcqmlqsZqAHH58WGCwb4yS6mENXFHU3iiOtHFPnK43yhcgquhIVzeW5epScXcnXXG37LHeAnvzkJ+P3f//38Xmf93l7P/8f/+N/4IlPfOIV7XvKAZpssskmm2yyyR6T9sIXvhA/9EM/hM1mV3hvvV7jh3/4h/F3/s7fuaJ9TxEgZe1FA7e2VcUCacFUopy0CPECEWgMmeACoI72MKatWaNHUJa2qHN53DgaU14VRKBJ58Zef9JEcyNMPG+sjyXlyUpjTOfn6NQIguEUWR4thq2Gf0qYIQSLDVW/RYu+LwKiW4e4KbkmJCK7tuhWco6cb1CibG6T0ND7rcrx8UlVmlFpsMGwLDDDkUVfcupo5T2ciwiHhSX2YMDBPIcJiFXXqXBBSEIVEJORfApDEJ6ctzWJoQemF0gA8+O5hECwUcm5iYNhtt+4ksrEsK2hU6BEb1TUZpy3ZiJgevWech8ULKC1wvTv91WUMUPCwjBZI7Ni60ikLp1XRIIcJRjlr3EEhn6mYDMTEg8/igQlK+OBzx0EB49yf1QOXVWZyVWFGg42rJOVqK+tgUl7blK1D010mFqJ+oRlifwc5tftsUV/VKDWQ2DIRM4Z7gKQjj26w3yS1x+ucPNBThS8fnaGw3LyjRVNL9L3ak1AWzr/yG5KsxNWZfAMyWEoJ9mYgLZ0BOmHnfUzrNb5u/6shT3L5+Ao2rqGkAyuFBSqoq4MAyaBZVNrEMqF0BQHBNEOBwpiokhPxQKeJOIyfgWyJiOVxwcDp3Ln6HVvHoyKrFYCu2qepivOWREUtdrsCcNfJftszwH6l//yX+IXfuEX8Bf+wl/Ad33Xd+Ev/sW/CGMMPvzhD+NNb3oTQgj4wR/8wSva9+QAKZs9BLgZqodJHJedAtWNs1Oaq6EqiJ9ggoKpVXi+Cv+OIK6KP0M7PwFoilNAD4DuNHH5cpVbxDi6lIJmnLtASTpvaF9yaWt24YQElSSKCuun82CG65nDMM/t8iUxc6M6JnkLuyFnx6ArJfd0Xs0aVbmrQIbF4RjAD55kJJ/Hz11VHsx9QJPuYf4HqAfPYUCzKDDdbMCyKw8blZfkAzFkS5VwZyPnLOncpaY8YCKMKG632eswJjEHU/AWftOW/VI+iamSmeuSdel7qL7MG+vrCNTjUUOtlfM92gfbKKE6C7uqj3nMyo+oj3O2sHyX7hXH4prCrVOJnSoomMrwjUqu/lRM2NiVI0QwnZJn2Zc0m48tjjT9zQ/3QXJ8jBdl+NQV2HbZYijw6nBg2emm8TYcAsNxyfE5jrBHeZwdHWWn5frlCjcvTwEAN8xOcVxwwKXtEUsnXSwJaheGBfP0RBgWQdUO0rpMMheHBU6HAnv5FqshOzunm7zt7GyGeJq/21xy6C7mY1GhRneSFMdOFJkRggPDSEiUigcWFp4oIZTkhS6KYONk6KSkTRLTY5hODUgak7CSAB8VXKWvcbnObiNOrSjXy5hujHKeR4vO/GUA6domQX+2Q2A333wz3ve+9+E7v/M78cpXvlLmSGPwVV/1VXjzm9+Mm2+++Yr2PTlAk0022WSTTTbZY9ae+tSn4q677sJDDz2EP/zDP0RKCU9/+tMrSYwrsckBUnZwT0Db1PgCR3iUFhOt+EILYfNVRHccMTGj0KqGKeg3qtKBP9d1qSpZmVbLbiMRIAlDiwCj9ZIgSKsZJMu/r9qoV8D0VaNWznvYsPNOyk9UIqs+L66s6gwCMSZzdZJUIuly72YlyYrM4jrs70N69TNTXZuqEo5WkFpbitqwSPAHJfJTIlS2k1h5jBarPn+ZxoQ1iSM5KSlIzybYqDsSDH/l7xo0JTJEUEDbBAylvHgIFihwgyR3SzJ9sxZY1W3BRG0VsaQuyW+lP6ivoro2aY5d27NKrBLzebVsquTRvdEmNY4qmGFcet6jKj3nKJeKunACu69JFem863MQaG1M83BZGIB+o0gbjY+SyKxRL660a+CPc9TELxz8olQpltfhwHDl4HAI9OdKpO+4FANct8GNR/kGvml5ihvnOdrzpNlFAMATuksMYTkTOYl5FWe4GAoRZxl769AxhLXyLVd8bb1M7UPZth5anK5Kqf2qA1aqchX5PmxpnK0Vo3J5HYs7U/SPWJqD0jWLrUEg6FkxmXMydA+Z2wYAo2hl/n052NIiutH1sOBoUYp128ZzxDiiucPc3QsFQsXMrXc52uyHsPd7V8NMSgKzXuHvP1Psuuuuw1/7a3/tUdvf5AApa1cRTRP33gTsEDSGHzAmGFBUXuAIBQWocsnUYMdR0O+1I6H5evZxWWingCYMvzCwhbF4XMaeNwpsZAfDD5nK2aKvRtQPGfVg4e/odKDReWnm1Ux5L++B/LcWvNSTYpWLUtpNk95YHoK2VTlbiuNGJlP6PIlEyDJwON2N1O6BnKc0Nl2pHYKtnByq+KJcIP2ZDxaRyo9LlVscLNJZbpjdGswuEgyYf9OoB1DFUdOnHZkGLauBTj1s6PwVLFuJoe5xbCuhWgW9JXodwbt6v/R7TZFA1zkLzdL50PENwyhuk9Csy/Ug3pyQHv5edEZgDgVVJ2e4Qk6fi3B1SR+y+G2MML4cdzuAb2zK5Zk3CIt8vcJcnJ7+qM7nAQB/AAxH5PR4tCWf54ajfHGfdHgJn3eUy3afvvg4ntw+BAA4b7NTNDcD5/UEWGxKJ56kOS6EXKX1J/b63K5kWdXdpxlO+wJnFed92zcy5rxFLDQS5sxJbo8SD6UFSHZOyRFF6SMNWZqqGg8Qxzx/WaoN8+8F9gTK3BnlWPscaVvmC7c1iHQdyKlqIeNXT0bRYCcHTtEeQJqixq7hSlanHTMF9+7krXntcV1l+yyHwK6mTVVgk0022WSTTTbZZ51NESBl6xsbdMZVRHOa1wIAKhLCAOYGoTCIJsiqmGy1F74n4bSCoui9l0VOaiQ/MLaSaErcJ25t0K7LKmarNJxohT1EBSfI6iU1VOWmV81GVtlGQS5eGi5cLgrqUVwwBHv5ueW2UhQjzEzVT8KQrVZcOsFXQ3OKXZaPq6o2qB/tUEfLy16FjDEY7tDInW9gClQVDQCKWGmxrXK6IdhKPHan6k7xBKXBwGwK3EBw31bxRW3rpG8gQ5sUEWnWoi21o+1ULCpdsjCjvi9jo0WVAD+O2lTEmTrx06joGiU2K7ih0gWjLtSJ++p9xcar+2lMygioqGcCi1QqYUowxGxUoqxRcLRO+N9NdnZDUvBHiby5IGOnsRx5Yh24eQN/WAgFjxy2x3m/2/PAUKoJh6Nygoce3UEe7NcdbHDU5Qt90yKHWp60uIDPnd8HALi1fQA3uAyBzUvot1UddQCPg5L1fpCkvPTj5lxuFwwubDMsdt/ZIU7X+UJtT8vgXTVcZNCsDEcY2zOgzYdFd5qP151ENGeFU2gbmNBSEsYNIpGHzp1EY6jYwBl1PU19bVVuM1CP46gqF5l1XlV86nlUFzbwNmeqIhSyKjCkCjTovV1QVApC+DmG3FG/z9H1hNBfu0fnZ3sV2NW0yQFS5ucGjTfsKOQcBMkRIONwezLiQCi4hR/uI1K5sSCglp/YlyuRQ7dyk2o4jIwY9XOI3/B7y5U2UrlCjo8WENXnZXp5yOqqHg5l90Lpb7almmmr5e7LRDlrEOd5Ag6HLZf/+uIU2UE9GJOquLnMQ1L6JVUTLFDDLMmZin5/TGIWG8AvKR/JIhBbNcOUqXqgD+2oMQZMsqbZk+1g4LalPzm/QOAsGwVa0BOphgzG7M7NOqE9KyX5Z54fRslK6Tk9jJKVSS47kQVK6sUpl0o9w5WH5Dz7BeBVDpGWUaGiNh4NZgQr0eeaWHDfA8SoPCZVfs9UBY0QGSZLN5B4yUk9XOUhaGooVMFw2oGndhGkUzGsl/6OziAt8oAJKkeKnKrh0GFzXW7A9hwwZP8Dw7GQFs6OsqNzuNjiuBAWHndbHLf5/fk2D4gb2jOGuIbksBlppZxFy9s2qcWFkBOKLoYl/qTP0NdHz24EAHzs0nW4/2KGxYaTDs3FfA7zk9zu7iLQXcrn2F2KMqbWgQVuK7V6De1wGXupfJy1XEkHY3YYlXX+WYaVSncrB6iiE6GFS4LKNROoaZ94snaEPImpKmHVpJjQuXR+NGb5+DqNQC28eO4cMZED5b7uDcJ2D358tWyCwK6aTRDYZJNNNtlkk032WWdTBEhZs0lovIYegoqa5O9kfgvJIqWVptDwK2KzBRBLhVF0UGxae0wnGDtJsjYcHRGYxWpYSK3cmX8FBslSqJoiE42s2HTUh/bpk0BcKttXQ2AURbDWiOds7a4UQJIohO0j8xNRm60+l6BgL59glYgkt4tJAlXoiyq/WifQXWO58iQsHAbiHCEB07msWuMGLFqrk7cr4kk3quxSScF2kIiG1qkimK9dCcxifeIKPVpVIyaRsnCmggaoXwTWshwJTM7Az3MjJZpluOqu4o7SHDg6EjlKDg+tRCdNgpAmht2kdKOEI53Sc2o2BCkpvp6RttdYDNUEGXN6lc+RB6shLFNF+nK/qQR3JxG/qCsXS1Mo6qTPJfcBJY9bFWWQY9F93R8avq+zZEq5r5cB7aImzDyebXD9PEd7lq7nyM9xwXSciVgVTPFj/U34w3JBpNqrw5YSn/0cF/sMcT24XeDCJr8/Ocshj/5kBnOafz87sZzQrOVhupMSEVxHhsaRIBIdLRVP7BcI1tA4me0jQOOfrkdjpFKuUdE5BXdJBCgxTFnxVCmeL10IQfsK5dUvIRGguYoCNxCxaroPmiT6jpcL3PDcqM4xGSGtpdehRHZ7XDObILCrZ5MDpMwOCW4QcUOMSN8AAMaoELzdEZ4Mc3nvDyJQwuPGRa4MilRh5C1ADKbJCNylQrAEs1RhTCsTP1ly4HheVMryBIPUXxZnpVJ39jL5JfUQ4ocylScPWmBUNJLGZcr5C4aPRU6AW0McnaAI5mKE4QogqsjxgvOFAC4docqTtkVa5IsQD2aIbYHelLApO0BK26cq19a5CioXgWArnpS9fnirSbEXB6ApeVjNJlYkfnZUNaLFWKMze50FeuD4uZMHi1Pl1ktx7OhhoLWMJEdCwa+tYvbm81cOsRc4z23l3MXRkXPsTiK6C0Wz6iQ/EezZFqankjHlSHdtVVEFAHHWSB6JM0BT55TsQFkPgzpolft9MEtsZBzEpn7QUV/xQ3amYFO6v+dZHw4AwjICy3ze3cJjPsvnu2hFk8tHOa91adi25I1EGDSlY31yXKauy9lPSjXXpe0cm/K77bZF6EUXD8gl7O6s5PicSl5PS0LAq5pAUlfLmVGek0mRFy4IiecTmo+SMXINjJE5gnFGu5c4NjXiuGiIucrh2bkXBfIMWp9rJvvUlaRam5AdIO30KAdHFlwyz2ohaKtgbLuV90AZ/xtJjbgmNkFgV80mB2iyySabbLLJHqM2RYCunk0OkLL5Qx6dN5zsa9cDRzNY26ebcYVTf2TQl2qQnmCvZSbZAwB0EYZkFNqARGx0xKWRIErK3nCFECXhWRW2R1KrI1uvfoC8iqFVvtvIyp3MxKQ4U6RqqF4t70abouIvYvVzL5ElOzi4QSJDuS2KVE5BG8JjlHjVaHyUaFRI2NFVAiQ6YixA8GOB+NKiQzgs5G7nO/TnChHckUXhjFP8Q5oTCBIxI/0hFaq3g8B8DHspRXE7aEK/JJwpBBdaidSYBBjKzuYkz6Q4TcxO0m6WFcBOH8bWiKwARbgWmXsGKHAAK2eXrrQqedwlgWKpi6Mae16gvfZEoJT5g6G8DmgfyHw15qETYJ3DZCnIgOMrZw2My+dt2haY5+tksShtbeAX+fMwEwV3nRCrq9+YeJKiAJr0cURAykaQ5hwqSdqovpF9kcVGihPiogzaeUBToK5l5zHvSmRXPV36IpOyWR3gks0huVnrWQfOl8jvEB3zTMVkeKiTzErwFmFbEo8HC7MtxQO9RVukYjjSc6Z4fNaKS4nG41Yi2iYmua+MqvokfSvnEBs1IYwyRJM1Mm80EgHiiLgiiY0OFWRZ8USB4M38ficyizpClCM85b26J2qOtNIuRYJJE5eJAmWZ0T1MrxriZc6tIVX3OL3aIV1TIsTJrp5NDpAyuw0waGRCWHYMtYSCl/fnG1UNYqQEtoTH40wJ9yUgllB1WjWAqhoCANMbcXa0ztU+0i5V6ZMiHjZ9PRn5XDNZS+m65HlISFomKST5vfV1G6hNXGasJrryrCtsxeW7UXKOdJ4LTbTJGKRG4DTOfyplSalrJD/KSW4Tw0edQ5jLQ5RzYTQrtAqfawZshrC2uw9/1++ZKL2ci1UQWIar8tvhoOQgtWAtJJ2TwhOxLp5LQnQofWXk2hhdOWiwwz7e6c9FN0k/LDjcvzUC8/UCdRGzuNsIu/jsUsLsQj759sGcv2IvrtjpQT8g+XIiygFih9U2QFsaeXSAcJQdH39UYMqlk6ohgx24OTqDRHCeFWeHx157eed2J89pkRAL8WXqEkwZc/RqtbhmAmxZuMzaoqnVecwbcXqIaXm9bbHdlPMpTgu2VlULmp3zMl5yvpAEjqPvOQ+0KveExmSzzpAWkHN7AKDZxoqeQjPHA+R8KEiR3quKSZ3bs9doHKqFk14Y6ZyzfRQIlQOjtvGYHd2X1BcV8etYvy6oc9X9m0qOoW5+qO87cqod3+uJYXw3CPs47W+nO1IC/DV0gCYI7KrZ5ABNNtlkk0022WPYJhjr6tjkAClLzsLPm4pUjom/iDNlnrV+8jaVeMlVCEq52CXJ2/QGlkLZKsmUNa+UDIREFtRKVqm1552Xr6jFDiesDpKo2pYVo+2TSmJOqJSxAYTO8jlmLqOy48tpgdE2tapk6MUZblh146rVqR2kLZR0a1sL4xUWMbKKC4aqvWZKaXpumA/EL8B6TEU4O8MaFBXx4IgcExKqUHiyKrG9k3ZTsqRVvEy6QinoyhROyk0MGRI3kNvohGrRPaKkd62XloypiNzCaMwhqTGzMUhDfcHsgKpyS1evAXm8OEri3ia0Z6pq6ExxxAA5ulNUz9E2MMTZM8vb4rxDPMgn45eN6MApyERLolDELszkfKKKNOoxI9FMeeUoho4GdWCOpzgrF3SpIKxFj3kJsczbvM2ZyBDbEB3DVoHIMmGwHnID+6FBvy2yGOsGKFpaDScjG1ZQbzZ7ohcxSUVmBMZLdOvl/nC9VBA2myARQlWZSdHU2FmOOlLScehMBVFpGxP+VRxOcgtX94Seg3ZgdIPLJqqPIfvUyLWjcwZUtFVFYI1I9O3lCct6ghTBEY0v6iOnCFYRZe67nBGRKHRUkmU90hRR+QyyyQFStrq5hZu1qjx4VFqLepJoNtipKPBLi0iT7yLAkDN0OCCWGyuuaGdOwrzKGaIHULNOnF9jvOD3lSihdgjUhMVkjhTOVTk1qZVy8aS0dURI1Kj3Gl4pv1cVZ1ovSofya7bs0l/EvruumaodVYENcaccPLUWgeAuBXHpCi/Kf/ELcXbCMiEU3a9ED0Erk5fZWiaxFPoAlQviEsMY+hpr51TnEI0Zv7XIqwmmgtHIeEzZ+mFQtlYQAzvYKk+JTJfhV23Qm/S+KBVtRu0XWClsJM+jUUSDrpAEMjsw8rWRhy89DE0thMuwlDhxWq9NGM1V9ZoVyLQipRuV5O/owFHeTqdgwI4grsh6bSFavh36AmUN0WLT54b1fQNf4KxYXo23nLtnvYEp13a2NpWWVn5NXBVo+1S1l0w7H/uEW0mjTC9coJ3AT8LgtuMsonYiqxwaDccpZndjRg6OMs3ErkvcKwhMVSESk7iuVuT5aqzVVdqv8xvp2osor3LgtyoHb5D2E2Tql1YWDZqtWjl7FdNz1OOPjivXwMRrK4ZalJc/vd9fgb35zW/Gj/3Yj+Gee+7BF37hF+L1r389nvvc5172++9973txxx134EMf+hCe9KQn4RWveAVe+tKX7v3uz//8z+NFL3oRvu7rvg6/+Iu/eEXtezRsIkKcbLLJJptssseoURXYp/Pvkdo73/lOvOxlL8MP/uAP4nd/93fx3Oc+F1/91V+Nu+++e+/3P/rRj+KFL3whnvvc5+J3f/d38QM/8AP47u/+bvzn//yfd777sY99DN/7vd/7sM7UtbIpAqRsfZOF61TyqYp+kGmeCERZIVBVhuulSidsjcgtdFFWX+XnySWVlKiiD8XckNCclBD9ysOu8zLf+Ch8OJRw6iSiAWN4e+IVeoNQVvF+4dAfEZeMREGikozQicNMLd/uJlvawUDTxAMjOG+QyE9bNIfaswi39nJefflySjnpGeC2hsYiLKStBD9SpMcvFezUSRu1yrwpWkjJJlFXaBNCV7iGeAWbYIhDJBqYohNFPCjWKHgmGl6Nuo3oerGa9rau4uNIHfE3GbVaVtCBlgjR9P8aZoij71qrVsiDgtYUvb+OCIwrn5LilYpOyDOtd7BFSZwhS4+dKF11fo2svJOrZUiiivYAGaaiisk0i0AnJH0AkIJcA9tbSRynYaghGatkTBThKEVmY2zQFw6d3iacmTyAEiWHbyyro7uNwexhKoW0snzmz6JjSb/QHIC5ipSMknWB8oAaK40HiQalRhbwEVY4fYi7KaGqoqxL4PLnHL0YBy2oDZp8sLq25dpYNUftK47Qh1Rjk4kOlzpSXZqyjEwSa7TkDA3u3nKUzW0Mk5bSfWZ0vwcjFaxqfLMsTqfmtlH0mtuspjZdDSvzmIpEBiBsP7NjBz/+4z+Ob//2b8c/+Sf/BADw+te/Hu9617vwkz/5k3jNa16z8/2f+qmfwlOe8hS8/vWvBwB8wRd8AX7rt34Lr3vd6/BN3/RN/L0QAv7+3//7+JEf+RH82q/9Gi5cuHAtTueyNjlAyjY3ZuFIeoCM9YXotdLqUpUKQP6bKmqalWG8OVlhmuUwbq8qEbbYIdeKDlztlHXFyg5CBAZFNldM52OQFlecFafnoK0cCZ3vA+QHVZEcwnAAhIPCdLuIQKNmawDJy+QEY6XCiByhjSIO29aMyPk1wrEO0VDpD5nSuabQDuTS3XKqunRW5dpoB62CGagahEjSjEFyMulSpU/S+Upcb24EAqNXb6p8Iauund4OlIdO1e6yD1WdpLWOtMAo2T7SuApm0Hev2sbPAp03sWcVyLCDfm6OHiD0HYYztNNjsFuR1taOG1+vhXJ8FjK27Lw4wm2EczVjeAyGqSOiN4jkfdK5RMNCuCZqzxBSOVjKzQ1QOQ3USEf34sbW+XjqfpZzlXOs4OCRQ6oXTdnBofFT9u9HY4cf9Hlbs6lpJAjGdtsALQ5LRnmKFgDppZrSmNQnteICV4xZRVVBpokOkxHHR06m/hOaaRx5jPOCqjO8SOmPgOE4H8ufK1pkhwOWi9zYWetZcLgfiEqgy/lV+cx3HbuudvCNnrNHDn6YQdISOpnPGGp1CYnhbsO5gXqwC6SfF3xx82lAUo/UFFx5xb8HcOnSpWrzbDbDbDbb+Xrf9/jt3/5tfP/3f3+1/QUveAHe97737T3E+9//frzgBS+otn3VV30V3va2t2EYBrSlGvTVr341brrpJnz7t387fu3Xfu1Kz+hRs89sN3ayySabbLLJHsdGOZWfzj8AuPXWW3Hu3Dn+ty+SAwD3338/Qgi4+eabq+0333wz7r333r2/uffee/d+33uP+++/HwDw67/+63jb296Gt771rZ9mjzx6NkWAlA3nA0IXJQKkV7tKWVib0KmXVdzKcFSnOUMFjRAURBVaWjfJ+FQl2uV9yiotOctRHWstDFHiq6RUOMWd09SrZSJ3pG2GiNiogutIVU0dRsQlRWKiWkHq0ILZ7SNd4UGM+ooHJXriyEkwBeJKxjAEZnwUwsnCu+QXFsNhiVwdGgyHpY2lraED4kwtj5SsQ1WdMvoMCUJMSZu8YdI5NxjYAp0xvKkiW7maSsFCowhOUpGU3F3lAy8dpqNVDFtxoq+CI1QEqarE0RDEiGelOi8FsVX5AMQ7oyrD3CCcKBlOG0UJrKmKBDgqSFWDSdoSlTxFbOQeSiVZ2XQBrsCQTRPRNvk9RYI6F+CKHH2IluUliFBw07cIJWIQvRFJBmqHtiojPEcxASAOjGnCl85zKgFXwyT6XDjhepYkyZ6gHAsYq6KL5Vgo48mtLZqzER6ujpXzNkpUJ0SYlqITlpPQk5ELrU+biThLaKyK4qQEp4sjRtGkZAU61zIm+0hJY2uFf0tFlvuj/PlwAOFIO06Ix0UyZVl00+YDa6c5k7At1Z9EBhm9VUUVe8aZbtYoKknjjOaFOEvAvMxnTYR1qqOL0dyUgpG50yaJHtO9GA3iYBHXjz8ixD/+4z/G8fEx/70v+qPNjOR5Uko72z7Z92n7yckJ/sE/+Ad461vfihtvvPGRNv2q2eQAaZtFoI3gyLAm1No3USn2Zo1RVyKZJazdnSV0J2WyX5WJfuVht6rMeCQqmoyRGJ0eXCGpO5ISBCK/NykBpUrBNZRD4RgOg2mFsI8qgWaQqimXoOZXYbul185wGDk2CZ5wHc47AmvoxFYmLcl5EW0rp8Lupk2sS0SlqLG1VUXamPwsQwyQ/Sc6loT+OSfEpdpBIueVyuE3FraUNGtyQHrNAqdlV1upztHOCu/bSH/EEWMxUBw3nf9FfdPS+Sk4QkF/OoehYoreU32jczx0nopmus2vCp4cwE631T4zO8GCl9mgnDyCdyyjTlWfZL2l8gdDb0Y5oRHWiuMDAMu2ZwfIqp0N5SG5ajucbfOioO8b1thLEbUzhPJgo7ExaNhWvEh6cAaYimKAX7WTqR+6ZUxZIlVspeIsRoMwaouuamrW4JL5TuXINWfl/t2oOaL3LM5cMzqX83aOFxAEnafG7iU6TE40+rjEexvUfIKduSe1ju9Lv3QYDvKxhsP8+fa8YUb84VxCOCow98GAg4M86A5m+bVrpBzSBwdX+ouut+uiUBAkxTqtNRA1tKfys6QyUM1bJZcsbS3vl8aj0VAXZL5Is6hI6MnxRD6YXnhebXuUILDj4+PKAbqc3XjjjXDO7UR77rvvvp0oD9ktt9yy9/tN0+CGG27Ahz70IfzRH/0RvuZrvoY/j2WV3DQN/tf/+l/43M/93EdyVo+KTRDYZJNNNtlkkz1G7VpXgXVdh2c84xl4z3veU21/z3veg+c85zl7f3PbbbftfP/d7343nvnMZ6JtW3z+538+fv/3fx8f/OAH+d/Xfu3X4su//MvxwQ9+ELfeeusja+SjZFMESFupJNH6XBIdKKveUQLjPrkESvp1fZJV3SWP5iSHD+yq7GDTSzJz3yMNeVUUe9Kp2A09A4CxFok0loiUruuAWSffZWLGsmI77DCcK6rU5xy2pGFGYeoDSU7FMnBlhuZPoUhQSgax9FEwjhf2vpRYJWvQ6KZzlYnhv4muPhmRQzBRol+sk9VIpERHMlASVnVCa2xTBVnsu++5G5NcW9Za2lg0VMWl4S59bVkTKFaEa2YP1watvIkMEJBomAmApSqbUbSHXnVllpa90BpJ3Ad7rJLwiOr9SHIlQ3QUvROdNs3VklTCq07OZj4YHTEk2HWQtuVqKYqSFuhkbRGWBKM4DEUlflt0ttZNi64QFjqTODpAFpJhTS2/dYibwlU0mB21dwMVMVNPBKka1ORa4ARYo6KEVZSBI18GtvB6pSJ7EyAQsAkGDZP7lTmkrwlQnSI9zNsibIng2j7UUR8aZyoCUennjZe0CQBFg5wQu+Z5rlT4UeRZRZZNVGORoPXGwJfr1R/JHLK9Ln+vP58wXJfb3Z3b4nCRb6DDWY/DLr+fO4n89OX460HCOkyMGS1iuYYxGg4rWiIi7U3FC8UJ/VZHWekiqYixuo5VZaWqHuWAXTTcH4nGA1UbXktq5j8DHqA77rgDL37xi/HMZz4Tt912G97ylrfg7rvvZl6fV77ylfjTP/1TvOMd7wAAvPSlL8Ub3/hG3HHHHXjJS16C97///Xjb296Gn/u5nwMAzOdzfNEXfVF1jPPnzwPAzvZraZMDpMxeatCkRnJ4TgUuoAej8SOIiuZ/VSZMgoTNOsJtiiMxRNGx6giKMjAqPD0OVKe+r6AuEpZE08CQs7Mo5byLGeJBxrP8UQdfwtO+PHzD3MATm7UmDNQsyXtgL+ekOkc7QIErciTEH/lhIYy0gMzVFJr2c4P+qDhwQVeH1bALgIoBO1n1ICcYRzmkcabI9pwR/J4FHBMikcoZBX0Rk++6LmMXZyj/plkltHRtTz3cpkATGw9DWlh0vaytSvoppymsBeKrNJS4Oqw8AFpxGK0DEhX9rbHjLGnW3orBV8GFuppJV+3k/STlIBnOI9F6SyygOiJoZNNwm5pvNbPD2HELM8Mae37p2AHfFFbpTRd5Z0ZBmjQOY7BIJYfHbC0cObKDcly0X8N9nCQ/hIgSFx7trOSpNIEdZXb0gxUB08EydJd6K8eg/K5k1CJK2iLCmgKBmSTwJonn5rYSPGozNAXADgGJPCs6pgUvhlJrEYtYsCwgjLx34sjqar/KIVAOL5eRc/WbYch8WAIDwV3nS/vO9bjhfMaLz83XOC5OT6McVxaMDQ1OtrmU82zbYdsXlu11OdjGsWM5OzWVHtpOH0YZ67ERpvRIla5a0Bn1vULnTQ5z7BSp6WCAcZ4gkK/z+jP70fkt3/IteOCBB/DqV78a99xzD77oi74Id911F5761KcCAO65556KE+hpT3sa7rrrLrz85S/Hm970JjzpSU/CG97whqoE/rFon9lXcbLJJptssskex3alZIb691dit99+O26//fa9n7397W/f2fa85z0Pv/M7v/Mp73/fPq61TQ6QsuXdFrOoYI7taJULFC2m3dWl1vnR0AfrH80sbKHdtz4vUWwfYUsSp+k96y2ZojRsAKCh1Z1DXBS9pVkDvxDoACDNn9JGrcrMWmKmUs5mHh1Kgu5STgIHYJoIVyAw5wQC05FUSkqNNsJSyJkIIFXSp21MJTUB1DdkJk0UaIATdDnCo6rjeoNxBRSSRG0qVfRGfYmbY6rrqXWHgBzx0RpqTYneuRL1cRvP1XR24znR3AxeMA/KoLcGpkAiZuU4GtQUmCe2FrEkrIa5Ff0hsmSEp0Wt1jOXUXmvdeL2vK9kEBgCEBixqmijpNyNSu4Oe2ZOoyoHW1MRHVKbkoZrNdcMRVUaiY5omI7I5Qi6iF3iyFNFFkm7SwKJGK8gJi9fqqIb9EPN01MSl7vFgBuPspbFLQcnOGxy5zA/TWxwacgD+aSf4aGzXIa4Vnw1rPXXC3FfTjAHt5HaRD0UOukv5kzqLFyJBvmFhSvREa36nih655T0iCo4iOq+15xFPHaMisSpeYETiFtJIqbE/DSCIKn6zRwUXbVlj5lKbr5YIjzb0HCy+qZoqPWrDigRHreycOvc7gXxaW0Fes48W5KkD+R5gedfU0O0MiapjxRXmBklUqPAynxqRvh/bOL3zN1W+L/C9vKahY+6PUpJ0JPt2uQAKbMeiHMRPsVRPWGQaQIuMqqE0LCYCTVBHmth8WuELfpDbog7D5xcASUOlFQSKfHNavIrbbX1e/pcnB3AL4sDc1Ae4gsPS3k/NqFpBIuSQrMCN5jEMIS1iasmmNHVAKHkDURv1MNIx6HLNg8WQ9UOJ1W7mmj4gdzoB8Ae+AVnyhFwBruYIqptGrYEsvPTkEbZNsJtKA+DSodVFc4Q2FFFjEAYYXeuQypwRGoaqc5hVl25nqlRgpUKAqtKfjXENS55TwA/m4byD3UujgifCjEl6VW5TayhIjqWM7niELUjY4ic08hDmzo2GbMDrVBbxuKamk04UzNIewEg9qZ6iPG+OL9GHSPVjga916zu5PRX50ifJ8O5KA9uFlg19VOyDw6XNnkHF88W6E/yzWQvNZidlvFLMHkCQ1SaTT7tqzrWq3sFQ8o5uopAUT/0gRq2qs5XnXflABn1npmSCQ6EiMfOIlCcQ0MLoGgqyBHlfSwO4MpbrE5yHxmTkKgqbxDiVFuqsdq1EThrLXAz51f2iefLrBdYxiHfs6maL9nxa0wlwAsUZ47uq07B5OTshdopYqfZqbmLxiaRnmrtvcketzY5QJNNNtlkk032GLU/Kwjss8EmB0iZX+xZpe2LIhTTURde5amkXevBfDjtql7R5y9b6GxGgpUqckQ6nq1HcRxx68ROJTYreQha3eWVaNlHFxnuauZ5ub2YD1xxY5EQy4kT6RwAxBIFiMlgKMRlwTsJH+uE2UI8pgnNKDKRKfkJRjR7E3QrUkUQtKGSdVmqQHHw6C66zF3PSaAKz5Pk1FhJBTAURFUyPsKQbtngay6QphA7UlVe62ptthHCZUKU6GBITKoYyqo6tEb4i/SwURVI2AM1Za0iWSXn80vqvbSZ4dnOcgKuTjR3g8CP+neM9jkryb4KAtYRUq1LJlGXMo4cAIIvG4lqsl6UlpzQsNUeOK++9vI3B5ii+gNyPUJD+mAtTsq5rDYd727YlkT2VQN3Kb9vTw0OC3eP26gCCSIhdEZUzzsFN2tphlYayOdDmnO6Uk/DHwk7JK1jDcGxWnyu1LvME3AHJk9SOBABQ2SR1NTBVrxnBPPRjjQhZ6WtpSLhIvchxQXtOvF7ivRASeDkPqBIJG1TciFRVS42TkHHpn4t3+X5SEX39djjijAj8HxVZdZJn1wT+zNSg/9ssMkBUub6Ar1wkoG6j/WEM5o4qs/H4WiuuAGMIsEDCrxD5eBK9FBYdVOFbSeVeyEikGX/FpUzRlUuVFmTZpHzHazSwZqVypeDWY/GCYyzHWRoMDtreUCkpELDNiL6WigqRSkxt73g+82qvI4Ysp0i5GMSPkklEJhFwysqx8PqsDhDB6m6jvkLKjdKQ2F02iHBDQSBCcRFuVkABMpatABV9TW2ynvJ7VOTVgT7uYaxEQtTPIkUAEPHJYjAakc4sgNivBBecum6IsOLnaoEorD/zKIvJct+buCXtL00BZJ71Z0mFq3NzZRcEz4WOfhBKh5Jz2pMuscPI71wKNCErSgOkojHlodkLRK7m/+VrOS6pKZ+P5aOMlFycBoPHpPpIeorh9SUKkoHdgQox6jt1ZgdQTZjBncgMaxrgzjYRKiZmaTlGrCTp+YYne+k4d4dPVOXdvpF76tyvBOq6jNw1Wb56sbAEMyt2q1zxbRemSO4mOBGO4JwVcUZO+UkINwnBe2pPD925FPluKSR7piJYOZh6yPff9kBlwUT/YZZipXDSOPfH4Id1uQSC5+2KwWrKsdTk69O9vi2yQFSRisVnVcwLvkNKtEWETv5DkavNI1MyqaV71C5eAhys+akxHrWzrIHpvqb2zrGpj0QKUGwUQ8sysWxMj9HGKZ+J/bcECzn9TQucuQnKa4V/dwNRB0fDWKR5WA+Ha+SQHvDE78rmH+z0Xw64sBo7hHOdxolbnJEQZVqS4JiYtoBtw0wJWGZHI26b60w6DbSV/Q+zBv4ZYnqcBm92ZEWAMpDjvKEyFEJSuYkRIkilbyiKodo8IAvMy211Xvefxp81fk7FAjHSwzLPIP313XYXJfPi52ehUQh9BiiB1t7KsnfdpAHT2zAjOCUsD2ORnFEjh9gcf/K/TKL0HHJfm6jPOy0Ezcc5Pf06ucSLdILk1rZW97rh9k4algfH0jlRPl0R7k6dF+Hw92Hc1VOrnOANJ2DOh45GtppqSI8Tap+XzdMb7tMJ7Oobx3RGCdn61yxfSr3NiQl3izjuyqtVw5rVMnV0cl36GT1HFblhSHn/TScPykRzH3Xy+jJzagk/UZe2dlZiEgrvSarznttVB4SeOFCCdnJlW09rplNENjVs8kBmmyyySabbLLHqmkY9Ep/P9lemxwgZSYCsCpMHFMVggfyakVXVYzJwnRoVIeRM0N0fs9VOFsJ+2s9J7LopGJBwz869E+D2w3gtlq1uqNwblw5JkzjA0JWp5suiZjjPMK2pQJKMeQSzp6SbI+wUv1F+QNbJ3JPCrojIdN8eInxWxWKF9Zo6Tfa1mwAs6kjCiYK1JSZbsu+kuXohYly3lItoqqwWsm70dUi4/yuqCJ0Ob9F4DoSl5UVspBgNmeDRIDOCoaw3gDb/OXU90gjIkXTNDCLUlFz/hzScS67Hq5bYDgujMnHRYvpwIg47Kyu9svtlv6yfYYggRziz23FDgEltcWMg2eq8iaL9Zb9UnVcVLkZPjGjMXwUskjaR1OTRYYikEv5SMOhxVDgumEJ+IPyc86vUW3xpirvJ6hGkw9CnUsa3bfjKkld1p9fk0Srgtntl5EJRJ1qqBwCo+Q/sBNlrmAtpV+XXOIQCx8/GonwRL3fstOo7qlgqmgP56ApUlEdyeZ90a4UESKO5F7QOnWasZxzuXTOE3WLpi1QNAxSCWjgS/tyxFigUjIdedI2hrkzeznBYmpseP25nDffC0nGj253dPik13+yx4dNDpAyJXIu2/jBILNBFVZXuSi8D/XbWoSyTvTTYWQNnbGzZQQii6O8Bp0MCABQiYaZC4OgnPJxC+HzMJCS9PISOskj8cuEWPiF4iLAFGeHxR5VmbydeU7eprwh3zlRfg8WgTqlJPW6LqAtDlYIFtvCqZHWDbO/OsoXWsl5hSgTGXEOaT6TcUkwRmF1DX3o3/EErh+CLbBPdJQfJl4m5fwwye/pGjeNFYigsZKL0aqa6PLemEOYwn4cl/mp4Q86+IO8zS8s/ILgLMPvJdFdtbsBJwzrSb3KuVIPvNz+hGZFTnnkUuMqoTnVjk8+WO3s5NfIkKMZAkN6JiSBEgsXUjjoMBzmcxyOnFISL+e6FJgin2M9jnL2ajmH0QNpnAMUnRr/Og9E8WH5RbleXZLE4UY5H3z+hvPdEOVeqpwSNS8YxXMF5AeszinR3Dv0ShIdedc0H+yeVz5+2e/W8IJHzzu8WBhJonDf6QRfBddVOUlABXnq+UoXgOj7h8vrW7WQpN90gJnTmBFRae2wyn4NAhc9qPt+TAeBum8JWneDOOiZB0gWOflVrsH4HHdoB0x5vw+CvEo2QWBXzyYHaLLJJptssskeqxbTp1d2dk1L1h5fNjlAyvoDoGkBV3rFeiMhUE7YS6zfpeEwIc+S/UWVAGmiWnmQBlWbVLhVh45klaOtil5wdRkl8NbH3ql2ukyYmPcdweXsdtBQkYEhCbIStVnMBhzOckhh0QxYFrXHTSh6PsFhKLBTiFLeT2KWFokZY0/7GS6s8jJ/ZcACiNRAE021Qq4S1Mt5SV8AO2KQgFRgJdSJpvu+S1CRBwytOlVIXAuJMuQyqNUmwZvrCNdTJCTKNSUduK7haFaaC/wzHJY+PHboC7lmf5QrVYAcEaEKPx2VkPFpZAVMJctbBXfoKEBpc3ea0KwoYRzQicscoeQSxdHnYyjAGdG8s5YZLWPnWPeLiOrCzDLpaOgk8kn3XHuqWL4bVOzHgFqNjyxZ0bijfqvuJT1mKEoxS0IZ0UYY6ltmMlB9HSUClIIBqGJxIPhVRSG8gp1U9JBNk5YS67WTBttBoknA7vjP10uiPpo0kY5FEb8dTTiGtsqrU9EorZ+ljsWwkYLsNVWBRLGSlJM3CalorhFcbqwqXR8s4jp3AlWKmij9pvUAq/Yz6anMjbGiRqB9SSRd74OitUhGqB3UnK12satjdy2fnFMO0FWzyQHSRg9Fgg68qo5RVUdJ0fNzHgmHg02VX7CPwZfNqAdnr+AwGrAxAboMvrzPWHsNg/i5Yoee78kDGQkCjidSzR5dTTguMfS1mOUn000Hp7hlkYlQbp6fYFFi+0PZgU8Opz4f+KH+AKsi/UHqz9YkNKUBx7MNO0ZtE7AqFU79aX4dmga2VJQ5Z1QVmGorO0hJJi+rnBWCBXSVy1ZBQDr/IMnvyS4n+skq8YNUrNB4aVYR7qzwKm2HHZbv1DqEZYF/Dhr054pI5PW2vAL9dfk34SBm7iYgY650zQrMaHrD5fNuo6pYVB6Mfvjqsua8o6RypwRm1KXn2vmujMqP6fxM/ZDS8hcEOegH1xj2BdRDVj2wTQIiXXPKi3Pg6xTV+/FYBspDWOflqHwbIENO9HB2bZD8MYWt8QPb2+z4ADDGIHEjym+iQHM5n8foj+vjNvV8kn8P5qrJDs6e7bqcXZmmh6C/q4oyDQOO85xs/Z7zdnifMrbG+wWyMym8Y8rpaRKzSVPuYFLwpa6wq/J21DXkRYiSeRF6DFT7oi7hdYvblePR51VVEKJezGo+snH7Jnv82+QATTbZZJNNNtlj1Aw+zRygR60ln3k2OUDKDv8kYh4CmlVZCW4Ci19WPBOlakjzxkQScFQr3TAzCCwkuJ+Ej8z6BFdgiGaVQwt21XMSKZxjEr44axgqGY5IXBPwxOw7B4ZSMRMLEWJsk0Q1TOLKEQrlV4yzBpJoarIgKiAQVmcDFiX8YRFZMPLM52XWaehw3/oIAHAyzHDW52jOugghGgMmVTQmscaY9xZ+UzqMhEQHI8nGqqqOzChewGTBnCnQyaMKgtCVL3bPKppXnXs4VzTk4wZUmlrEZEvEgLYPDBul1iHOhKgQAPxSoj7bcxb9cd4vvfrDhLgsY6+VJXLyhhl6Obl2EN4lt1aRH1oCq0ogDYHRCro/kpNNTmDVqJiYo14BK2hkJwKhomhuqI87JuerOJ6citTrSM6eCKpOFiY4MLOyJ/6dVCeoPiAYRCe6EseVT0glUhhaC9NR4n/5ngVDxCkYib4FU2vcobSDDhUNMMYgDJCosquRkAVFd+wgScGa7VrDspootIpajh+UCurSkQ6d5F8RSCposOpP5Cg4ETjujdo0Se4/lziKVhE0ajLVIBMi9wfxf6kqNA3dSaI5QAWqOiWg4l7jdu121dh4mKnxa3s1DerjDgmhv9yeroJNTNBXzR7zDtCrXvUq/MiP/Ei17eabb8a9994LAEgp4Ud+5Efwlre8BQ899BCe9axn4U1vehO+8Au/8BEf6/yHL6HRXaLI8oj1N7aKat0mxBEDcHSqqqiRh4mGBpi5WEsUbCPaS/npbC8VxsDTMyHIa1uY40Nug7BC03FrlXCuIpmr0vZSuWVUOS1JL6RoJKwfZEIy0aBf54MU9n+kZHA2ZKdm0YpHctIXB2g7w9kqvx82DTsz0A8grzB5yp0IQKdUtIE6r6EKu6uqjX2WnEzG/EDvDFyn8iXGpd/qYaEdQsmpER/SWtlv6NR3E+W/CJFi7AyXdlPOi58brkDyC8XKzBBekjyTmCBPVJHIoLyUlKwK1RvOf4n8kMde8UYhy5QxWzETj0go83mBOyHMFJTUEEQsuRtuY6TiTDlD+wj/dAWSdrpEqDMJTQNBM02UykQXRfnAJn7QkqMdB4s0lJMZDNBTw2Wccy6PgrACKderfZrBcrWV8ZJztQ/6yxvkHAHpU/69cmSpr3j8DzWUqYkIgVJOHnZvgoqQkBjoW1Pn64ypADoFLyajVODVeejrZffcfLq6lKaGwdT7AIBgBJoOu31YCb/qnCp1z2qqgkrl/uFCHgY7jjhQ3+Pcx2uBtLuTAtOfBLQXtvBh8zAHmezxYo8LJPMLv/ALcc899/C/3//93+fPXvva1+LHf/zH8cY3vhG/+Zu/iVtuuQVf+ZVfiZOTk4fZ42STTTbZZJM99o3K4D+df5Ptt8d8BAgAmqbBLbfcsrM9pYTXv/71+MEf/EF84zd+IwDgp3/6p3HzzTfjZ3/2Z/Ed3/Edj+g4w3VzJDvjxOOKLI8qUIyqvLJSoRQodKtlKEZJxxTtaQg6WUV0l/Ky0609L9nTIkdX0vECcVa4dZYN/EERbjyyXCFEBHh+KZwmfpkQDgucdpSXM8tFj0WXl2TORk7oXBfunvWm40hPio75eOwgK7VUlGIv2UNcKJGl2CUkWpnTuXoLQ7CVF24SoyISFdeGl/ecZLlnxafD20zPYRUcsDGS2BgMIiUOUyTIJb6OeyNHCq3QVSjE8aM1mrwReCQ2Zic50raOvxtmBgNx92ielCpSSN9Vkbs58S5JVZIxEtGOJXoXXeLKqzCXiAL1odtKdNCtdxM4k0ElW8AJn6pSrkom5khNFJ4cShoeDCzdP0GS1mMCoKAr7gOqFGqFeyeVqI/pooj1zjzmZfzOSwWhUwnKMRke00Nw8AXDJNHebdPAOxoHliFOkm8xQarmYCBRSwX3SYWgilgo3pmKC0xDJyMYJQvOKoiLfqfuAx0tpmR7t9WitjqKTBw3u6GP2BiGX0Nn4OeG33M0qNxzcSvXuXH1nFf1BW1TRRlAfX9UZI/a1DYt+KorLfN51ZxAVvVN1SagJGyXc+ikMEQS7FMN1+3hD9L3elrrY9A8ZstrgkkdvK/nvKtqOj3hSn8/2V57XDhAH/nIR/CkJz0Js9kMz3rWs3DnnXficz7nc/DRj34U9957L17wghfwd2ezGZ73vOfhfe9732UdoO12i+1WMIFLly4BAE7/3Ayundc3rs6LKX9rwU6nnBkgT0haGZs0okyIqlKG4CuLUCan/niuStrpc8M3c5iJwnSYK8dnQQ/MhLggcsKA2Tzf0UQ42LVCWJiSwVCeANu+VCJtGqCUorq1UwKmwhzM+TMDWGMsQyYkKDnqKyDnXoxyBUyqYS12YOLuRLfDlEvHIDPg3IzsWIkjaslBYEhH5ShEww9vJZJWVdmMydmsl7bEBtXYCDt5IPLezxTcRSzGCmoKMyVaWxxXO/doOqId6NEVodoIwzptJFIbgoUvE7TfNogF3iEnFpB+0Q+mfWy2Ok8kzBQERVDqLHJihLFRYCH6vbGIJfkjmYhIhJU2MSxL1UG2C3DlHJ1LLMbblOSOxkU4Q/ln8vmi0C50NvCYjskwDYOzEUPpm8aKE7kpasQDGslB25S+2ho05cFn+z05J6neVkG0/CCXiaP6rtK3yr9RQqJD4jxDy2SSQUg0jVEP7MsE7BkWtqJLRtBdzPB63r/k08WmhsYAcsrFCRw79bEFKodQOTvj31TN2zefXuYc+M/Rwoh+xwugTkgy83yYvxAWEamMVaPyGMULBVekcZOiQaK5YuUUjYN8h/plWDaw3iH019ABmuyq2WMeAnvWs56Fd7zjHXjXu96Ft771rbj33nvxnOc8Bw888ADnAd18883Vb3SO0D57zWteg3PnzvG/W2+99aqew2STTTbZZJNdiZmUPu1/k+23x3wE6Ku/+qv5/Rd/8Rfjtttuw+d+7ufip3/6p/HsZz8bAGBGod+U0s42ba985Stxxx138N+XLl3CrbfeivUNBk1bh7fHvDN59VbeDwndSV6mNCc5jmzXA8ymLA9DyP+AjNUU6YN4mHGQ5GaiQ6XUrln2YCaEbnk1nt/HVpO2yara0mq6C7zKoZXuettio7qEFN6HkhiavM24DoCxZhLBNqL8rWQ9tlBREQ0Dlh3sWRVWRHEqOdbEVFe3qDYAOdFbK0jT76FWoARtJAVFCl+I0larDiDnV638dRKxajv/hlbGBkh8neRkOZLSqetIUbyFklvoIswyn/jyIA+u48UWNyxy6O362YojHX102JTSwksl6fxkO8fppsCm0cJTYjtz3Bh4SmoPqCv/Rn0Q28QQVFxEYJZPfnaYx/es9WhLMn1KhuVPeDz1DUfkYrB8EGMTHCcsl6ipC3DED2Mjv6fkbqvCBcYk7gMy/XeE4WrEmAwCVRaqSKfvy/23aWDO8qBqL+XP25NMvAiMxpSCtVj2Q2vt+ST3AkV4+iiR3yj8XpJwnRSOqSJHe8hQtZl91UD691svn1MUwxmO1sbOcRVimFukEqkze54CUUUCK0iUAkRBoDvui1D3F7dbMRFXRIaa42zE21QTRKKOAmMUBV8mrnbFwqMpY5ai38Ykjio6F3fG0dmmw/aMcDNXHSuOYNscdTII24fLtH6UTc1FV/z7yfbaY94BGtvBwQG++Iu/GB/5yEfw9V//9QCAe++9F0984hP5O/fdd99OVEjbbDbDbLbLjNUf5ZuNieQGwChnB8h4tGb7tdsSvt7kWcBsBmBQTG5FgDTNOsSjfEx/VEj+Dhx6JWjZE9tvKWH3iwxtASXfgqpfWoEhqAqmaQPrc7VN2Psw0XMqTQIE3WwWLTZFe2pYtYgFPkmnFk2ZEVIpfDAe6C6V/hhkMuFQua5IsyonSmHvOsdBw15jwkDoidKo/JNKmFRNsA8Tbjexdqa48or2r1m83e6kvFMVNcpDoPaOTU/w7GwNgC1fjtYgFQeCKAGMSQx7zZxHVzppkQyTSGrIR0j6jFTiUO6TjXVZuK8baYJUlsGCSRftzKMtUOqsLaSONrFj0iuugKghwPLWdQGWxqFNwgRetukcHmNQsy2jQGDlO42J1ffz8R07PT5abArh5tY3OCsO4bApDtqqhV3n9jYry3CXK6/NRldepV1H2ydhVA6Si6MFYdlBGkkPCIuxvg+ok2Qsk6OSGoPYEIyoIMuQYAdxrABklnES2o0KZqeFl2ZADio/bEjsiBLBZD4YfVmaaEaLwPxdWRwKHJgqx5CFnpUTWBFbchWaMIIXJg2EmanvOw09Q35L7aKcw2QcAmun5WN2nayqQrDYlLywoTjEYeOEdiMYWVxawzCbzl/Mv8FknwH2mIfAxrbdbvHhD38YT3ziE/G0pz0Nt9xyC97znvfw533f473vfS+e85zn/Bm2crLJJptsssk+fZsgsKtnj/kI0Pd+7/fia77ma/CUpzwF9913H370R38Uly5dwrd927fBGIOXvexluPPOO/H0pz8dT3/603HnnXdiuVziW7/1Wx/xsSgBVKsncyUCyQdsE1dx6dUfRTnQufwPQOoaxFLRNRyqKq7DEp5fGCYs9EuJ/HAl0CLCHOYGHBxtcdNhjtHfsFjhuM1LVyIknFmP1lCirOUkZ7JWrZ6HaLEtS6mLQ17i3L85wMdPMnnhsGrhzvLvu4cMZhfz7xb3FyLEix7taV5V5eiGrFyBXD0nUgOmep9/o1elajXt0y6nif6zigaVbToS5BPcVogrTaAEdEk+pzaEmePrMRwUWKAVhWg4OTYHJjQ8ZyA8KWY3wgMoDSbNOVR2H2aGIc0wd/BlZV74FPEgwDDO6dCx3hoAjnqshyIxEhxDUClaWeVzNdgIIqaoDMN5pmp3KseNfYdtX6qobF6aGxd5ZW2s7IQ4pFIyEnW0iaUPrEkc+ZHoo4zREIEBalkPYLBOjmUE3aFoV1LYaogCewVv4UvkB9tSzbixTBZZkWkS3HEZOYTqNY36DWV8MycV9XdiWCnDXeX9XtgLO2ZCgvM0jnW0KQI0ljniaJirLDVWRXCkoIKiSbGV8Z8aifZUCvGlQXYAV5Xpturv7oP+BE6u4bzIidZ0/6nKyLkq8KDAvIUq9ZRd6UirrnJkcs/gchQUwFCKAbzrpFBisNJGuj9k9zlZn47RybXTvE9IQBpziF1NS9g7Th7R7yfba495B+hP/uRP8KIXvQj3338/brrpJjz72c/GBz7wATz1qU8FALziFa/Aer3G7bffzkSI7373u3F0dPSIj3X0x8AsCquv66PkAJXQsw1J3vvED1kSuYyLDv6gsDQfOPhlqfI6MIxZD6Vp2ekp5eQHAW6Rn7DdrOSDzHucm2dH50kHl/A5y/sBAJ83/zj+fJvfH9kciz0wnkO/AQZDuYs3ZXY+iXPc68/nPu2vx/97mmkFPnrxBgDAxz9xDPOJPPssP2Ewfyi3cXYxortYqnPOKN9py5NTmjdMBinVP7uwV2VJ9JFgsRc2MmrSpwfAPkbT5OofGy4TDhCFQ/CxiE0bkJwlS1QH1iBpZ4naTo6PAdJAxwWil/d8/D3l/a4HdMUYkB1qqvDLJb9lsi5Vedu1w30H+QsXD+asw6bzF6garB8aDNt8nVMvJH1uRaW76qGfBIrhNqcaBtRVYIkfnsXR6RIi5fLMAmzZTnkXxghh4NiYlJCgP+XApGSEvJCcOTXx20YRHe6przY28TByTYQrOUtxQdCigS/OXOotTIE8hEHboCGIdzBcei7wj6k04wTuqvOB8isqiIzg82ocEqysnPrqPrC7fTjWA8zbFGyVRhAwaqh2nwCyfnW9lNwnazhHTQgq5Z6wfpSjA8B0rlrshFZ+R2NdaxNW71XuD7Vbw5CaiZnazIvPIA5KHlKuPl8DGUeXI0Cle1jtVztZfC+X8vzQ49rZxAR91ewx7wD9/M///MN+bozBq171KrzqVa+6Ng2abLLJJptssske9/aYd4CupR3c69EakbowPkmoWUFdaVb4V+ZqRUb6X52FX5YQ7IHBQJUKB8BwWEL/JeqDg4BuKUSFx/O8BD1qSyVQt8F13QoAcEN7husLIU9rAkd7yHrYakVNkZ9PhGMAwP/ZPgEfWT8BAPCRSzfhjx+8DgCwfijHnpsLDWYP5d93J0CzKlGwjQrB00J21vIKNcxUZclMQVxVXDm/8IoTKPIOqGAtNLJYYa6OJMmQJhkYIiCjFV0fLl/lQLwpvJTUsXQ5r6bwpBhV5sJRp1H7BTZK6r36qiLBjBoiG63yNdW/1ihrVmU8XXCIHY2zDhdLMnxqkggUEeQySHJruzUC12oJBYoARaUCT4mlo4RvilQCgB9H8JL8LkWJ2jAfkBOoS687Q7AKuiqHN6g4WQgu0xEeo2Az2k5VaJ0LUt2jIN4QLUfHhhIqidFgW6owfdsglD4MVKTQWL6H3RZI1IeUJK0hn+0owXc0DjIRIkVPDHN9ad4dglorvTWKQowIKHVFJfeLikxoTiImDlURR83no8cc8RI5lcTMlpJwYnHkTT6OjcBZVL0adbWWSlzWUUWOylh1vkbNDRRtVUnWWg9NiidUH+iqVbv7fgxt7vSRfq+iRblaVaJ6QNb9s0OCH64dBvbpsjlPTNCXt8kBUmb7CNMmhHmBI2ZKY2kPKVYO45IjkD8LI4KuwESFEZiXMvXy2s0GHM7zTHvQ9ZgVhttGVb7Myp03twNsudNXscMnfHZs7sV5blcoM84qzrCKuUEPlsSiu9fX4/+u8m8+cXbIFRCcH5LUhDGasAQeoYe7VaW18uDQDNnaOB9AVcvw5FYd1yDQMbhaJaq8g4hk6YIQBGFUeFwl/PnEEJg4TfK5XVu4UukWCb6cORG6VdebKwC3AYZI63pJCIpdg1B0vxj+PHRVvoMwKZfXEcJBk3lD2llqfs1Cugqi4jwP2dc+FmLed5LfGCul+JoyYG+lW5UPpxocHO+M0K5ITlkXYQgia2NVBWbLuKY9WRvZgamcHtpnNNV2+i7ls82bge+ZmAzWpQps1bdYlyqwflXYzb0V1mdv0FA+UClnbjaAW5VTOAXasgBgcVufqgctzwFO8sa0NlwoBJB+IfNBVU7OFVCpYtkGgNQKuzqT+QHAYKWS7YQq2iC0A9CUD9gxkyBwrlfXVlc40eGUMyNjK3FeUOw0NFbaf5k8Kk2GWomKamdHl9KjOD1KzNeOHJFMRSAwZL0IoddyPVo9pgXG0yX5PMyizFdWsXzrfCe7jUjDNawtnyCwq2aPuyqwySabbLLJJptssk/XpgiQsvVNLcyyrVZvWruJXvWKjcnsOGKSqpUcazi1Abas6oiXom0Ch+gvbedwPYXz828utTOsSvbgSTfHosS6Gxu44osiRDEZTpA9DTOpFCq/92pp1rkAV0jCwowgACerUivh7dgZqTAimvyQVKhblpq8Ygup1kFTsBGAmu8nqVC2DptTf3aWE5uTc8InwsnKSVU1GamSAWAo+ZSOu/UASaD4INEgHeOn900DlCgXE1jOWoTC4bS9ac4cTv2h5VW+Xg1XK2DqAgVtaOkQjnKRQkuvKg836pz0Slev8jW6x+cjx9TRPR7LzLlSJ6RyZZfBzhLJDkYdV0GddKytZU6hYMFQU3IJpox/1xIhYkQsUbqu9Rwt4r2bxLCZMQmbkiB+RpxJacnQWxgsQon2NJcc2pO8/aDAgG4r/F42CNRCkQXXK7hjqKM9ACmpU4THCG/NUpLZdVJwBf+MVNeTAROYxnZXIsQ0EjlL0WSSUuRKtua0RH6UPE0dLaQ38sKXS0EpySouLSK+bGQQJVtHtIAi2ULRQ6W5xREVdS9bL22wKrHYqEiPVrYX3S+5v/dBzNo4VUFFPSt+IZYDEcgxtHXkZ+f3UfM6ybHoGmb40sEPdcXi1TSd8H2lv59sv00OkLIH/jJgFgDfDU45OOTUNKnKT0njm1TFU40F52ukaBEKWde6VKOsk5H8FR2HpQe6S7i35AgdzrcMAehKoLkqjybozKvyYnKwnIlYD3nGCqrihh92TeJJ3S6YW62qGOIHiAqf6xJNTWK4JwIvvwmpmrz4gZogjiTDaUAqk1eeqEy9rxHJGhHFWb2dTiYEgMqLhwEgaI5zkBqgEGSmwzn8dTkZZntdfnJtrnPoj9SDrzwMKlhKn6ualC8rKFm2cRlvRSBZmtebipGcITmVu1HnQdCB5TjMiu3EuSUHK6iHWZypB6FqV5VTsjc/hZwm5USqp29S9wINz9Am9MUZ2rSRc4A4V8hbJqjLD/+S51QYm2crleekSPjckNhpZjP7YRC6BqFTUK+1FZTD56rEa/fml4yIAfm3dFxyPBsgaU2uQnuRtgS/StttApP8NWsjOUmkWzbUDhCZHls616aCtslRoPytUS4NjwnKXZorJvO5WugxVCYCwtga5t5Er5ydDbU/Mczo+sj5SDpBLKm5QMgi9cqqvKoFVSWSWr7r5yICGztwddo+0d+q+lOPX8UqnxwQNxb4/+Da2ASBXTWbILDJJptssskmm+yzzqYIkLL0hA1Sp4lpEsahjAyzlI3eSIRE0cnr6Ah9bgZIVYVKWNUrUV5RqQTJdeGH2W5ExMq6WEEDACoCOmNkVbdXnlmfD63KFwGBw9RWVrO98J/IjwwqUrhPssDgsDttaIyCCFSCsEq43ltFllTYmzl2IhxXhgQ4kiTpPQzJAdCJWQssidt+iTQr3Dmz3Lf+sMNwlLdtzzn0x7kRRFYZ5jUUSoSVqU0iJUGmkrNN0O/VNlXFoqtz5FxlG0V9tD6d5p3hrtIRGoIxnaxg98FaRsERFb+LjpqohNcqYdrWY5bant/Ux6H7Ro8tCflBAq+F08V6VBVtDSUpn+QvdqcRzUqIL/m8Z1YVKhToQyWih1akWsbJ6QBBtJB20bmoCqS0BwHRkOdYQ1D3R3SjzynipSIeVYSJ4RmJLjk1Hqr5ZAwhO1F713CcvmeryA+1UV3nqCJBUY+N8bFsgtXnotpNkR9OLl9FIVDUcjqlCCE6VVSh5giyPOblWHEu8CRFe6iaUUeu/By1vBCA1CVO3IeVtAWSGaqsRP3j6hpqYXwKc+wn/f1ke21ygJTFYGEGC9NLtQhVvxDRlh0eHlOtKgqA6sE31tSpCL6MqizhCccgnHZlm+w0Wv3gUXCHLo+mzVRNFQHs0Wsip8lFUzHlcpmvzjmh0vcuFXxv9JAkaKMzdc7UuJJO56SM/c1xrsAgD+dmk9CW3Ie2VOc0JwOzPxsfBbsDmPQwzotQaGtVabnDcFhYjs8VR+ewZqQdCyEmlwTG6KLkbLQJZqRTlaJB9DKOMGJlrsaQItOjawD1sKv6JdbvgZJP1Ei4P4xYdcNMVQdZAKPfV1U06npWsA/lrDTKUW+SOFOXS07Si4Ew6oOenKDaCTO6rFsxsRMDOzl+yQJ+6fiQXJa9NCworHOyyEzEDiSpiSurnBr9PQWNaGhwp5rKq6qlQSrJ6NUEYTw3Q6zLz8t5RRq7rWGaidgYjIk/q98Zdb2oWkvfXwZ7H4Z6ASL6XJIjFhV0B7u7g2rBt6c83+mS9YqVvewX2mkvlBqdctqFnULNG8LAHWZgupGwADxV3s5knFKelZkFuCIaPS95kF3rsexyY7vGwxmBYlclbWBb9MP6oUHfNxV9w9W2T1fOYpLCuLxNENhkk0022WSTTfZZZ1MESNnsozN0qa3gAK4Y4BWKSqJz2OV10VwYKpEQwC4Molb+muyLQv36uHlpXt4lcKVPRSymIhaiei4/37dqlaVVvYinKAKM8JxIWxWkozR/eHWqV5p6taxyYyvjJGoVCaGkybWEzduzyKtotyl8PL4+fmpzJ8TOIiwKN0+RQ/ALg6FIkwxHqHTYgLxiTEz/n4CyagQnwkuirrPgqI91CU1RbmeumibAkeZVEp2qbeFf6vsGochXYO2AIlvBgQpTjznqPBPAch0EbYyhj52EbDXOquuhj6V/r2Avjvzw2EpIM4p8RRjCH/dAlklrjEWDVODcRBFWpVFmB0my50TZTa4QAnJ1kI54AUB/ZJU6uIo6dnJYiuY0K3XfpVTdC3mbNF9H1JKC+yrTETNCT2iTqYnzxlCP2waR0xmCqLknSSS3TLbayL2mokFBcUxxpWpjdqKxVQWgRhzHUVjUEFlydWRIzrvMBT1gtvUNreFZrX8XnSoYKOSkNiR+r/mDohp7un07cFyXE7GBMibnpZNmEbZEdpyTga75qIg0c16qcTsXsGhF22JT+KTOth3OVrlz/WneZlcOzZkFNtcwdjAlQV81mxwgZcd3JzRNUvkp4FA6TTK67DV2qHBusqQ1dHRuCOUDsXaQ4QdHrvYo77k8VGH9I7IvIQfcfUAkBadRZVfo5H1FYqYneI2QjfN+lOUHqso/of7icnXAcAmqOpZC6Bh6UDCg7Wu4K5+XwAlJlefT7BhbqdgJC4ehsHD7heQCSOVKTWsQNLtyeSVYy3aB9a1oIrUK5tKsuNYkLEoI/WiWn97LZmABU2siYmnkqkyuF/s5LqxyPtJZN0eweftgCNKRAyQLOOIe9GCSSLdV14DaEhLnnUVKgQojB34MbXR1H+jKR4JYDc33LlX9II3c3ZR/Vz6wSb5C4yDYijRzPOb8QliGszYVfVFeK7oFNY66UinWrAU2k4WHvpEoV0jGVrJmB/6MemGjnFPtODHkY+vFDkOZDPWm7PgAML0Ha9YRK3XrEEt+ml82GI4K/HKkxrQqva80rx4m5W/H6akc7BrejG2qoGkAVZWXXgQxpKrysFIrzmNIwLAPwtpDMsul86G+ttwuEoruFFlkm2ALO3jTBb5faTESkxEdumQwDLIIAXLqA8HV8BYgsskzi7ZUHi5P8sfNBn8GWmDAZdnuP9XfT7bXJgdosskmm2yyyR6jNuUAXT2bHCBl23MGvpOoTLQqesDyFonDsDBpp7LFRMORHrcFL2PG4WGgDvs3WyG8a8uqVRPgJWcU8ZgRuvZBXpt1gYW2IWtkATCkWWMMJwX7wxbDQXlfICE/NxxW15lhOXlbRaFQzqOC4+jL1L7RtoeDZFAnUTLhGEWrnILFGiOVV0USI8xy8jJAiZPUX9hVoO6SqJorTS1ezTdRIhYGrEpOltSp6CiIayKH1Snqc/1sheMiL37QbPm764Kf3rs5x2SV26FB4KTXAnHMqfMyAkd93wy71V8mSPSP4EpArkFsAH+Y3w8HCeGorJwPSuLncsC8RLAaVy81SVNrW0gIh75BIFX1QVVB0jFdgimrddsEqaixCakkn/K5zi1CifAMwQAFIpNkYhUdithJILdDXSVGCfLNKnHklPhltK6T5nohODvMJHF6WKr7fV+kBfK+kkugy5xKdBdA6gUaZn6lRYM4I0xtxvskrpvYWfRH+QD9sWWolokzIVGlCqbfo7lVcdxgdK8xPCkf6sjWzmPT6CKAxIWv1VeC2dmWXFJzw54kal0xSaGiUFefJpbsodckEeWUEAluDmknYmsU9ud7B78t42+TH392beEKnGcHOa7tZX4mI+02UoOZ7PFtkwOkLHSAUZNMVQmhc314wjBVCDx/QZUpD8rZ6VFp2wDIDKkqB4j2Jaylsv/YyPbkwBMBTYTNNnEIvz01TARnaJoKCabMMraPcKNqEhMsHOU2fRJ428Qasx+TyiUjE5rdpurBANROU2xHFWOc01F+owRnk8rNiPseTBptbJOUuy6KM9hGOIK4nHJ2qnyO8pDdI8jpbGSWbv2dECy2Jjd8E+SWWhQ87/rmjHXc7kvHADIzty+zaIridHO+0WWwjNhKf1DFWiaKA/cLE9cRg+8iISzLBTn0mB/kdp0/yGx6NyzOcNxlZ62zgdvqo8OlIR/k42dHAICHsEAYNOZS2kCQptsPR7RNEK07JWDKuVG+wXqbG96X17BxQHlYmUFoA0i/y0Qwc3azEmfH+rS3ipFywcLMMLRGDN7DoSqVPohIVHVZxkuKcromGKnqU5Wiov0mDlR/aGCi8spR3/euF4dWYGt1DVs5ru2V06tymKK6b+PowVxV1+mqU+zC4GFu+L4KM8OVp7HkfMVWl4tD7hvK6dpYaJoHJseMhiEsjWuxQ+sBp6oBads+RmaU/MjkDFLjuP2R2NrbhE1Z5KzLtUOb6oFQyDXpmHZt9/aLZk3nsVdYxMMnmSMfVUv4NHOAHrWWfMbZ5AApC3MAKmlZJ/NKYrQGplElNAMUMSnv1edVuayeC8oVGGbAMHroVaswox76avKhiT5sDCcVat4IW5ImU2vhFyXqM9cCpvl7mismyyGoNo5Mr3p19IEjXFExPUfszb0QSn5xgIalwbZw71DEInPvXCanCsjRNrW6pARdLAJm5UF/uBDB2UWJ0DQmIkLyAoD8ENaSIcS1RA9pYxK/t0YSn2fOY+4koZLsQqnNfXA4EKFanz3qC9s5hrIv50RAVCeGVjkatDBWUTLhvQH8Uh5WIqlQHIIuoCvRl1nrOV+JEj8jDDZBPP+5YhzvykU96HIfDtHyKO2bBpGiNuQcKB4VQJJP2yZInlSb97VsBmY176PDpT73zck2eyKnmw7bImoa1g1wWh5cxQFqT6RgIKt0l2MOyhlXuXCUHzYcAJ4S4CnScxD5gZ+aBEN9SInuNu1wb+U/5C1deR+EaT0NVqJkRKmxtVz+bwfIYkZTPxTHrjvNES1AmL8BcXTCTHEG6Tw+lb+jFwhVYnG55MyXM4MsGmYRoGT3MnaaNvB1DoPN1wSAKzkzVjlApaP4uHS+ZJpqQ0dauA/UgjCOkrO5/WXusuq8k9HCwepe0od/OIdAz9MjegigHLMRKrhrYlMS9FWzqQx+sskmm2yyySar7M1vfjOe9rSnYT6f4xnPeAZ+7dd+7WG//973vhfPeMYzMJ/P8Tmf8zn4qZ/6qerzt771rXjuc5+L6667Dtdddx2e//zn4zd+4zeu5il8UpsiQMr8YUJqdG5F/R5AVa6uSfooupG1kFDeY3dFBlS5GRTV8QsVblXRHaNWtZXG0DjqEgBPFV83OKxvKPCKRitUBIqjMWnP5yrcv0NaWA7tVOSL+4OFSnVErNb9ovOnaFPogOGg5DscAsNx6Y+DAi/NlVhko6ILFKVoIldrzbuBCc0Oui2esMilQOfaDPUs3MCl6UO02JYlJL32sYEvS+uYDPryvleCtbacuDEJjZEqk7NCmLYprNOnQ4dtgcPWfcuabBTxcCrmbkxi1tlQKlvi3MCXaJCZYe+qlUnv5hEoMJ+bidDtYpYH56L1rBk3a3ylJUftp+q0DOHlsEgfHJcEr4fyed9wxMzYxAK/+yyp6hsfLDZlH/T7PjqOMEUY3h5U5C0QvHLm0F3I72cP5v13lxLny9k+8b2Qdawk8gPU5JxVtSON095UlX2J1oYcGIuwjdxATSuhDq42UvApVRp55yR/pZAcRBMZLjNRoiO2VBw1a2B2oUTOToU0UZM9Qmvl7SGu5Ht2jiofr9bKKr+j+6sFIsFGRvJu0pZyvqxAxGuHZp3fE5SEWOcYabiOI0N6uOi2MIFj2aAIO2sdNznXuAeyr+ZJDblfptQfGEW5VR8lBclrCC1X2V7DqIqej6/094/Q3vnOd+JlL3sZ3vzmN+Ov//W/jn//7/89vvqrvxr/83/+TzzlKU/Z+f5HP/pRvPCFL8RLXvIS/MzP/Ax+/dd/HbfffjtuuukmfNM3fRMA4Fd/9Vfxohe9CM95znMwn8/x2te+Fi94wQvwoQ99CE9+8pM/jRO8cjMpTfGxS5cu4dy5c/i8778TTTuvRA2NCskCBf4ZcYgAUDBMnRNT8WqMS2t1SXIniYLCh2KqMDHnBSi2U3IOdJjZbQ3nRjhVWq+duYdjsx6X3O8Lqz+cM2ViNV+ofCHJ5eH8lK5ONPeHxRE4LGH3pUdb+DrmswHzkkdC8M288fwQ7WzAvOAEM+dxVJKQbyzqmYdO6OuH5LAqnc+OUGo55+XisMClIX/+wCrjJeu+RV8YYWOwnCSdosBl2vgBMmIXZ6Mx49J+aE87p/xgUKXpxfnoFgOWi5LXM1/jHJfi522NiWjI0UiWHQ2CAH10nLvUR4eNL47b0GFNJcPlNSih3RjEaQie+sJUY8coYV9yGsgB01BSSgah9Ge/LgN97eAK7NVeMugu5s2zi/l33aWIZlUcYS/3T3SGIV5yrjNDtsrRURQAAOVWqffkFLBzkSQ/q4sw5VyMTWjnXs69bOuLMn3yVvKYKM8lgZ/0dhD276aU7rcrgb2aTaocCYasifV6YWrJB4LJtXCrpuXgxYz0l3CYJUAVAVRq6UCGh8o4tltTseMDowWjV/OYYoJmMyP4fbQUrxjPPXYEX3cY6PXcNDbt1Ni6b+hzfVyyqGBoDSOaCITtBn/4//wALl68iOPj4z0H/fSNnktf8UWvQONmn/wHlzEftvj//sFrH1Fbn/WsZ+Gv/tW/ip/8yZ/kbV/wBV+Ar//6r8drXvOane//i3/xL/Bf/st/wYc//GHe9tKXvhS/93u/h/e///17jxFCwHXXXYc3vvGN+If/8B8+wrN6dGyCwCabbLLJJpvsM9wuXbpU/dtut3u/1/c9fvu3fxsveMELqu0veMEL8L73vW/vb97//vfvfP+rvuqr8Fu/9VsYhmHvb1arFYZhwPXXX38FZ/Po2ASBKWtPAHOgNiTIqpKgKq2P5HZXE0kTKTbg1XpyiZmFk1plEaRjXKwSXQHAR8srmhRM9V2CHmg1HQZV3nnm0JiRb6vOxRoo8dbSFL3iUlVeOtmQk3KtCgoFiOCrJoPk6EYdogeklBTI/Uql7WERgWVuWLssUNZyy7CWs5FhJ0o2njvP2+ZuwIySka1nuEubK/HgCMsRkIfKEvrjm2PcS9VOZwusz/KqK62kXJYrkLypYMBxVJCqRaiPGJ6hhqgk1DCT8VWx2zI0EWFaufbdLO/4XKniun65wo3zXAN+fbvCcZO3L53U8K5KaOAT/RGX4vcq6kMw38Y3DNcFVeLeKHI5Li82wFAiYkTg6AcnpHIJSFQuM4CTZslSMFI27Q1cYdftCrTSrHRpexY/ze9LFdkmCg2Ej5L8OpdSKIZdNVN0swupwNTQMzH30fesM3zfxrlh0c60CPDUBkv34ujeo4hXiZ41J4aTt91G0Tww3J2qhOeKhXoUaKyirSpBXsaWGkedzDFjzcL8IyPZvQmlfFzGtu2VXqCXCjwKrOrE5Wo+UbQZBOfVbPR72oJdaEpv08LJVVRHRfJkRyM4bnSsZNQ5eojwsIKeiCKEqizDft/h6tijlAR96623Vpt/+Id/GK961at2vn7//fcjhICbb7652n7zzTfj3nvv3XuIe++9d+/3vfe4//778cQnPnHnN9///d+PJz/5yXj+85//SM7mUbXJAVLmD2oHyCRU4oBAhqqYNbdLSI2wkQKF+4S4NCBcFNYmniC7VgguaNu88VxVRE7NZmixLvwr600rvDQJ/JDh/IJ1g/Zibmx3CWjowUGT05BGmPzuDSUK2jK5ZFkAamw5vEHFZaSr3oB6QoKRPtSOEJfedklECzvJKXGNPGTJQrTwZSdUrdUHx87Qyrf8vrMBJ+WEP2EOeR/0u5Xv8NAm57o8uMoO0MmlBdJJ7u/mxGJ2ViDF0oeul4kyiW9aSY9UoXgjn+uyfd625yEVC6W/nXt0BVppmoCWSstdZBjwsFRmHbdbdOVcL/k5LpX6eIK6Tv0MF0pl1aXtnLl9ooKzqrL/8r5xsXI+gTw2bTnz1dBx3ktfLniKJrPpAkBvGfaxveHqrX35dG4rkEnx39BskgiJriJckY/QY5dyyfzCsVMSOiPyJ0oYVsNemuE570euTeXQFrMezM5tEhCp9D0BodyDwcnDne5Ls7VwZ7ktbRlP7QlQUFm4rbov2XlIsvBxBsSskGaQMUXVbV3tKOwIswbIj5LdZfZWVkncDEYgdQ2jK1hLOJgSb6OHrWZ6jo3A30HnLj1cXovOV9qXhzh2CnUfqBQGar/d41wybUJIwnWWpO+iA8LMVseLhY9tz/R59exRcoD++I//uILAZrOHh9WMqS9QSmln2yf7/r7tAPDa174WP/dzP4df/dVfxXw+3/n8WtnkAE022WSTTTbZZ7gdHx9/SjlAN954I5xzO9Ge++67byfKQ3bLLbfs/X7TNLjhhhuq7a973etw55134r/9t/+GL/mSL3mEZ/Ho2uQAKfOHCTgXa2I9WjxRdcQswhZejLYLzKBLK/R549EWvMPZyKvwzgXmiqFXbTEZhmQ4CVUl3Q4nHeyqVBL1Bg0xlyrdMOZEWQHdWQ0X2EFWlXDC+kyJoTmZUiWJ7hFx5RV6L1pddlCrZU5CBUMbSUFBFBWyvSR/22AQymo6RCAYglQS9yFFdZyNHJ3oSx9547ApRHIhWo6eNS4wNEY2RIezPp/Yat2hPy0nWfrVrRQjbC+raYY/l+ocGwhRXJcUlELhc13iohrB8KhoGdlZgCOenhL5mnUDjuZ5iX2u21T8Qn0hUKTE5Qv9nBO2t77BqlSkUfTw7GyGWOBRKHimgmLpvUss+GqbyJE4igA1Tq6BD44hMIJf06qB3QgvDEVz3FpFDCiiMAjc4NR7IgbUHFO5kqeMKSUETBGgDHHR+DX1PVyOxXpqSUUwFYStmZYZLta3KkV2t1oTyyKRMGbpw2SApojbNiuJ9lBfWM3mrZJ7pcLJqEpRUyVqjwVKd6rARufttjqZvk7A17A9UPbzMIGG5CDEsLNMHsknDJoHjGzSbWTMXG3bx19U7zKbTTttrZK0o8w3OdJYflbGWdzK2GtghCU8yHgj4lhYoziWLBNmMnHmQdGD1GPlats15gHqug7PeMYz8J73vAff8A3fwNvf85734Ou+7uv2/ua2227DL/3SL1Xb3v3ud+OZz3wm2lY668d+7Mfwoz/6o3jXu96FZz7zmY+oXVfDJgdI2XCdhztnuSTZGLDSM8ER827AuUWROGh7HDOpW349dD0zAM+s53Jni8Tv2xKjXcWOczMuDEs81Gco5mKfQ4IPnSzh78/v5w84rhJpVN5ALbpYHiZ9YqFMsthZDpv7uShIs+SEnlyNJn6UiaQifeQQvUxkOoeIJhkoR0LnBWnWW4Ip/MbCFwfEHxaoKliuPJq1fkeqQQsd+mC5/DgGy/c9kfUlr6qxBgNLTgp9bybs0ckmKRHQsBZBjkZyutBGqZ7hr17OASKoNKIhR7pVjjQ7GuLARbWvPjiG8ZhFOVmuQhuC4zJycr6bNnCzYxsVdqeeSgTtDSIMGbeAHydMGDmHah9UHbR2cCqHxzGcpYVJwa9ayoVpFAbZf0WdQKSe6iHK31OwVbNOSOWHVDUeG8Ol8bGtczp4m64I08zv5byrHDfaHBTrOREdDhnmym0B2jM5Rz4neo5rB4jhUclXSsox8zOpmNQq9brqVOfVfDKr5HsAJJcqB4uhaafGfJLvcv9rp4vuA70AsPI7ZofeyWWiC6U30jlK2kElX8PU+UZSAawQITqCLBVVRzJgh6DKeSQS2Fbg0/5AKuzI2RsOgbCMiOsrqC2/UvszKIO/44478OIXvxjPfOYzcdttt+Etb3kL7r77brz0pS8FALzyla/En/7pn+Id73gHgFzx9cY3vhF33HEHXvKSl+D9738/3va2t+Hnfu7neJ+vfe1r8a/+1b/Cz/7sz+LP//k/zxGjw8NDHB4e7jbiGtjkAE022WSTTTbZY9T+LMRQv+VbvgUPPPAAXv3qV+Oee+7BF33RF+Guu+7CU5/6VADAPffcg7vvvpu//7SnPQ133XUXXv7yl+NNb3oTnvSkJ+ENb3gDcwABmVix73t88zd/c3WsyyVjXwubHCBl8/MbLM8PVZSBQvy0bdEOOCgcNMftFjfMcljmhlKucnN7EdeXDOTz7gytkgyklfylmJNvPz6cw5/EXAJ44me4Z5UrkO67mF/9qoVbSeKzXoGylhCtKquVpEhKVGF1VSnBCaH6VUU6NPSA0aqyCjmPElnzNiVLEJLABSpJmojEnOITcT0kenBWKpEuOgyLvDTfdpG5WAi+MdGIxMBgWEzRbUU7ivtGhf39MiGSPtayVI4thwrqIWOCvmAxDAUuc5F1rmadr2A6AEjJsNRFPzSceEyRGoWuwgeLk1Ve2hOXTIxG6ZJxU3KFEV8PFcHihNHEcFZiCQaJhmUOJ0nm3dlWfQAZQErjaRzR0z8xChLVkMlgJeqnhTw5UXUwCgIrnw9yDZIzO1ED6yXSaX0U/bugf1eiZXPHK/vhwDLMoaMAnDxrlNaTjgSpiJOOevL4p2T5debyAXL0p6WqtZ6iIwqONhJ9YK0/C5hGTnafsDBXVHZJcY2lnUjKPv6d/GV1D3NSuoGiypLryMfXbdrVQcx9QpEelahcVZLSOBtHFinCMzpmOa9xNNYEU4//YtEBlooLQBE3U8H4oqkov2desrlBUbCBPxSJGX9QdrCIsHMPs7qWZWB/Nnb77bfj9ttv3/vZ29/+9p1tz3ve8/A7v/M7l93fH/3RHz1KLXv0bHKAlC3nA5adKHv3vkEoDxPOdQiWc3Q2M2HFdeVmm9uBIa656atJ86w8DU6KUuLFsMTHt9nZ+dOzc7j/Ug4D9mflDu4tizLGwfDDxAbsVINAVVZV4XwWN5QQe5jJpFkR8Cnl+moTTTpc1m3qCoutHBfIOLyItAKGHzYKAlAVNfQQM0oBOrDGGRDPqAGqvFlrBtH7CGj2WK7cKP3mFypvZx7Z8Vkc5hO4/nDFlVWHbc/VTmS9Kj1pbOTKKy0gSqX3Z77DacnLeWi7wMkmv19v8wXxW4dYnClsFTQX6gcYACCBy+/bjXYQysdGIJHYYVcQU1c1Gew4EhVRnM7H0I6V3teIHT03Rn5T6eKpEm89ZoDiKHv1XkG4+fyERXxfHpXxCdYXuHqIMPRerXhjUZ5PjQGVPu0jw4sqlyYZcQrIn9BwuPX8HM90B+zE5ddmJTlybpvkfPrSvpikytJpLS9xhEiZPswkXy50ypEgU/2SnQJ6T22Vh//lmJrZId2iYpavFkH7TC2IxtsyzF0O2xmMq7iSNQri2jMmgfpzxQDNH8f6defnSnDWKX+Fch31mBcHSLTR/GFEPCiVuTMR+O06j4D93DZXxSYtsKtmkwM02WSTTTbZZI9Vi+lhvNBP8feT7bXJAVJ2uu4wNC1XdAHClULVWMFbpLLtol3g/zbnAAD/q70JADBvPStnH3dbrvhqVLnHyuflxkObBe47yVGf9dkM6awQ7pWqEndmZbW8VYl8kGRIstBmKQmUz0hLKxZFZ02mB6hkRk9RHyMJvtFUK0VNJpe/nCQxdxw9AOBU1ChEA1dWIBJqByxtGyJvt70is6MVoV4hG/DnOgJGq2W/MBioDxZKYoOjYYl5dnAQ0BWyxXPLHPc/P1vjCYucvXpTd4pzpWxHS2jE0piTMMdpWZqfhhlOioTGhaL2fjp0rGp+4WyBTak4M5dymMydWcwKV5NbS/SAoyoq+qKjLlZXRkU6P5XUrjiJ9Aq+VulGZTrKV+nX7Zs3zWjlPaoA1KR9dpDE52aTiQsBoKGI3xBVG+VgaR/XiFHt0e2ihNYkMEmCYegrzkrkduHgSQqjk0qfighRRXho/LkCZdkokRJTEf4lxQ8kEJ7m9hlLwRg1juPMcrt0xdFQ+MjCXEWmRpE9oEC9a2rLbnTO+vo9R316uQ5UKdqsIxxFqUIN0+XjG5GzcSZH1YD6nqX7UhNPtmZ/VFInuOvIEHajdDvElWpeMn401lXEi9qvZU7G115LXsCo/hoMV/iRDzFEg+At4uoaJkFPdtVscoCU9RfmCKmDJc0ilQcS+sKyvHWVrhM9V4hz91Ir5c2mFcbmpgtcou1LlY3ftMC6lLavLdpVweSpXFiRw1lVeVXp6BDePVeh8mXkCg6U8n3jZEJLQfJIOGfAQOAwFe6vsHYyI1BSUnpmVAUTBsmHaFogrWqnxaWEppTpt6cedlv6e6tqjoM82LjUuWvgl4XF+FxhMT402J7Pn/fngeGoOH6LCJSwNWlmtTOPxTx36HWLNW5aZA/kpnl2ep48u4Cb24sAgJubi7iluZS7sFzls9Tij4fMafGH8WZ8os/O692n1+ETp/n96Wl2esJZKzpWJwZHF/JpdSf0GiU3ZBMZHtEPm9DSQ9xW4p4seruk76qHo5H3UVEZkFW5Lkq3yamHu5QHS7l2lbOiHbPRcyDZcTViGf9nHnaTD2L6fGAzBHmyOCsOdqlXT61jCAvWyDiw8pqa0keQtiRnEDvJ/QEAv7BVFVglElzObx9cRw6c6xPDWhURowUisUJrckF21gTuYgQ3SsUbthGOoDkn7eOSfJ0/44Ex7FQ5x0rvr4IZGaJTuXk+8bV1m5K/tg58D5ohAEO5f4jc0Big9HdqLOKsiNtSdV5reczaxiARm7ZNqt0Cg9el/uXciSC1LeeLsqAa9Ws17vSYVPvlcaKcLxOVA2TU70cVhblfDAyRhm7LF1xCbNK1rQKbILCrZpMDNNlkk0022WSPWfs0HaBPhRPhs9QmB0iZ2Vpg7djbD0ZBryW64QYjmZEJQiLG8hgRhniEbGJ+iv60A0plk1sXavxekn61kjLz7uiQtYIwYgOJ5qjVEa8KewNLHxBJW5Lf5CgBRXCk/RQpgQWHj2MYLa9A0aSyuoqAKREtIjS0val4fhiCIikAC678wlEDW2AK65tqdQ/kBMq+cAL1RxZ9zhnHcFxejxLiQVm1Lj1mi6IhttiyZAQRUy7aoeJtOi46IVTBd+g2WJbOdybiwRJioaq9/715In7rwlMBAP/rEzdh9YmMU7QPNOgu5PaczwEkzC5EzB7Kbeku9rBn+bimREEwDMBQLu62R/K03C3RxeUS6fp8kv1NS4Tr82pbwyOsAt5JtCc1qirI7k58JhpOqKYoXXuiEni3Ce2ZikyNI0DQ0RODMaeKHaKK6A0S7dkMwLYM7FAGs1cRv6aBIaZC6oO2hW0kGpQ4oZkiJlaiL42krCdnECkyRKv/pNuo4BcFVUlivUpcVtETivxY9T6/Ukit7NNIgq/uO/lN5EiItQa2EFsK7CwyFLE1igRwF77J+ytt7CXyQ+13W6mUc0OSRGwvCeZUNWf6ALMtUTofAU8CduV7gMicNA6uJJ3bXq6Lc1QeJ9B1jlIF3p7bb/jahblDmJeo+IK4ntTv1dwlSdqjsa2jSRzxUdGmMp8EC75OTOqoCkh0NDVZVSyiuZa0ht1kj2ubHCBlNiI7OjToWym7Bt0sSCpsLh4SlR67RsqjgSwOCQBpkJtGE8XtKwnWzg7ZODeDv1PgMhPFcWpOlWNFjowSbg0zYYCOZRKMMUouAxLDYbaJfG5GVYxRiXUKhiE/cgzDwiAuhHCQJjfOyZkZ9IfFGexlsjZBhcA5LwIYyOk5SohFLBUll6eZD1gUcdCj+RZHs/xUv3F+hnNtTo6YKTpfyuHZxoY1sx7ss0exCa1QFfQzXCxaYRfO8uv2oTnaB3IDZw8CNzxUmvJQRHexOFsn+SLYsy3MulyQbS8PfTLngOMMm8WDGfxBW8675JotHIaD4vgdGnH8joDhOPdXOCzQxXJAU+C+thGolcRMvXcYNrndYdOABjhrcyWww2uHBM3IrCuXyMgftkOE7QtMsunLq3J0hgGpKEGnwYuTV/oixQTDOV1WSqvIjIEpLLJmPoOZFS+vaBileQtDMMzcsWMESMUVE4LGJDkjITH0xrlHKUnpuM5HItitcQrqMup3CtYJcixRCzY1NIYavjFBnEy3zv0ze1DBfc5clvk670DtS8FaRAlghyCOV1SRhAjGlsY5TACy87MpkwupeUdpuDGGoUrTlpu2bYHyPnWNOKzOSD4OOeVq5aarQjU7NOfoGGFvTuq0+bZW7TYBMERyqbXIlPCwV7mS9Llm8ZZrp5xSun1jdm4rpverbRMEdtVscoAmm2yyySab7LFqUYfvr/T3k+2zyQFSFs8NaM8ZNF2p3HKRFdqpMCUl4QTq+wZ+W1Y8JQzs1w1CgXfsYGAL3NWtgJaqfkihXVcvRFXdoyQnJCy+fxBX1Vr8O1ntUtWGXxgMhwWOO4JKti2/cQraa0UPyljAkBJ4iQARTxIAVrPXTdQRh7iRIWZIfTyJLlNqRI4DELiMuTiWJaEZObnbHeTV6HyRowyH8x7n5jnSc+P8DNd1uWznhvYM8xJSI7mRh/wSn9jkUMrH14e4/yRHftanJaJw1iodK4OGErnLQni21cmxiuulj7x6jl1Z9Zo5cDTnbX5Zxkd5HQ4kmTl2e4gplezBcBSRzhUplsMtnnCQz/em5Wk571Ocb6libYuhXNQLpSTu45tj/MmlcwCABy8dIJRKtUjwksXeVa/xickImVwwpJwgiwxxYV06qbym9QapL1GfEJB05Cup0AdtKgnAxjkYjCIKXcsRIMxnwKKQRXZlbHUNK8BXbJERu/COT7ADQW8RhqIZmjuIBrAxHL0AvRojVEdWRXGSSvDV0SSKaJj6Hs1tibAFGsQQYEp/MSSYEmBrOFA6TEWZHs6or8Nun7NRH9A+Q+DvJ+93Pwck6tO10ufqlfotzhu5F5yFTu4HCgTGMLllHbeoCCBpPmv6yPcaVay5bRDepyCFEmHeYCjR5f6YIs+Goz5+UfP8AEBcBob/jUtCIKokNnTEJ9mE2OzqOU72+LPJAVLWLgccHEQug0/JMIzQFyjLe8ewVhycVHEVxub21LBmV3cCzC6WCqSziGYtk3H+kWDTgJq0vUykvC3Wk0PlkaHkMChcPFD570Fpa2vlJk5QlUKSu4R5Ifua+crZIfFL6pdZEyoniMQ5+6LD1UeTK80AmK0VYVRVispmVc5TQvUgpldm7e1EnLMrlXoH3RbXz/LD/1y7xrIks5yGGS4USldyBO7bHOITZxl2unhpiXAhO0btBarWupxzql7V84TOY3sscBU5nGEGDCQuq8jVmJiySyywG+eRIb225DAdHWxw00EeSJ97dD/+/PwBAIVp3OXtB6VccG4GDAXffCAccKXaWXF0Vr7FZigQ29bBlhw3EufUelXtaURzktvQnA0CbVGF3uVylwjWUg6PcY6dGeMc0JT33a5TE5cdVxUlBTVJJY88MKX8GrXjQ5YU3MUl0alabOw4LfrnVrFOa2jG7jo1xqvcIE9QcH3fSo6N59eqP0e5NnlnNAdYEIEjUhSHhpwT7WBanRik+oVzrkJ1vVKsnSNjrfzOWtkfQWTOAQ05hi1wkO+ruMyDOi46+GVxTmeW9daS24UBLwcLcv7ZkJg2wa093BlBrMrxIGdr0SLMizjygcP2fIG5zxUY/VC0vMJBRDoscHVZTC3mPS/kBu/Q9wUu9laxsdMFz+kOkVZC18JS3Lt4eES/n2yvTQ7QZJNNNtlkkz1WbcoBumo2OUDK2iaibUTOICFhU0gLN+uyyjkRfpfZmWHVZ4K32rOEZlVW0+uI5iyvNuxGQrZsFkzYBmMkMTMqMjJlppdkxTFZnObLibMGYVaqKri6QkgC/WGGVQAgHRJXjhADdk3gRG5nI0tCdI3oXZEEyHZoGBLclpVT9MIzBJckgZCLyURzqF1J1MXExKHwKjmceJNCg77Ai6Fs6weH9ZCvzT3NEZzKMN34HFHYFumS0/UM/VkJwZw06C6WqJ1S7qbjRkWPz4rhDSqVcMNEJrtJqWEGhALdxcUudHdu1uP8QqC7Jy9y+dgT2sw9dGNzglvaCwCAG+wZri9c/uetwaas6B4MuTH/b38L/sfqVgDAH1x6Ej5+lmG+S+scAer7Bn5VkolPGrSXciM7qli7mDC7WJKoTz0crbJ9xI6SNGGXANB1koNLXDc6iuCswFXzhlfpRE4YFL9R7Ewd4USJnmgpjFGwJyesSsIrRzUbU3EVAZnbSMtuaGLJsdURINqGvVwxmiuJxqnthdfJhFBFfoASTfMqckNRFYr0WCN9GKJAYyEABC/SNmNU9Zw6FwWVJQ2HUdSnaWBHVXc5EZyi00YiQCVyh8blyA+AtOgQDvL48oelQvHIccWmnytFe1BXDXQAADbiSURBVC2vwtdDOJba04TupESfz/L52U1gqNWoSBWNp9Q6ROJ4WjbMC7Y5b7E9n7/LlaLHEeko95ebB7QlxUEXq9Dc5b3LenvICABFfixV9pr8u9DuqVS5WjblAF01mxwgZTHmhyTZsGkQyoOjuZRvtvlFg9mF/Pn8oYTZBbpxi3Ow8TCUNxFjRehHJji4U+Rnjh/+On9AKjUAXQXG+1KTMuPgM8PwC78eqmqqcwHmuDyQi9Mz62oRWBLt9MHClfAwpS00zvDnIVl+T2ZckhC+gehYnZQH7yWgPSmT3ypV1Rx0DsNW8qhIawnRMlt1LA+b1SAha9eIjpsxWbwUAAuYhq1DIhJLKLbs8uDtj9VDdB4RiTlbC7Dqa0DaaRFMg8AVc21EV+Csw8UWTyhw1o2F/vl8u8YNXd52zq1xYyFdPC7e4NJs2ZnbpBb/p3ivD4Qj/El/PQDgD1dPAAB87Ow6fPw0X9yT03mp9AJQztWuLbpLlvuexm93KvAsOduxtUhHxeM7uAxExDAJdhzxDFmWMdsq2Eo5K1WujWIAFmI82VdikkAFoeqycF2+zPtCVRoOZGoGx+NI6WPRodRDYp8WbBb/1LCWtEWYzPNrEwwclbn3uwufNG8BkHCelT7k8n4Fi/sopembgausDDuZrnZKo3J2ACAG8RvbFqnAj2neILIDpPJb+Nqqa+eIakAgydganq9YZHZhhJphJtcm6/2VUyyw6/xCRHta5s51YOoEpvdoLFLJIYqNYaeZnWRr2Hn2C4v+qNzDh4AnFu0FwfsJWpMx8n1bzi9ZdnScixUBLlVU8m9KEyeX4jPDJgdosskmm2yyyR6rNkFgV80mB0jZ5t4DNM2SIxZND8ypEmglry1BXKeROVOEXMsBMypxgoTQ9yVrWlktx87y6kaSPdVqekTnTkar5tAZhmr8vK6iAoCwFA4ds/RoC3dOy7BW4kiOMxG0bnSqymso2cghWeGYCY4jLbFsi4MFtiLx0RQpDEoOb08S92GzEe4SQKqhSFbDROlD6rP8gfwmUsK1kfcxWL7vkyItI5JKuMSL5HAkn7nCpzPrPLo291FScF9fIoIpGUmktUlyTjm4kbgS7lKQKFlTLt7CDdyftomI5cQ2BTe4kJb42PZGAMD/3Z7DQ4WraOVbrAq0RxDfyWbGEhzxUofmYt4vj9mzDDUCGXagihpb+JeiMxwFCLP9sBBZdFqSYs8XEvZWLmYyu7IiN/I5rcKz8jt/Ox9LSUJodfEKnqL+DhAeH2Cn8krLx1RVb/w9NUaS/KmTqQ2RoY6iRbpqM78miWa1DqBIBiV3t3avppbepgkm6Xpk8sLSXDVXVPpYFHli3hqBlf3ScuVhde2orVpzQvWtPldtmrMn/14+cxugJW7PbeJIHBWCWC+J6GHmuGhDV4NpaRBKqKaKUS1nkpyqouzkfBi+VWMDwE5Rh7VJKliDVPkiAb0nWL/02+AAbxDXe+bzq2Xq3rni30+21yYHSFn7kENnDVwJ01bkhJoRlqLLrcFAukWWSj7FgYnqvWZx1Q/08UQLyES6I8E1rmyBTOqaDXj84MhvAJT8mbhx6CmHZp2/aF2UFARXkzma0cwXk6lgL8rHCcXpwdZx+b/dGhFo5DZLWeqwlFB77ERXTDtww3GZzQ895ym1reQjkflgESLh98iQGUYPTJr4gzTINILzU5WZtYmFcLelum1YN0hnImZKD77kgKBEVgHAdQPvS7fxE6uiGTZ0uKfNSQqH7Y04aPITgpyiTWhxoc9Ozdq3OOvzxQ3RCrRHuVerFqmIrHYXHLNSswO0SXtJNkl/CajHrGbg3dFVGuXBaN2w/JoEslQQl74X9pkWxOQH9lzRAnQyliv2cz00ldNjVLEQgJrhVzlDPB7iOK+nvCfag40wKtfO2m5bwsKxPlb+oBxXVZExXN3JQ5313jpUQqBa90+TmQIZet3HCk1M0q6Xc0DadVoqq5zXPU7kyNkba8q5QZxqzd6s+4AZn03dlrFgbMV6rd6LwKr0RSaILNublCEvQPQQFwGuLPg6tbChxZ0xCT7IvEGVX8FbRJrTNqX8f8hM6mazrwMne7zZ5ABNNtlkk0022WPVJgjsqtnkACmzPufuEgGe24hMA1d6DHXCpE48BgA/M4rrpV6xcFRmzwpbyxHsU3oef1eHvWkbrfSMhyjKl2hWagwrayfnOMGX29Qk5kEMTcKgqeFRtytzouhMUTpugaJ6A1OiTSZJZKrk5CLMBE7IyvKynRXtF1Kl1hzk6Mhs5nEwK1ITRlZvFI0aouWV3BYtBlKjptXdYGEGaoRUeJD0SWgMJxD3BkyfwfxJ3rCGmfGGEzsBwJUVoS/7H4JBXJQKwPnA0Rpq33pocOryyTbugPdDEaytdxyB0ufog8W2RO1iIeG0Jw7NWeH2Od0jqZLqKELkZNvysVqNZ/xIzosjQEZ9zHpMAntKIrskC+vE5tSgSlKmdpHpMU3tCooMMqpKop39oESjVNQmjo8ximiwVEwvv6l1v8p2eh1FfSRBWLdHddKe6Ad9L3RSIaXnCJaK6SR6EWdJkvHnEbZEMqgqybmkeMuAgbi4TvJNZc8caw/mtlDi/86mfM58wfZ8PoqMUX9V3Izq96y3ZoBBnS9QR3BioyJSqq94fLqkEt8Tf45CXpiaxMUHsIAt/dGWz7vOY9mVOaT1OOq25av5NyvfciVpSga+zPUpGCSq7FM6h3YwHE2/JhZHBGRX9PvJ9tnkAClz21KBqhiVSYOGwt96EgytqkSgMPZctGb8chSqHofw9T1kwGOcNJrcWvkZKkSPJNUtRoWfNSTD4WMlnshzk1NYO09IBqFMuqk1LKRZ51NQskDdFj6FuH/yo0mP9NT8gfzOJIgwqwNP9hTGBsBs22FwTEfgyiTXNILp+ODgS8g69o7D1gQHGDVp6Qcyl+aumgo6MMqB4Lbqh6/qz0CEknTBorDIeu8kv6pAi9YkrAqBZAiWK9XoNykZntRTFGct9g4gJ46qvLZG2mrVmCNIcyb5SvsezPq7GZ5Rn6fRd8dzKTnNlVMvcFk15tUxdo5/mSovGjupSUzaKXl1CurZmtpBH1tSp6Kq+6shwM6a2T3vZGRMq5J4fa/pdlf33QgtSU7dSjOZF+KiXMQuihafi5L25hIT9tG4b5wQt/pgsd2OcXMZk9l5Lw/ypMZqVUkq9yJNPrRYMUb6YN8jVTvSJtbXM9CcSE7ePLGTl1ySSkuqpmwiO3m2iWhKu4iZP7P0U8WnDGqLpFgYSpWjCyyIfNj2WBa4mfLxjjuLVaE7ecAteZEyGMViH2Sc2QFAv6cDJnvc2eQATTbZZJNNNtlj1SYI7KrZ5AApa1eZnYNgr2abqgRCIK9qoqpO0Ks+oA7njleKsl1WshrWogiKUYmqZLYH85g068QkYlzRM6SdZMv8R/k81Kt8CuETh0cOxeuVuyRskumoDlQkhG8vlfDKlSWjUPfOeavqGhjAbUYr2OR2JCnouADgXX1eDSfjYgdSBFRbHLiSRj4cJdWWjxlajHrlrsP5iffLidXB5GgNMjRHxGkU1bE2SVQnGuY1Yh2iZJC0jJaOwnHlFI2XOvqRRrpiWb2a2vXJI1s6mZ5hm5kkt9Jq2OyTC9Ft1pEYK8erYA4z+g5GY4OlLCTaw308GI6EjitddooLxtEmBbONrUrg1RAgR4NGPxjBbTrCU0F/DcHOkqiLWYDtcqd1pQKxbQJmJVE3Fxzkrw7ewQeR5AEyySUVH6TewRSZE0eRsY1AtVVUplUwHuTG1sSTRPRJ11vDXkYViDBcqBOjQw2z7RBLNirKOw+w5f5o5/m8Z63HvPTBvB2YE6tzMsCsGV+IHNWh7zTqxo9qoPWlYTR0fLTYFFLRTH5Y2hiN9Gep+moKcatV8PdVt8kBumo2OUDKjj86oDO2zvEh4i+qkmkNDGP9pnpwACUkznk1QCqQTpglwayrqo2yr17llxAkMwjU5XpxdpqNQHKsl7OJLPZohwi7Kto567KDvgcJLKbFTBhVCztvmLlKg0k6QHWQehiI+KBlJ4rD2zNTwTA6LE5GuRVuo/Ix9pGrqqboCiV+yDfqIWZrJ4sf8PohTX2r5hQRIJV8C32NuCTf1+fADq2qxGEIoYlccu+6yIKy2kJ5iO2V6klgRwfeMAGkGQw7jNV4UQ4n74Iedl2S7Vq8Vl9mgvDayMKQrlO6XuVhE3rHjp1ZO3ZK6LXZCCGgCcrhNLq/do+vc9hIfLbq72RqZwTYybXh67xnvh9DwNgzjqRysq6skgOo47DDZ1TJ+f7j05jgsTWLrLvnZoErGgnKMiYxzcSg4FPvHXwvzg4AYCNaezaoiksaL6mueBPiSIHeuN+8grGj3KOcEqBzgLzA77rfJc8Je/OcGA60arVhE8NdBGvNGo+Dkqtz3G0xd6WKi5PNxAHqo4MnSgmT+Duz8huLhG058ZXvsCml7RdLleXpdob1trDGn8xgL+bPZxcsV1TOLuVjdScJbhvhB2nHZI9fmxygySabbLLJJnus2iSFcdVscoCULf/PQ3CLJeIihxTCskFcFCKsjqAiU/F1hHl5T9VLnVplWUjEwRskquopx8uVRBSqRlVdw69MfJb1dYAcaXFlBeh6CnknWJKEGBLMkBvEpHYJUp1jhMaeNMM0tb02ExNHhAJXt5mK/IzOPVIfKBr8fO7llapsNlBaYIlD9JoQUZOzcQVTY6p+BgDMZVXpO0mUDcBOpKQK4QfwnMKIZDQKjkscMtAQWFWVx5CmqaMLyJGxOMsXz88icw2xqSRp46VqzlFUZzCqAkmiKlYlu1eSKIofRqrqSl8sErCkkFuELREe5lJyEbOij9S5gNZJJIIgl768nq5nYEWJaJjAkZNjvakjBnRtB+xES6sigKTgExVl0NdL4BU58Qr6I+xCR5s0VEvHs5K4zHxB8/o3HHlSsFdVpcaJ4gmREovpq0l+pxODk0r05d1Gg74k+fckYaIjY0bDn+CqJK5CivrLIiXDUadZ5sbJDUwckdaRQNPLHCCwqhyD+MGMggGTBYye55DHYB31kYTqqnoLyNHwTpKciTOLomBdEzAvePZxu8GNsyIbQ1oayISsALCNDdZlQrjk59gUVkSCumIyTB56YbvAySZPImerMs+vG5jT/N32xGL2YN7//MGExYMFenso34DthQ3MuocP1w4DSymKntsV/n6y/TY5QMr8jYcIBwfwByV340DEGvWkPSbtGhtDWL08fLEuP9afDxDSOKAuRwWAlHF1On6i3Awnul5au4ct1bkT1O59lVtVjpLO1eHd7T7cY5uq8hmdI0CvReYKrt9XsSa5Vc06cs6VCapUh/o2qkld5SZFrXt2UOCCQ4PhiHKbwM4QlxR3cu46v2QfpGK9EYeNIJkBNdyhfjfOcdD6RWHmuC06v0U7ZnQMcgzdVt43W0XCp9mAGfZSfXBgMBxKGwAgzpNccgNmAZ93Rats1uN4lg/WmIimDLpNaBguiIlK9gMSVTy6hFiEPAPBp41lxmPbq/PaCjEj63MN6nobgV4rB0kz+I58yCpHKIErp+IeCNcE9cxP4r/w+D9TUKgTB187MloUV+f9jK+HfuBnAd264SYYvl8jVfRpUyzjKRiByb1hOJYhUUXoCUDlotHx5WRTE7NOH50XNYtXAOq8FXTGfqWTRU4Wn6WbhfYvDNipTXLeNslxFfmgLZ83ncdilsfiQSlXP+y2OKRqLRsY4toojLctE2VrAmaF6vx8u8JQTuLM58aehg4+CaUEUU1w328cs/9bL/0ZO8PiroSVptbCbmfwvgX+f7g2pkVqr/T3k+21yQFSlixgU2JhSDsIjXxVRswPaVlJBpV4uo9ltVrBjpIHd9qhVqLhMqsrnmC5/Br1E4ISGFW72RnTk7bKY+GVYpNkctPlv3qxqXJSaJVvOPESaM/KrtaJk7dJNsQOIn+RcxTKjttdFe+cZyUPRk7UVg8bYb8V3iMA8OVLiTmP6vPic9nTR1W0RynTaye1yi1K9b6izgXTLMjKAdMRHhaLpDyvdUKzKhGaPiqB3cRPJOGgcjAkBxLsTjK9HSx8SS4PBxFbkixZysOXHgoH3ZYTRje+xbY4QCwSnFCXHZc8oVRW8ME2PI5sb+V6NXLuevxzEr+XiIFIwqjrbHUOkIrSqYiE5uFJajtZtbAgeQmdHL4nh6cuHMjvM2UEpI17yuDpvE0rz65q/cEhKIk0CpW0yvMKRvKsBnVfqPYJ11jC2Eu03gAU5dqMpvvRgqvadxVZqr4uzVZt4FMqUR10IipqnPD0EA1E2waO9izaAYcq3wcADpstzhen5tBtOfIzK4NnbiQxOiSLTXFQLvolTsvFuVSSEi/0C9y/zlxbl9ZzrM8K5w+xup9aEWo+AdqyeOtOItqzkpu0LvmVfawd78ke1zY5QJNNNtlkk032WLX0aeYATRGgy9rkAClzpwNcFEwqOSM5JZ2sSjkHaK4qnwrRVwUfqbwGg91Vg44W1WXy8qrzBxjKWUrpLOVzGJM4xyh6I+zHFPCICspSK0yCDdCpqqU2oGlkvxQR8IWsL/SOQ/DRWVAMPARZKlJ1zXBgFOShcnECRa4M92FU+VNkVsFOOm+nJiFUv1ds20xhQCRmG4EW9CqOobutjsSoSjuOXIGjg3QMoORPqagFAFgLmJWcx5hZ2MSkqmiEcNMQ8eYQYZh9PHIYwcSIZCmsUaAmH9FsKUqmK5ikv2mF6w8cYhHnHI5KNUw3x8U5jafAVUfGJMShDmeaJkofugRXxh+J0GLuEanaMBokT31ldiMlVaVdYiiU+lLn28VGRXtUrg9TFQQgPQxMMNbak8jPblTS9qnKnZMfUvsN5wT6heHcPC4rNxouritIAdQknAlVFIpt1F4+B2qWLmfnqlOJltK+dL6dSUCVmzQmaFT9qeHHCprXETdFB0LnF0q/xHlCLNGg2EllYSp9YG1iCHbeDLhxnkPGT5idAACePLuAcy7fQEdug7bcLEPB+Faxw//trwMA/MnmOnz09HoAwCfODjm3ZyiM6Vg5uFXJlVwZzEuUmKLF7Rpoz/JJdicR7WnRCjvzsOvcxv9/e+ceJEV1/fHvvT2v5Y0i7K4iYBIfuMYHmgRLWGIpSgqjUJZEqyzNw5IoIsGkYsqKqPWroLE0aBnjoywTSyuYh1omISgKbCREgwiRoEGiIGItISCRZZedme57fn9033tPzwwKKOzKnE/V1szOdPfcvtPTffo8vkeF6aovOog5QDCf0OUkOUB7RAwghoqiVM4Bd6V7tVd/Ik+dG2tozvCwV1D0yZ01DRx2IU8lDAb+uVWKDQohMolmCNfCMO4CEMAkO2J1ZZSG09oA4JIOrYGUyfgGqNlM5FRUw0iju5TEvyN/9nTbJThdI9u+wuQUQuZW9+0j4td0yC8Kcaf6eB/9vlj5/kwnYPMeuTozD8fxHCPNTuqVzWNViFQ4zuchJWGnonEGiI4oNjwAqDB5ZMaPyQcIGxJDQgful1QrRKZD/1lBKUn8LJGTW1DGh13BXnOflVHwmagB02hiYbxkrFnAd1iPmMZTsk5mNwvTZew2gch2LM+wPA+uA9QnOV4U4jJuAJQzTr+IJxk55WDDJAR4zpS9eGe84nj8OdaIYyE+Homx82nnKPLzFJTgDmZlqiJB8fa5UVHxvg4JsGExFpVyuTo55TuSF7QLeceaP+mwbLzffly8bQaAlE6WirjUBblHn3hfkfPFDJh4AZUOzbmy/ooQuJsINh/sJiJeH76FCJcNsNuvuMGgyu8z8OFmCnzCtcoZJ6mQS5Lt+xRKriXFoHw3BmbjH/mApA+RVgZdyYH4QdQXO8pxCOuD5PG9zkH4z644EbKjowFhZ1IA0hkgSNrCFGyhRafPP8t2ETJdye/dSogUDXQxObcWI6hSPEZVCqGKyUmLt5PQGiqSMvhDATGABEEQBKG3IiGwA4YYQAzKBqBc4O+MiYVHeGiF3ZWmSnmR9vrE3g+2fedCT/7ngmzc/cyPVyZoppLeVibUKO72CYbxAMFu7ZVLYiQuiJaomlJAMLYaw3qCjIZJPETd3VlXIWGKXl3WVXvx8RnlXvB3jFQRzrNj9GO1ycgqUnHNOoCgW/vkT3vjlQVsFCYqo0o0kQuyZXh/HsW8b/bOukgu6TbYbZDpTsq9Q+62s3f8GpGt4hpgxSI1woYklJRXLuxJvKyahTGctylUCOznJiXH2S6CLhm3jq32Uyk3RY1EVKV8aIIt6zxjxiBj0gelirxgJ5T3TnCPhesNF/gQLzGRSRvaiBuU2uOIee3stsJ0+b7rb8cqwlw1YIk1GCXvYQsi5j1hFYBVHj1DzmOnDLkk+/i5nVv/3TpR06x2YUBjZSACgHJeCNQXHPhwHA/d1ag3SP38nOeLK2/b5RRS30el6KKKYq8EECfdBqVqDyT3ULlQVMaLkpaT6qVyH5awnWXNZdl3az3OUcF7bSjDEqpTIf3kn9BXp7lH7iQpKSgWkrdNgu15pVwO0JWNXd4dpTy2d/cBADRk4h9NTkc+GT/KoLMUD9aWsPNzFIU+8R/ww9aseMGdN4z3GvLiBzevWkHZELPWoGz8pavQ/7ApE4A0j1ceWMgY0CcIgUkZ/J4RA4hROrwBql8OYXJSjAoqpb0D2Jh+/Jyy6fwTACkNkVT3aFY26kpVdfos6k6UroxepbaVUmhWNmRhXc7w4TK2mC/9VamLM9cXAuJzgHXq6jKQYfk8bohM+ySl6eKqrWycn6BYjlJluC0qB4B1We/SvsUB/MnYNpQlXpGGuKIJgKvMCrrVHpWkfdjJhn+Ua1oaNGiUTDoJgrS/yEU5f+Fw3z3vVp/j7n62Edulvuw1VeIwXvw8u8vn6jipgG4DlZyknBaSIfcaKeWsbgrSF7x4Wz4fgzKKhVz4hSkJ7YWsGs9W4kXk2oKYQHltq6zyFWVsjm3IxmThvvta6ssgbuz4UGZul823iBAkhnxmVxnahh6YQUp2HzMaFHBBGrh9t/vgK/HIHWwuX0orb9Ay9fJy3ySXp6D8b4GHy3jIiRkq9nrLKwBrKUEr8p3l7Q8sYOMmrZw+l9s2yzNUpF30RZvIh0a11e7RTssrymuU+yTnq+Sx3JA0H0aSs9g/CVM3GKiGZL5dexY/BiL48Ka9mSpr91wZbwC5KjWuDk1+8kjDySWYRIE+yhHCJJS6u1DADqtJZRWhM75KLB6PcuMC4ioyk4Tsw3LglLP5eS7VliWV+2SPb/uu9r+pHAF9mZw67Las0RQfZ2GYAdZVLSZ8xhADSBAEQRB6KxICO2CIAcTYeXQOql/GKx4XvCciKiR3lFmkEze5sBgQ3/WyJpUuBGaU80S4LnxQqFWV5DxAvOoJ7I6GH89cPI4nY1aE5qrWq0imTCkbM7d83Mw07engHhnScCrDOhHYy7AeUsaomv2LbFWGLvs7Nl7RYlWUKUuuggQKMCZZOAkHRg3eS6bLac0Ul9yZfJ9hg9/h+M69xiSxxFAXBnE9zliIgGmuUEDeBe+ifDoVfkyLwCQfldzN65JPwtQlG8ti4S3lvT5xlmriBXMeP3YHG/hqP1fVxBS2ddH4yEbI7moTopx2oQs0aHZM+EReuysaNcI3xu+rDlmCbzeQTZLNs7uSZpVdEYLOpOJmVzdUd/JjsS6PIAASz4HKBqBs4kVIHnnVE2W0H2OGi3fasJevUEp5dpPfd1iAq1wEqn9rlVpPXCm6MjqR8gqxZZ2elgbPGffJ1Tk7b95TabKETOKdCwLlw2D2cMh5sVZekeaqUwv++I8KXikdGfJelZL/EnmhgxuimwOvWK7LyjUI1ew79lWOfg5SWkUspOg8jWWNKAktI/FK5XK+GWo28OEwq1dVDAOUyok3KfQuWNLk5kPb0F8IaCbyGuX9MQEAUcRPjMw7FygX2k79DhQQlQAsw8HBsHDk/iAG0B4RA4ix60gg6AMvyJb3pefI24SSiuC/PWFYkcFIpWLQVmE00+3LLgPWLLKWyGBK2VX517hKay1VZ75+Vfdl1qGaK9nyypRUQ1f7et6HoFIy+snzoE8ZhUJ84cokLRSIFMrJSSksZ718vz3REnxJf96rxyIgP7+2vD+glKCam5fEyDLlAGRzm0i7DuoRD5Nk/Ph9WwAwo8QbRSlDpsI+MjXaWQCJ3ckblwLQ3dorH3cBWVaFEj8aF/4JukPoYkX4xxhXlQTAFYEh8IaASpKjdEal84XsOlH1wZESBrRhUGY0KXau5QaMPdAUedmCSHujwTXWjODbX0QsDyNiYSlmrGkbrsuxU5E1ZLT2Rk8hcOEwqzRtAuVCd/xiZbKqKszBRTRNlgmXshB2KpRpq/pYCNqFVspAKuzFDCP7Wc5AUcqHRVkukAtT8ias7LfqwsoBQNbQVuyQtJ8fEbQNRYVedJRK/MbIrsTycnZrb8yzULzdFt8f9/uIkLrBcC1s2PedMhxZfmNVGxTN5jtrnKxHxobCWPNgQ8o3hE3GXw4DlJksh23noUvKV40mj5lu+By8GmKSvLVPlFXeYMyx44NX/Wkg6oZwCCAGkCAIgiD0Voi7kPd3faEWYgAxwgEGpsGwHjbw0u6J90FnjNPAocgn5zntlVA5r0+w29+NZHZ7QTJfrcI+nHltuJaMD2Ok76JcawX4dVKNIa1+S87fSbqkP2J3NKwiLdXqwk5Bkeke2RYKeQNk/B2bTVbMJv7viLkZwkzge+7YO02jWLWJSVeyWU+DrVJjMvpae2+QvcMvk4JxoowArMeO/E4YdqdpPVcqG/nPZecHY93pkfJePSsaSb7CiUgxL5z2zUqT7z6300vq5z80yHXEC+c+jG/Rg64y9M7k4Cix8jXFXDQ86Td5TgZQSYgoKCcet6wXN6SMTuvoIEmW5iFae0J0oRnmWePHgPF6NKmeWi4JWvmmtNq/7/p/lXxlFhe/rHX8k9ZA4gVynp6c9/pE+YB5cLwHyTCBUu4B4m0p4u0j9T07p6MbKxsL91jwM6SdtsA73FSN30yqzxzTN9LJPGv2uzZQCCpDaFHc/y0eHyFI9Goy3ZH3ECp/vjE2QTfyBQWuSXI3C/nkWFVb3oejuBc41RYm9M/jz0JKy6vS+2wyYE1i0x7lygpXUt4zi6zvC6aZN6rbhriMdt6ekhU37A6gd9uKSu0FTDtr9NUrkd8XsHMj00xKNXHlTZ1ZiyMArsLVhRIPAmTIeQH3a30xgPbIIWMA3X///bjzzjvR3t6OE088EfPmzcO4ceP2aRtUMDB9Iu/yDQj27KT5oz1hKO1i0643V6RiNToAvGGmyQBB8rrNBQqKTG24SCw0Zk+UvsxXh+TyQ2LxQXsisxcLlgfCftA8Dm8vXBSkf/D2kZ/seRm7i5XbjWV86M8pAMNfDHgJqw4MdFLZ4ZaNfC6BKgf+ossrvmzYzRBU4I1QU6lwHWmoxMDSkTdA7H4CvsScSLuTgSG4Du2KX4Gc5efnw22xrFyoKw4B+ByIjOt9Fj/mPyTkdnmjJ9gVWxK6Mz47q84uoDt+TmEIZQ0fa/RkMkBSgots1lczZbQzCmzZDmV0Kv+LnJKyrw5yBrNCVchGRaw3G2u2qkMv0Ojy18h8ZDqCMvCCgoYbQFGVsCRC4xR2UyXeyf6pMOPDfaHxzWVt37McL1tiNyGRr1LkIoQ8/MeVmP3nsvcqLthcdZoyQGQv9BU5UPG+sryYIqAq+rxluwg6KXOP1Z3JPweS8n5fBm+r43hI1DYLpmwAFdp8Og2dzJFVFg+6lTOAeKgnFqGEmxsA1UrQbmL8oyv1Z9WwrsyeKbkr8nMQ38glz3mo1d5IljTCZPKjYtL0lPI+hy5SPrRsf+ulivQCd3OZDjXGY1VsXORvEHjIMUFHcDmafD64AWSyBBNVWK0HEmKJdfu9vlCLGj/fzx5PPvkkZs2ahZtuugmrVq3CuHHjMGnSJGzatKmnhyYIgiAIQi/kkPAA3X333fj2t7+N73znOwCAefPm4bnnnsMvfvELzJ07d6+3o7o1VBD4RFyKvQZAXM0EAKbop4wiJgZWQ/Lf5MkliaoCXNWD06fpTmvUOG+QSzT0rmFjAJVol1TqCwGJHohL2GPy+AG787d3PBpVSdJxYmb8NK4cST43R7FXDIBO+kU19Ckhn0ja98sXkQ+Syo3AV391J13E++UD7G6IJ8G21NjdlYOxnam7grhHF4CgqL2gH+sbxVuDOAFFl4+tvFue6SZBwSVEuzvdCKDQ3nUqn9xt+2fsQZeJC75p5r3Lxm2LkO0Ech3J3b3tHt0Z+Q7Su0Po3Ynnp2wHGwD9YoEWxQTXYHVvWNUTBcyDo2voAGV4tYpKiffZR+fd8Dm58CKFxLwTBjoRjlRAlXeiCudNqrGAgRMiVBF5D4bbJnnPVVC9OiJy3wnllRdFdJpFbD2lUuE77wGKH8MCUO6fvJb3r9sE4NirareV9hbZ/UyLNlpvE1Cp/2MyzAsBH+6yHsH8tiKC/8WuQlUqA7bPVORcld5VEmggm7SiyWZcsjgx0R7vPTPIJM+NTQrOaQSJtzXKsl5hPOzJPEGpOWQilHZ9J56IGmF05fc7bgeS7H/Jh85Sjgxb2VXWoKzVtPLjcqcmgheEtXPNik2InbtI+WXs+FTkRUnJsKpRq6lU0dXC/taCEhC6Kt5kUdLxcVk8eL4DCYEdOD7zBlCpVMLKlStx4403pl6fOHEili9fXnOdYrGIYtE3s9u5c2d6AXu8MAOH/wBTyqcsxAQAXJGZWBgF8KX0ViAvyivnuo1yPqTie1v5s1Hc34hfoO3nspOU8sv6Ff34fF6Qb95qRdLCfsb1e9KFENkGa+CU0L8Qz9XgfHzSzgURMjWUSQ07e9r3Q9IuT8pSKmXctTAoKdeoM9vhQ0i8bxqv3qlsOsr7E3Ejju+7P1Pyi1j6BGpX8MYBM6xs6W/I8gt2x4YPEDdSdIaPrewqGX9h0gDlk4tYIbZSKVAgKw6X1b6aSfHvk4XzeGluhdBhlGPHAa/gY/3WeKVRpVKuMiw0EiiffxVRVcVMbBywEIIdog3HVRgPOrIVa4YJMDKjqNbJ2e43kS//1/67cQ1j2SpRwEQs876SJ7T5HAV/zEd9jKtCpIyXWFBMqRyVCS5Q7iJIoYayhnC3dga8Zvl+9jjOdpLL/7JVf6psvCHczZpq2v3OZl35PzLMEOZGcUWel9tEEjrLJOFyKmrXuFXnA58PlFH+d1WRT2h3t9KIpEzaUHVVYDZk5BUaYuOX5/vk/XMgHSIDKZiKG59UqDZK39hY+DHtwtVM9dkd38y4jasc7fGdvFaRqpDqjeb6M7LP58f9wUBCYAeMz7wBtG3bNkRRhGHDhqVeHzZsGLZs2VJznblz5+LWW2+tet10d8c5qBXy/gDSBpDxzz/SAKrchj2nWsOmqAB7/ivCP7c/XHZnsr8GUK3GqxHFJcyAz2UwGQPjMjdDREkb7ygqI0w6H5fD+IylgqimNDs3gMrJ3WxECmFi2EVRUrrepWDsxaA7g8jm0hQBZU+m3ACyd9YG7kTpHsk/T0kFwM+H+z4UUqW5Ls+oxircALLJ0BTCazgV/Qlcl3zOBpKLO4WRv9CHUZV+C5ECJZdwozTIqubuyQCy6xnl886S4zBSftmUAWS9lgQnrcMNIDvHKHmjAmUDsrk65mMMID5hzChKG0DJaqFhCf37YADZfYWGUWlvLEEhSgYQBRr+XkW5Y8YeGpFy6Vsw2rjGwZ/UAFJFDXTb33OyJDuOdYkQlv1xEO930XcTNzUMIBP5H6YKnIeSdABS1lOSzEHFzUWlthORhkmM0ygIENl2IKRgkD6OKnfXGUDWo0LK3biYyjY++GgDqHJZaHacRvDeWq6r9jEGkCP066uInTus1yf0r8Xvf4QBZOCPKcXOk3a3CDCGYLrjO9eD4V0JUa4+HPd1faEmn3kDyKIqdFCIqOo1y49+9CPMnj3b/f/+++9j9OjR2Dzn/w7oGAVBEIRDh46ODgwcOPCAbDuXy6GxsRHLtiz4xNtqbGxELpf7+AXrjM+8ATRkyBAEQVDl7dm6dWuVV8iSz+eRz+fd//369cMbb7yB0aNH47333sOAAQMO6JgPNXbu3Inhw4fL3O0nMn/7j8zd/iNzt/8QETo6OtDc3HzAPqNQKGDDhg0ocZmM/SSXy6FQKHwKozq0+MwbQLlcDmPGjMGiRYswZcoU9/qiRYtw4YUX7tU2tNY48sgjAQADBgyQk8F+InP3yZD5239k7vYfmbv940B5fjiFQkEMlwPIZ94AAoDZs2fj8ssvx+mnn46xY8fioYcewqZNmzB9+vSeHpogCIIgCL2QQ8IAmjZtGrZv347bbrsN7e3taGlpwYIFCzBixIieHpogCIIgCL2QQ8IAAoBrrrkG11xzzX6vn8/nMWfOnFRukLB3yNx9MmT+9h+Zu/1H5k6odxSJSpIgCIIgCHXGIdEKQxAEQRAEYV8QA0gQBEEQhLpDDCBBEARBEOoOMYAEQRAEQag7xAACcP/992PUqFEoFAoYM2YMXnrppZ4eUq/jlltugVIq9dfY2OjeJyLccsstaG5uRkNDAyZMmIC1a9f24Ih7lr/85S+44IIL0NzcDKUUnnnmmdT7ezNfxWIR1113HYYMGYK+ffvi61//OjZv3nwQ96Jn+Li5u/LKK6uOxa985SupZep17ubOnYszzjgD/fv3x9ChQ3HRRRdh3bp1qWXk2BOEmLo3gJ588knMmjULN910E1atWoVx48Zh0qRJ2LRpU08Prddx4oknor293f2tWbPGvffTn/4Ud999N+677z6sWLECjY2NOPfcc9HR0dGDI+45Ojs7cfLJJ+O+++6r+f7ezNesWbPw9NNPY/78+Vi2bBl27dqFyZMnu6ayhyofN3cAcP7556eOxQUL0v2S6nXu2tracO211+Lll1/GokWLEIYhJk6ciM7OTreMHHuCkEB1zpe+9CWaPn166rXjjz+ebrzxxh4aUe9kzpw5dPLJJ9d8zxhDjY2NdPvtt7vXuru7aeDAgfTAAw8cpBH2XgDQ008/7f7fm/n63//+R9lslubPn++Wef/990lrTQsXLjxoY+9pKueOiOiKK66gCy+8cI/ryNx5tm7dSgCora2NiOTYEwROXXuASqUSVq5ciYkTJ6ZenzhxIpYvX95Do+q9rF+/Hs3NzRg1ahS+8Y1v4J133gEAbNiwAVu2bEnNYz6fR2trq8xjDfZmvlauXIlyuZxaprm5GS0tLTKnAJYuXYqhQ4fi2GOPxVVXXYWtW7e692TuPB9++CEA4LDDDgMgx54gcOraANq2bRuiKKrqGj9s2LCq7vL1zpe//GU89thjeO655/Dwww9jy5YtOPPMM7F9+3Y3VzKPe8fezNeWLVuQy+UwePDgPS5Tr0yaNAlPPPEEFi9ejLvuugsrVqzA2WefjWKxCEDmzkJEmD17Ns466yy0tLQAkGNPEDiHTCuMT4JSKvU/EVW9Vu9MmjTJPT/ppJMwduxYfO5zn8OvfvUrl4Aq87hv7M98yZzGvf8sLS0tOP300zFixAj86U9/wtSpU/e4Xr3N3YwZM/D6669j2bJlVe/JsScIde4BGjJkCIIgqLqr2bp1a9UdkpCmb9++OOmkk7B+/XpXDSbzuHfszXw1NjaiVCphx44de1xGiGlqasKIESOwfv16ADJ3AHDdddfh2WefxZIlS3DUUUe51+XYEwRPXRtAuVwOY8aMwaJFi1KvL1q0CGeeeWYPjeqzQbFYxJtvvommpiaMGjUKjY2NqXkslUpoa2uTeazB3szXmDFjkM1mU8u0t7fjn//8p8xpBdu3b8d7772HpqYmAPU9d0SEGTNm4KmnnsLixYsxatSo1Pty7AkCo8fSr3sJ8+fPp2w2S4888gi98cYbNGvWLOrbty9t3Lixp4fWq7jhhhto6dKl9M4779DLL79MkydPpv79+7t5uv3222ngwIH01FNP0Zo1a+jSSy+lpqYm2rlzZw+PvGfo6OigVatW0apVqwgA3X333bRq1Sp69913iWjv5mv69Ol01FFH0QsvvECvvfYanX322XTyySdTGIY9tVsHhY+au46ODrrhhhto+fLltGHDBlqyZAmNHTuWjjzySJk7Ivrud79LAwcOpKVLl1J7e7v76+rqcsvIsScIMXVvABER/fznP6cRI0ZQLpej0047zZWMCp5p06ZRU1MTZbNZam5upqlTp9LatWvd+8YYmjNnDjU2NlI+n6fx48fTmjVrenDEPcuSJUsIQNXfFVdcQUR7N1+7d++mGTNm0GGHHUYNDQ00efJk2rRpUw/szcHlo+auq6uLJk6cSEcccQRls1k6+uij6Yorrqial3qdu1rzBoAeffRRt4wce4IQo4iIDrbXSRAEQRAEoSep6xwgQRAEQRDqEzGABEEQBEGoO8QAEgRBEASh7hADSBAEQRCEukMMIEEQBEEQ6g4xgARBEARBqDvEABIEQRAEoe4QA0gQBEEQhLpDDCBBqHMmTJiAWbNm9fQwUiil8Mwzz/T0MARBOIQRA0gQBEEQhLpDDCBB6IX84Q9/wKBBg2CMAQCsXr0aSin84Ac/cMtcffXVuPTSSwEAy5cvx/jx49HQ0IDhw4dj5syZ6OzsdMvef//9+MIXvoBCoYBhw4bh4osvBgBceeWVaGtrwz333AOlFJRS2Lhx48eO74033sDXvvY19OvXD8OGDcPll1+Obdu2ufcXLlyIs846C4MGDcLhhx+OyZMn4+2333bvl0olzJgxA01NTSgUChg5ciTmzp0LABg5ciQAYMqUKVBKuf8FQRA+TcQAEoReyPjx49HR0YFVq1YBANra2jBkyBC0tbW5ZZYuXYrW1lasWbMG5513HqZOnYrXX38dTz75JJYtW4YZM2YAAF599VXMnDkTt912G9atW4eFCxdi/PjxAIB77rkHY8eOxVVXXYX29na0t7dj+PDhHzm29vZ2tLa24pRTTsGrr76KhQsX4j//+Q8uueQSt0xnZydmz56NFStW4MUXX4TWGlOmTHEG3b333otnn30Wv/nNb7Bu3To8/vjjztBZsWIFAODRRx9Fe3u7+18QBOHTRJqhCkIvZcyYMbjssstwww03YMqUKTjjjDNw6623Ytu2bejs7ERTUxPefPNN/OQnP0FDQwMefPBBt+6yZcvQ2tqKzs5OLFiwAN/85jexefNm9O/fv+pzJkyYgFNOOQXz5s3bq3HdfPPNeOWVV/Dcc8+51zZv3ozhw4dj3bp1OPbYY6vW+e9//4uhQ4dizZo1aGlpwcyZM7F27Vq88MILUEpVLa+UwtNPP42LLrpor8YkCIKwr4gHSBB6KRMmTMDSpUtBRHjppZdw4YUXoqWlBcuWLcOSJUswbNgwHH/88Vi5ciV++ctfol+/fu7vvPPOgzEGGzZswLnnnosRI0bgmGOOweWXX44nnngCXV1d+z2ulStXYsmSJanPO/744wHAhbnefvttXHbZZTjmmGMwYMAAjBo1CgCwadMmAHHobfXq1TjuuOMwc+ZMPP/8859wtgRBEPaNTE8PQBCE2kyYMAGPPPII/vGPf0BrjdGjR6O1tRVtbW3YsWMHWltbAQDGGFx99dWYOXNm1TaOPvpo5HI5vPbaa1i6dCmef/553HzzzbjllluwYsUKDBo0aJ/HZYzBBRdcgDvuuKPqvaamJgDABRdcgOHDh+Phhx9Gc3MzjDFoaWlBqVQCAJx22mnYsGED/vznP+OFF17AJZdcgnPOOQe/+93v9nk8giAI+4MYQILQS7F5QPPmzUNrayuUUmhtbcXcuXOxY8cOXH/99QBiY2Lt2rX4/Oc/v8dtZTIZnHPOOTjnnHMwZ84cDBo0CIsXL8bUqVORy+UQRdFej+u0007D73//e4wcORKZTPUpZPv27XjzzTfx4IMPYty4cQDikFwlAwYMwLRp0zBt2jRcfPHFOP/88/HBBx/gsMMOQzab3acxCYIg7CsSAhOEXsrAgQNxyimn4PHHH8eECRMAxEbRa6+9hrfeesu99sMf/hB/+9vfcO2112L16tVYv349nn32WVx33XUAgD/+8Y+49957sXr1arz77rt47LHHYIzBcccdByCuunrllVewceNGbNu2zSUq74lrr70WH3zwAS699FL8/e9/xzvvvIPnn38e3/rWtxBFEQYPHozDDz8cDz30EP79739j8eLFmD17dmobP/vZzzB//nz861//wltvvYXf/va3aGxsdB6pkSNH4sUXX8SWLVuwY8eOT29SBUEQEsQAEoRezFe/+lVEUeSMncGDB2P06NE44ogjcMIJJwAAvvjFL6KtrQ3r16/HuHHjcOqpp+LHP/6xC0cNGjQITz31FM4++2yccMIJeOCBB/DrX/8aJ554IgDg+9//PoIgcNu1eTp7orm5GX/9618RRRHOO+88tLS04Prrr8fAgQOhtYbWGvPnz8fKlSvR0tKC733ve7jzzjtT2+jXrx/uuOMOnH766TjjjDOwceNGLFiwAFrHp6S77roLixYtwvDhw3Hqqad+mlMqCIIAQKrABEEQBEGoQ8QDJAiCIAhC3SEGkCAIKaZPn54qced/06dP7+nhCYIgfCpICEwQhBRbt27Fzp07a743YMAADB069CCPSBAE4dNHDCBBEARBEOoOCYEJgiAIglB3iAEkCIIgCELdIQaQIAiCIAh1hxhAgiAIgiDUHWIACYIgCIJQd4gBJAiCIAhC3SEGkCAIgiAIdYcYQIIgCIIg1B3/D9dVB1TmsLs1AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# By default, xarray does not interpret the wrfout/wrfstat time information in a way that attaches \n",
+    "# it to each variable. Here is at trick to map the time held in XTIME with the Time coordinate \n",
+    "# associated with each variable.\n",
+    "ds_stat[\"Time\"] = ds_stat[\"XTIME\"]\n",
+    "\n",
+    "# Now that we fixed the time coordinate, we can use xarray's plotting features to get time-labeled plots.\n",
+    "\n",
+    "hour_to_plot = 17\n",
+    "\n",
+    "# Time series:\n",
+    "ds_stat[\"CST_LWP\"].plot()\n",
+    "plt.show()\n",
+    "\n",
+    "# Profile at a selected time (plots sideways, though, since we are being lazy):\n",
+    "ds_stat[\"CSP_LWC\"].sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\").plot()\n",
+    "plt.show()\n",
+    "\n",
+    "# X-Y slice for a selected time:\n",
+    "ds_stat[\"CSS_LWP\"].sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\").plot()\n",
+    "plt.show()\n",
+    "\n",
+    "# A vertical slice from the volume at a selected time:\n",
+    "# We'll assign the vertical coordinate values for this one and hide the cloud-free upper atmosphere.\n",
+    "plot_data = ds_stat[\"CSV_LWC\"].assign_coords(height=(ds_stat[\"CSP_Z\"]))\n",
+    "plot_data.sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\", south_north=1).plot(y=\"height\", ylim=[0, 1500])\n",
+    "plt.show()\n",
+    "\n",
+    "# Add lines and modify variables to plot desired figures... In this notebook, we plotted "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a2777ed-b10a-479f-bcfa-81defe6078a4",
+   "metadata": {},
+   "source": [
+    "# Plot wind speed "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "272ce2e6-d330-4fe3-8e6e-e0b2716a786d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Open a wrfout time series using a manual approach...\n",
+    "\n",
+    "case_date = datetime(2019, 4, 4)\n",
+    "sim_id = 4\n",
+    "\n",
+    "# Note the extra details required by open_mfdataset to connect the files together in time.\n",
+    "ds_wrf = xr.open_mfdataset(f\"{path_shcu_root}/{case_date:%Y%m%d}/sim{sim_id:04d}/raw_model/wrfout_d01_*.nc\", \n",
+    "                           combine=\"nested\", concat_dim=\"Time\")\n",
+    "\n",
+    "ds_wrf[\"Time\"] = ds_wrf[\"XTIME\"]  # Fix the time coordinate like we did for the wrfstat file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "17bce5d8-a8bb-454a-ad16-9734ea084e1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot wind vectors at a selected level to demonstrate how to destagger the wind components to cell-center values with xarray...\n",
+    "\n",
+    "plot_level = 12  # index of level to plot\n",
+    "skip_xy = 10  # Sampling interval for the vector thinning\n",
+    "\n",
+    "nt, nz, ny, nx = ds_wrf[\"T\"].shape\n",
+    "\n",
+    "# We need to:\n",
+    "# 1) destagger to cell centers\n",
+    "# 2) rename the staggered dimension back to the non-staggered name to avoid dimension conflicts\n",
+    "# 3) (re)name the unstaggered wind for convenience\n",
+    "# Then, we are able to put these new DataArrays back into the ds_wrf Dataset.\n",
+    "\n",
+    "ds_wrf[\"UA\"] = 0.5*( ds_wrf[\"U\"].isel(west_east_stag=slice(0, nx)) + \n",
+    "                    ds_wrf[\"U\"].shift(west_east_stag=-1).isel(west_east_stag=slice(0, nx)) ).\\\n",
+    "                    rename(\"UA\").rename(west_east_stag=\"west_east\")\n",
+    "ds_wrf[\"VA\"] = 0.5*( ds_wrf[\"V\"].isel(south_north_stag=slice(0, ny)) + \n",
+    "                    ds_wrf[\"V\"].shift(south_north_stag=-1).isel(south_north_stag=slice(0, ny)) ).\\\n",
+    "                    rename(\"VA\").rename(south_north_stag=\"south_north\")\n",
+    "ds_wrf[\"SPD\"] = np.sqrt(ds_wrf[\"UA\"]**2 + ds_wrf[\"VA\"]**2).rename(\"wind speed\").\\\n",
+    "                    assign_attrs(units=\"m s-1\", description=\"wind speed\")\n",
+    "\n",
+    "# Now, we can proceed to more plotting-specific data manipulation. We need to\n",
+    "# add spatial variables for the idealized domain (since XLAT and XLONG are\n",
+    "# constant in the file). This is needed by the xarray quiver routine.\n",
+    "# Then, thin the grid to reduce the number of arrrows.\n",
+    "ds_wrf[\"west_east\"] = xr.DataArray(data=np.arange(nx)*ds_wrf.attrs[\"DX\"], dims=\"west_east\", name=\"west_east\", attrs={\"units\": \"m\"})\n",
+    "ds_wrf[\"south_north\"] = xr.DataArray(data=np.arange(ny)*ds_wrf.attrs[\"DX\"], dims=\"south_north\", name=\"south_north\", attrs={\"units\": \"m\"})\n",
+    "\n",
+    "ds_wrf_thinned = ds_wrf.\\\n",
+    "    isel(west_east=slice(0, nx, skip_xy), south_north=slice(0, ny, skip_xy), bottom_top=plot_level).\\\n",
+    "    sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\")\n",
+    "\n",
+    "fig, ax = plt.subplots(ncols=1)\n",
+    "ds_wrf_thinned[\"SPD\"].plot(ax=ax, x=\"west_east\", y=\"south_north\")\n",
+    "ds_wrf_thinned.plot.quiver(ax=ax, x=\"west_east\", y=\"south_north\", u=\"UA\", v=\"VA\",\n",
+    "                           scale=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "051ffaeb-ea4d-4bb4-b0bd-6b730a143423",
+   "metadata": {},
+   "source": [
+    "# Compare with Observational Data from ARM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "74b1961f-6f2d-4347-97a6-436c749e3a3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.11/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in log10\n",
+      "  return func(*(_execute_task(a, cache) for a in args))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compare with ARM Observational Data\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "from arm_test_data import DATASETS\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import act\n",
+    "\n",
+    "# Place your username and token here\n",
+    "username = os.getenv('ARM_USERNAME')\n",
+    "token = os.getenv('ARM_PASSWORD')\n",
+    "\n",
+    "# If the username and token are not set, use the existing sample file\n",
+    "if username is None or token is None or len(username) == 0 or len(token) == 0:\n",
+    "    filename_ceil = DATASETS.fetch('sgpceilC1.b1.20190101.000000.nc')\n",
+    "    ceil_ds = act.io.arm.read_arm_netcdf(filename_ceil, engine='netcdf4')\n",
+    "else:\n",
+    "    # Example to show how easy it is to download ARM data if a username/token are set\n",
+    "    results = act.discovery.download_arm_data(\n",
+    "        username, token, 'sgpceilC1.b1', '2022-01-14', '2022-01-19'\n",
+    "    )\n",
+    "    ceil_ds = act.io.arm.read_arm_netcdf(results)\n",
+    "\n",
+    "# Adjust ceilometer data for plotting\n",
+    "ceil_ds = act.corrections.ceil.correct_ceil(ceil_ds, -9999.0)\n",
+    "\n",
+    "# Plot up ceilometer backscatter using HomeyerRainbow CVD friendly colormap\n",
+    "# The same could be done with keyword 'cmap='HomeyerRainbow'\n",
+    "display = act.plotting.TimeSeriesDisplay(ceil_ds, subplot_shape=(1,), figsize=(15, 5))\n",
+    "display.plot('backscatter', subplot_index=(0,), cvd_friendly=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ce7953c0-ad0e-4d30-aaa9-204f997982fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ARM Plotting v2\n",
+    "import act\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7424d9a-3f55-48a5-b417-3c52009e91f9",
+   "metadata": {},
+   "source": [
+    "# Plot Desired Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4d971989-9a86-4ef5-9187-8be6a05b973f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[DOWNLOADING] sgpaosmetE13.a1.20190404.000000.nc\n",
+      "\n",
+      "If you use these data to prepare a publication, please cite:\n",
+      "\n",
+      "Kyrouac, J., Springston, S., & Tuftedal, M. Meteorological Measurements\n",
+      "associated with the Aerosol Observing System (AOSMET). Atmospheric Radiation\n",
+      "Measurement (ARM) User Facility. https://doi.org/10.5439/1984920\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set your username and token here!\n",
+    "username = 'xmansoura'\n",
+    "token = '77da82486bcd2f4b'\n",
+    "\n",
+    "# Set the datastream and start/enddates\n",
+    "datastream = 'sgpaosmetE13.a1'\n",
+    "startdate = '2019-04-04'\n",
+    "enddate = '2019-04-05'\n",
+    "\n",
+    "# Use ACT to easily download the data.  Watch for the data citation!  Show some support\n",
+    "# for ARM's instrument experts and cite their data if you use it in a publication\n",
+    "result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fe391358-5d0a-4477-94bf-acf8e268afe8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 6MB\n",
+       "Dimensions:              (time: 86399)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 691kB 2019-04-04T00:00:00.2200...\n",
+       "Data variables: (12/17)\n",
+       "    base_time            datetime64[ns] 8B 2019-04-04\n",
+       "    time_offset          (time) datetime64[ns] 691kB 2019-04-04T00:00:00.2200...\n",
+       "    rh_ambient           (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
+       "    temperature_ambient  (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
+       "    pressure_ambient     (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
+       "    wind_speed           (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
+       "    ...                   ...\n",
+       "    heater_volts         (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
+       "    supply_volts         (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
+       "    ref_volts            (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
+       "    lat                  float32 4B ...\n",
+       "    lon                  float32 4B ...\n",
+       "    alt                  float32 4B ...\n",
+       "Attributes: (12/23)\n",
+       "    command_line:                aosmet_ingest -s sgp -f E13\n",
+       "    Conventions:                 ARM-1.2\n",
+       "    process_version:             ingest-aosmet-1.0-0.el6\n",
+       "    dod_version:                 aosmet-a1-2.2\n",
+       "    input_source:                /data/collection/sgp/sgpaosmetE13.00/sgpaosE...\n",
+       "    site_id:                     sgp\n",
+       "    ...                          ...\n",
+       "    doi:                         10.5439/1325987\n",
+       "    history:                     created by user dsmgr on machine ruby at 201...\n",
+       "    _file_dates:                 [&#x27;20190404&#x27;]\n",
+       "    _file_times:                 [&#x27;000000&#x27;]\n",
+       "    _datastream:                 sgpaosmetE13.a1\n",
+       "    _arm_standards_flag:         1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-74d52018-3d8b-47ba-b0d0-14ecd0240c37' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-74d52018-3d8b-47ba-b0d0-14ecd0240c37' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 86399</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a5090697-29e9-4a22-bbe6-6df46301870e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5090697-29e9-4a22-bbe6-6df46301870e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-04-04T00:00:00.220000 ... 2...</div><input id='attrs-906c1552-867b-484f-9074-2dab8b21bc0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-906c1552-867b-484f-9074-2dab8b21bc0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43d335de-19e9-40cf-a10a-1896dbd52361' class='xr-var-data-in' type='checkbox'><label for='data-43d335de-19e9-40cf-a10a-1896dbd52361' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from midnight</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-04-04T00:00:00.220000000&#x27;, &#x27;2019-04-04T00:00:01.220000000&#x27;,\n",
+       "       &#x27;2019-04-04T00:00:02.220000000&#x27;, ..., &#x27;2019-04-04T23:59:57.160000000&#x27;,\n",
+       "       &#x27;2019-04-04T23:59:58.160000000&#x27;, &#x27;2019-04-04T23:59:59.160000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e06f3f6-234c-47c3-8df1-2d4e515bd3f7' class='xr-section-summary-in' type='checkbox'  ><label for='section-8e06f3f6-234c-47c3-8df1-2d4e515bd3f7' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>base_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-04-04</div><input id='attrs-5a524090-0b41-47df-8fd2-d930baaa81ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a524090-0b41-47df-8fd2-d930baaa81ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37bcfb9f-cb5e-4c6b-98f3-ebbaa28093bb' class='xr-var-data-in' type='checkbox'><label for='data-37bcfb9f-cb5e-4c6b-98f3-ebbaa28093bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>string :</span></dt><dd>2019-04-04 00:00:00 0:00</dd><dt><span>long_name :</span></dt><dd>Base time in Epoch</dd><dt><span>ancillary_variables :</span></dt><dd>time_offset</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2019-04-04T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-04-04T00:00:00.220000 ... 2...</div><input id='attrs-379a9a85-346e-4070-8b19-0d93dcc01a0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-379a9a85-346e-4070-8b19-0d93dcc01a0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ab84c9d-5018-42e1-86c9-07aad83ec044' class='xr-var-data-in' type='checkbox'><label for='data-9ab84c9d-5018-42e1-86c9-07aad83ec044' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from base_time</dd><dt><span>ancillary_variables :</span></dt><dd>base_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-04-04T00:00:00.220000000&#x27;, &#x27;2019-04-04T00:00:01.220000000&#x27;,\n",
+       "       &#x27;2019-04-04T00:00:02.220000000&#x27;, ...,\n",
+       "       &#x27;2019-04-04T23:59:57.160000000&#x27;, &#x27;2019-04-04T23:59:58.160000000&#x27;,\n",
+       "       &#x27;2019-04-04T23:59:59.160000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rh_ambient</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-37963cf6-0376-44c7-a20d-af8d51275cb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37963cf6-0376-44c7-a20d-af8d51275cb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-897ae827-4cfa-443c-99b2-c2ad92246b8e' class='xr-var-data-in' type='checkbox'><label for='data-897ae827-4cfa-443c-99b2-c2ad92246b8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Ambient air relative humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_ambient</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-e2af20fd-9aac-4702-aa7c-6feb4c822b72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2af20fd-9aac-4702-aa7c-6feb4c822b72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3799ad90-6ea9-4a68-9cd4-c45a61b3dfcd' class='xr-var-data-in' type='checkbox'><label for='data-3799ad90-6ea9-4a68-9cd4-c45a61b3dfcd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Ambient air temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_ambient</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-a5ab369b-715c-4b13-836f-79f9c7eb8d62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5ab369b-715c-4b13-836f-79f9c7eb8d62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abaeaa6e-c45b-4ba3-82a9-53d83a680025' class='xr-var-data-in' type='checkbox'><label for='data-abaeaa6e-c45b-4ba3-82a9-53d83a680025' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Ambient pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-573c41bf-07f9-40ea-a68a-dfa5e0610e1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-573c41bf-07f9-40ea-a68a-dfa5e0610e1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fd6984a-97ea-44bf-9c44-3a7af1a5abf6' class='xr-var-data-in' type='checkbox'><label for='data-6fd6984a-97ea-44bf-9c44-3a7af1a5abf6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_direction</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-85da6db0-422d-4b1b-82c9-2c31be7a586f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85da6db0-422d-4b1b-82c9-2c31be7a586f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6d9615d-3ca4-4a05-aef7-cb8d16611c86' class='xr-var-data-in' type='checkbox'><label for='data-b6d9615d-3ca4-4a05-aef7-cb8d16611c86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind direction, relative to true North</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_amount</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-bda0742b-f421-4dbc-b282-bb8642990f9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bda0742b-f421-4dbc-b282-bb8642990f9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22e56553-8944-448d-a518-6de5d5435ded' class='xr-var-data-in' type='checkbox'><label for='data-22e56553-8944-448d-a518-6de5d5435ded' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rain amount</dd><dt><span>units :</span></dt><dd>mm/s</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_duration</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-f14b264a-b10a-461e-ac73-820f18c5970f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f14b264a-b10a-461e-ac73-820f18c5970f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5a92771-e24d-4424-832f-89e7cfc3f0d3' class='xr-var-data-in' type='checkbox'><label for='data-d5a92771-e24d-4424-832f-89e7cfc3f0d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rain duration</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_intensity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-1ae9b151-88e2-4489-abe6-832bf1fac511' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ae9b151-88e2-4489-abe6-832bf1fac511' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb252542-78d2-4f24-9e9f-a77015ade7ad' class='xr-var-data-in' type='checkbox'><label for='data-eb252542-78d2-4f24-9e9f-a77015ade7ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rain intensity</dd><dt><span>units :</span></dt><dd>mm/hr</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heater_temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-cc2d547e-5bc8-4e74-9520-f12bcbfa01c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc2d547e-5bc8-4e74-9520-f12bcbfa01c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0adabb9-2958-4a49-a88a-e7a65a768107' class='xr-var-data-in' type='checkbox'><label for='data-b0adabb9-2958-4a49-a88a-e7a65a768107' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Heater temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heater_volts</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-5bf2af8e-ba31-48f6-bbad-0a7bfc8ea811' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bf2af8e-ba31-48f6-bbad-0a7bfc8ea811' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bdb9de8-3642-49dc-995b-bd1f832f762e' class='xr-var-data-in' type='checkbox'><label for='data-0bdb9de8-3642-49dc-995b-bd1f832f762e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Heater voltage</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>supply_volts</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-b2e2952d-6e33-4b92-91c1-08e2de7e136a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2e2952d-6e33-4b92-91c1-08e2de7e136a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdc6b666-2543-4362-9731-5b945a1ded39' class='xr-var-data-in' type='checkbox'><label for='data-fdc6b666-2543-4362-9731-5b945a1ded39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Supply voltage</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ref_volts</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-0dcb5811-d90b-42cc-9756-f6ac1bcd3cdc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dcb5811-d90b-42cc-9756-f6ac1bcd3cdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28c1ba5d-7d1b-47bf-a54d-28ee7bf0181e' class='xr-var-data-in' type='checkbox'><label for='data-28c1ba5d-7d1b-47bf-a54d-28ee7bf0181e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Reference voltage</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-28d1cbca-2c5f-4a50-be7b-ef5be23800d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28d1cbca-2c5f-4a50-be7b-ef5be23800d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a56a69c4-3388-42c6-8c35-fd0696b4bfc0' class='xr-var-data-in' type='checkbox'><label for='data-a56a69c4-3388-42c6-8c35-fd0696b4bfc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>North latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5a3addc1-61aa-49b5-9a87-1967860d0773' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a3addc1-61aa-49b5-9a87-1967860d0773' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6230178-795f-4d5c-8ec7-50b0a327b9bd' class='xr-var-data-in' type='checkbox'><label for='data-b6230178-795f-4d5c-8ec7-50b0a327b9bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>East longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d677496-ccd2-4272-baad-7b96de4e86c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d677496-ccd2-4272-baad-7b96de4e86c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d236539f-82e9-4b30-9ce3-0b278a654261' class='xr-var-data-in' type='checkbox'><label for='data-d236539f-82e9-4b30-9ce3-0b278a654261' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude above mean sea level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>altitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6d3a2a0-34c7-4e55-89c8-85241265cedf' class='xr-section-summary-in' type='checkbox'  ><label for='section-b6d3a2a0-34c7-4e55-89c8-85241265cedf' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-496d55c8-29ff-4c2a-bc26-918a33058616' class='xr-index-data-in' type='checkbox'/><label for='index-496d55c8-29ff-4c2a-bc26-918a33058616' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-04-04 00:00:00.220000&#x27;, &#x27;2019-04-04 00:00:01.220000&#x27;,\n",
+       "               &#x27;2019-04-04 00:00:02.220000&#x27;, &#x27;2019-04-04 00:00:03.220000&#x27;,\n",
+       "               &#x27;2019-04-04 00:00:04.220000&#x27;, &#x27;2019-04-04 00:00:05.220000&#x27;,\n",
+       "               &#x27;2019-04-04 00:00:06.220000&#x27;, &#x27;2019-04-04 00:00:07.220000&#x27;,\n",
+       "               &#x27;2019-04-04 00:00:08.220000&#x27;, &#x27;2019-04-04 00:00:09.220000&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2019-04-04 23:59:50.160000&#x27;, &#x27;2019-04-04 23:59:51.160000&#x27;,\n",
+       "               &#x27;2019-04-04 23:59:52.160000&#x27;, &#x27;2019-04-04 23:59:53.160000&#x27;,\n",
+       "               &#x27;2019-04-04 23:59:54.160000&#x27;, &#x27;2019-04-04 23:59:55.160000&#x27;,\n",
+       "               &#x27;2019-04-04 23:59:56.160000&#x27;, &#x27;2019-04-04 23:59:57.160000&#x27;,\n",
+       "               &#x27;2019-04-04 23:59:58.160000&#x27;, &#x27;2019-04-04 23:59:59.160000&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=86399, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b98def3e-74d6-4bea-ba52-682a3ad768dd' class='xr-section-summary-in' type='checkbox'  ><label for='section-b98def3e-74d6-4bea-ba52-682a3ad768dd' class='xr-section-summary' >Attributes: <span>(23)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>command_line :</span></dt><dd>aosmet_ingest -s sgp -f E13</dd><dt><span>Conventions :</span></dt><dd>ARM-1.2</dd><dt><span>process_version :</span></dt><dd>ingest-aosmet-1.0-0.el6</dd><dt><span>dod_version :</span></dt><dd>aosmet-a1-2.2</dd><dt><span>input_source :</span></dt><dd>/data/collection/sgp/sgpaosmetE13.00/sgpaosE13.met.01s.00.20190404.000000.raw.tsv</dd><dt><span>site_id :</span></dt><dd>sgp</dd><dt><span>platform_id :</span></dt><dd>aosmet</dd><dt><span>facility_id :</span></dt><dd>E13</dd><dt><span>data_level :</span></dt><dd>a1</dd><dt><span>location_description :</span></dt><dd>Southern Great Plains (SGP), Lamont, Oklahoma</dd><dt><span>datastream :</span></dt><dd>sgpaosmetE13.a1</dd><dt><span>serial_number :</span></dt><dd>K/WD</dd><dt><span>sampling_interval :</span></dt><dd>1 second</dd><dt><span>met_instrument_description :</span></dt><dd>WXT520</dd><dt><span>instrument_manufacturer :</span></dt><dd>N/A</dd><dt><span>mentor_name :</span></dt><dd>Kyrouac</dd><dt><span>mentor_affiliation :</span></dt><dd>ANL</dd><dt><span>doi :</span></dt><dd>10.5439/1325987</dd><dt><span>history :</span></dt><dd>created by user dsmgr on machine ruby at 2019-04-04 01:22:00, using ingest-aosmet-1.0-0.el6</dd><dt><span>_file_dates :</span></dt><dd>[&#x27;20190404&#x27;]</dd><dt><span>_file_times :</span></dt><dd>[&#x27;000000&#x27;]</dd><dt><span>_datastream :</span></dt><dd>sgpaosmetE13.a1</dd><dt><span>_arm_standards_flag :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 6MB\n",
+       "Dimensions:              (time: 86399)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 691kB 2019-04-04T00:00:00.2200...\n",
+       "Data variables: (12/17)\n",
+       "    base_time            datetime64[ns] 8B 2019-04-04\n",
+       "    time_offset          (time) datetime64[ns] 691kB 2019-04-04T00:00:00.2200...\n",
+       "    rh_ambient           (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
+       "    temperature_ambient  (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
+       "    pressure_ambient     (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
+       "    wind_speed           (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
+       "    ...                   ...\n",
+       "    heater_volts         (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
+       "    supply_volts         (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
+       "    ref_volts            (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
+       "    lat                  float32 4B ...\n",
+       "    lon                  float32 4B ...\n",
+       "    alt                  float32 4B ...\n",
+       "Attributes: (12/23)\n",
+       "    command_line:                aosmet_ingest -s sgp -f E13\n",
+       "    Conventions:                 ARM-1.2\n",
+       "    process_version:             ingest-aosmet-1.0-0.el6\n",
+       "    dod_version:                 aosmet-a1-2.2\n",
+       "    input_source:                /data/collection/sgp/sgpaosmetE13.00/sgpaosE...\n",
+       "    site_id:                     sgp\n",
+       "    ...                          ...\n",
+       "    doi:                         10.5439/1325987\n",
+       "    history:                     created by user dsmgr on machine ruby at 201...\n",
+       "    _file_dates:                 ['20190404']\n",
+       "    _file_times:                 ['000000']\n",
+       "    _datastream:                 sgpaosmetE13.a1\n",
+       "    _arm_standards_flag:         1"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's read in the data using ACT and check out the data\n",
+    "ds_mpl = act.io.read_arm_netcdf(result)\n",
+    "\n",
+    "ds_mpl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd198c76-4701-40f1-8668-7e56ee84fd52",
+   "metadata": {},
+   "source": [
+    "# Change variable (as desired), Apply QC, and Plot Again"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5fc6c74c-cfbf-4dea-ae5d-0e4810dad8ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'sgpaosmetE13.a1 temperature_ambient on 20190404'}, xlabel='Time [UTC]', ylabel='(degC)'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's take a look at the quality control information associated with a variable from the MPL\n",
+    "variable = 'temperature_ambient'\n",
+    "\n",
+    "# First, for many of the ACT QC features, we need to get the dataset more to CF standard and that\n",
+    "# involves cleaning up some of the attributes and ways that ARM has historically handled QC\n",
+    "ds_mpl.clean.cleanup()\n",
+    "\n",
+    "# Apply corrections for the ceilometer, correcting for the vertical height\n",
+    "#ds_mpl = act.corrections.ceil.correct_ceil(ds_mpl,-999.0)\n",
+    "\n",
+    "# Next, let's take a look at visualizing the quality control information\n",
+    "# Create a plotting display object with 2 plots\n",
+    "display = act.plotting.TimeSeriesDisplay(ds_mpl, figsize=(10, 5), subplot_shape=(1,))\n",
+    "\n",
+    "# # Plot up the variable in the first plot\n",
+    "display.plot(variable, subplot_index=(0,))\n",
+    "\n",
+    "# # Plot up the QC variable in the second plot\n",
+    "# #display.qc_flag_block_plot(variable, subplot_index=(1,))\n",
+    "# plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_config.yml b/_config.yml
index f2f6049..2773af6 100644
--- a/_config.yml
+++ b/_config.yml
@@ -8,7 +8,11 @@ copyright: "2024"
 
 execute:
   # To execute notebooks via a Binder instead, replace 'cache' with 'binder'
-  execute_notebooks: cache
+<<<<<<< HEAD
+  execute_notebooks: "off"
+=======
+  execute_notebooks: 'off'
+>>>>>>> c6e5e4140ae752390e8c68fc4d76ca4ac4ec0f04
   timeout: 600
   allow_errors: False # cells with expected failures must set the `raises-exception` cell tag
 
diff --git a/notebooks/Calc_cloud_base_and_top.ipynb b/notebooks/Calc_cloud_base_and_top.ipynb
new file mode 100644
index 0000000..9d2861e
--- /dev/null
+++ b/notebooks/Calc_cloud_base_and_top.ipynb
@@ -0,0 +1,1016 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clouds over SGP for April 4, 2019\n",
+    "\n",
+    "Looking at LASSO data for April 4, 2019 to see meterological data and calculate cloud base and cloud top."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import xwrf\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bring in the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is the raw model output from LASSO."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_shcu_root = \"/data/project/ARM_Summer_School_2024_Data/lasso_tutorial/ShCu/untar\"  # on Jupyter\n",
+    "\n",
+    "case_date = datetime(2019, 4, 4)\n",
+    "sim_id = 7\n",
+    "\n",
+    "ds_stat = xr.open_dataset(f\"{path_shcu_root}/{case_date:%Y%m%d}/sim{sim_id:04d}/raw_model/wrfstat_d01_{case_date:%Y-%m-%d_12:00:00}.nc\")\n",
+    "ds_stat\n",
+    "\n",
+    "ds_stat[\"Time\"] = ds_stat[\"XTIME\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find the indices of the boundary layer depth - in case we happen to care about that later"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;bottom_top&#x27; (Time: 91)&gt; Size: 728B\n",
+       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  1.,\n",
+       "        2.,  2.,  2.,  3.,  3.,  3.,  2.,  2.,  2.,  3.,  3.,  3.,  4.,\n",
+       "        4.,  4.,  5.,  5.,  5.,  5.,  5.,  6.,  6.,  7.,  7.,  7.,  7.,\n",
+       "        8., 10., 12., 13., 13., 12., 12., 12., 11., 11., 11., 10., 11.,\n",
+       "       11., 11., 10., 11., 10., 10., 10., 10., 10., 10., 10.,  9.,  9.,\n",
+       "        9.,  9.,  8.,  8.,  7.,  7.,  6.,  6.,  4.,  3.,  0.,  0.,  0.,\n",
+       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])\n",
+       "Coordinates:\n",
+       "    XTIME    (Time) datetime64[ns] 728B 2019-04-04T12:00:00 ... 2019-04-05T03...\n",
+       "Dimensions without coordinates: Time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'bottom_top'</div><ul class='xr-dim-list'><li><span>Time</span>: 91</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0647bcca-7fa4-4bec-b686-291ec26bf1f6' class='xr-array-in' type='checkbox' checked><label for='section-0647bcca-7fa4-4bec-b686-291ec26bf1f6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  1.,\n",
+       "        2.,  2.,  2.,  3.,  3.,  3.,  2.,  2.,  2.,  3.,  3.,  3.,  4.,\n",
+       "        4.,  4.,  5.,  5.,  5.,  5.,  5.,  6.,  6.,  7.,  7.,  7.,  7.,\n",
+       "        8., 10., 12., 13., 13., 12., 12., 12., 11., 11., 11., 10., 11.,\n",
+       "       11., 11., 10., 11., 10., 10., 10., 10., 10., 10., 10.,  9.,  9.,\n",
+       "        9.,  9.,  8.,  8.,  7.,  7.,  6.,  6.,  4.,  3.,  0.,  0.,  0.,\n",
+       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])</pre></div></div></li><li class='xr-section-item'><input id='section-bed4e92d-4d00-459e-b552-f948c358cc40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bed4e92d-4d00-459e-b552-f948c358cc40' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>XTIME</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-04-04T12:00:00 ... 2019-04-...</div><input id='attrs-a709751c-70ec-4a42-bfc1-6ec9374aad49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a709751c-70ec-4a42-bfc1-6ec9374aad49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20d4eba5-7833-4f5c-8d8c-ba1577199c0c' class='xr-var-data-in' type='checkbox'><label for='data-20d4eba5-7833-4f5c-8d8c-ba1577199c0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>minutes since 2019-04-04 12:00:00</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-04-04T12:00:00.000000000&#x27;, &#x27;2019-04-04T12:10:00.000000000&#x27;,\n",
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+       "       &#x27;2019-04-04T18:00:00.000000000&#x27;, &#x27;2019-04-04T18:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T18:20:00.000000000&#x27;, &#x27;2019-04-04T18:30:00.000000000&#x27;,\n",
+       "...\n",
+       "       &#x27;2019-04-04T20:40:00.000000000&#x27;, &#x27;2019-04-04T20:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T21:00:00.000000000&#x27;, &#x27;2019-04-04T21:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T21:20:00.000000000&#x27;, &#x27;2019-04-04T21:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T21:40:00.000000000&#x27;, &#x27;2019-04-04T21:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T22:00:00.000000000&#x27;, &#x27;2019-04-04T22:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T22:20:00.000000000&#x27;, &#x27;2019-04-04T22:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T22:40:00.000000000&#x27;, &#x27;2019-04-04T22:50:00.000000000&#x27;,\n",
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+       "       &#x27;2019-04-05T02:20:00.000000000&#x27;, &#x27;2019-04-05T02:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T02:40:00.000000000&#x27;, &#x27;2019-04-05T02:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T03:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-07d0cb83-409a-4718-9e52-04deb5247e63' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-07d0cb83-409a-4718-9e52-04deb5247e63' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-6e50da6e-f60d-4c85-bd07-19b83525df71' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6e50da6e-f60d-4c85-bd07-19b83525df71' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'bottom_top' (Time: 91)> Size: 728B\n",
+       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  1.,\n",
+       "        2.,  2.,  2.,  3.,  3.,  3.,  2.,  2.,  2.,  3.,  3.,  3.,  4.,\n",
+       "        4.,  4.,  5.,  5.,  5.,  5.,  5.,  6.,  6.,  7.,  7.,  7.,  7.,\n",
+       "        8., 10., 12., 13., 13., 12., 12., 12., 11., 11., 11., 10., 11.,\n",
+       "       11., 11., 10., 11., 10., 10., 10., 10., 10., 10., 10.,  9.,  9.,\n",
+       "        9.,  9.,  8.,  8.,  7.,  7.,  6.,  6.,  4.,  3.,  0.,  0.,  0.,\n",
+       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])\n",
+       "Coordinates:\n",
+       "    XTIME    (Time) datetime64[ns] 728B 2019-04-04T12:00:00 ... 2019-04-05T03...\n",
+       "Dimensions without coordinates: Time"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_stat[\"bottom_top\"] = ds_stat.bottom_top\n",
+    "ds_stat\n",
+    "ki = ds_stat['CSP_THL'].idxmin(dim='bottom_top')\n",
+    "ki"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Let's look at some meteorological info for this date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fab40e4f7d0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_ql = ds_stat['CSP_QL'].assign_coords(height = (ds_stat[\"CSP_Z\"]))\n",
+    "plot_ql.isel(Time=slice(6,None)).plot(x = 'Time',y = 'height', ylim=[0,7000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fab32a82090>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_lwc = ds_stat['CSP_LWC'].assign_coords(height = (ds_stat[\"CSP_Z\"]))\n",
+    "plot_lwc.isel(Time=slice(6,None)).plot(x = 'Time',y = 'height', ylim=[0,7000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fab40c3b0d0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_thl = ds_stat['CSP_TH'].assign_coords(height = (ds_stat[\"CSP_Z\"]))\n",
+    "plot_thl.isel(Time=slice(6,None)).plot(x = 'Time',y = 'height', ylim=[0,7000],vmin=298,vmax=320)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fix some height things so that we can plot...\n",
+    "\n",
+    "The z values are time dependent, so we need to deal with the height values by assuming that the first time step is close enough\n",
+    "\n",
+    "We also need to make bottom_top a coordinate so that we aren't yelled at by errors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_stat[\"bottom_top\"] = ds_stat.bottom_top"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_stat['bottom_top'] = ds_stat['CSP_Z'].isel(Time = 1).values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   15.215184,    45.605114,    75.94632 ,   106.27506 ,\n",
+       "         136.59618 ,   166.91183 ,   197.23079 ,   227.55254 ,\n",
+       "         257.86115 ,   288.1541  ,   318.44684 ,   348.74237 ,\n",
+       "         379.03595 ,   409.33054 ,   439.62314 ,   469.91293 ,\n",
+       "         500.20493 ,   530.4964  ,   560.79016 ,   591.11    ,\n",
+       "         621.4132  ,   651.6837  ,   681.9462  ,   712.2066  ,\n",
+       "         742.4813  ,   772.7549  ,   803.0155  ,   833.27167 ,\n",
+       "         863.53253 ,   893.79333 ,   924.05646 ,   954.3343  ,\n",
+       "         984.625   ,  1014.9191  ,  1045.2263  ,  1075.5452  ,\n",
+       "        1105.8611  ,  1136.1669  ,  1166.4724  ,  1196.7739  ,\n",
+       "        1227.061   ,  1257.3391  ,  1287.613   ,  1317.8835  ,\n",
+       "        1348.1545  ,  1378.427   ,  1408.7014  ,  1438.9777  ,\n",
+       "        1469.2583  ,  1499.5316  ,  1529.7927  ,  1560.058   ,\n",
+       "        1590.3406  ,  1620.6185  ,  1650.8711  ,  1681.1194  ,\n",
+       "        1711.373   ,  1741.6282  ,  1771.863   ,  1802.084   ,\n",
+       "        1832.3059  ,  1862.5199  ,  1892.7288  ,  1922.9358  ,\n",
+       "        1953.1406  ,  1983.3519  ,  2013.5719  ,  2043.8057  ,\n",
+       "        2074.0422  ,  2104.2583  ,  2134.4634  ,  2164.6702  ,\n",
+       "        2194.8755  ,  2225.0752  ,  2255.268   ,  2285.4622  ,\n",
+       "        2315.6643  ,  2345.861   ,  2376.0405  ,  2406.2173  ,\n",
+       "        2436.3982  ,  2466.58    ,  2496.766   ,  2526.9626  ,\n",
+       "        2557.1538  ,  2587.3315  ,  2617.5088  ,  2647.6782  ,\n",
+       "        2677.8381  ,  2708.0024  ,  2738.185   ,  2768.3545  ,\n",
+       "        2798.5044  ,  2828.6675  ,  2858.8428  ,  2889.018   ,\n",
+       "        2919.1719  ,  2949.3062  ,  2979.443   ,  3009.5776  ,\n",
+       "        3039.703   ,  3069.83    ,  3099.9514  ,  3130.066   ,\n",
+       "        3160.1882  ,  3190.3154  ,  3220.4434  ,  3250.5742  ,\n",
+       "        3280.702   ,  3310.8237  ,  3340.931   ,  3371.0188  ,\n",
+       "        3401.102   ,  3431.1902  ,  3461.293   ,  3491.3945  ,\n",
+       "        3521.4683  ,  3551.5403  ,  3581.6167  ,  3611.6907  ,\n",
+       "        3641.769   ,  3671.8528  ,  3701.933   ,  3732.0137  ,\n",
+       "        3762.0945  ,  3792.1685  ,  3822.2473  ,  3852.3481  ,\n",
+       "        3882.4512  ,  3912.5269  ,  3942.5942  ,  3972.6646  ,\n",
+       "        4002.7378  ,  4032.811   ,  4062.8813  ,  4092.9517  ,\n",
+       "        4123.02    ,  4153.09    ,  4183.161   ,  4213.229   ,\n",
+       "        4243.2993  ,  4273.3696  ,  4303.4385  ,  4333.5093  ,\n",
+       "        4363.5786  ,  4393.6553  ,  4423.735   ,  4453.8096  ,\n",
+       "        4483.88    ,  4513.9585  ,  4544.05    ,  4574.1533  ,\n",
+       "        4604.2773  ,  4634.3887  ,  4664.45    ,  4694.4854  ,\n",
+       "        4724.5103  ,  4754.531   ,  4784.547   ,  4814.564   ,\n",
+       "        4844.583   ,  4874.598   ,  4904.608   ,  4934.618   ,\n",
+       "        4964.6323  ,  4994.6475  ,  5024.664   ,  5055.481   ,\n",
+       "        5087.9473  ,  5122.214   ,  5158.3315  ,  5196.3467  ,\n",
+       "        5236.46    ,  5278.7236  ,  5323.2344  ,  5370.1943  ,\n",
+       "        5419.6626  ,  5471.841   ,  5526.9717  ,  5585.007   ,\n",
+       "        5646.068   ,  5710.3896  ,  5778.146   ,  5849.596   ,\n",
+       "        5924.9067  ,  6004.247   ,  6087.865   ,  6176.043   ,\n",
+       "        6268.9814  ,  6366.915   ,  6470.13    ,  6578.867   ,\n",
+       "        6693.4404  ,  6814.2485  ,  6941.7056  ,  7076.026   ,\n",
+       "        7217.5884  ,  7366.8086  ,  7524.0337  ,  7689.741   ,\n",
+       "        7864.397   ,  8048.4517  ,  8242.3955  ,  8446.839   ,\n",
+       "        8662.638   ,  8890.157   ,  9130.356   ,  9383.886   ,\n",
+       "        9650.805   ,  9932.354   , 10227.337   , 10527.948   ,\n",
+       "       10828.727   , 11129.923   , 11431.03    , 11731.875   ,\n",
+       "       12032.737   , 12333.832   , 12635.557   , 12937.003   ,\n",
+       "       13237.864   , 13538.295   , 13838.356   , 14138.922   ,\n",
+       "       14440.3545  , 14719.19    ], dtype=float32)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_stat['bottom_top'].values # make sure that these are heights and not indicies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate cloud base and top from the liquid water conent and the liquid water mixing ratio\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 'cb_lwc' (Time: 91)> Size: 364B\n",
+      "array([       nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,  742.4813 ,  772.7549 ,\n",
+      "        772.7549 ,  803.0155 ,  833.27167,  863.53253,  893.79333,\n",
+      "        833.27167,  863.53253,  863.53253,  863.53253,  833.27167,\n",
+      "        651.6837 ,  742.4813 ,  772.7549 ,  651.6837 ,  469.91293,\n",
+      "        681.9462 ,  893.79333,  803.0155 ,  984.625  ,  651.6837 ,\n",
+      "        954.3343 ,  621.4132 , 1075.5452 , 1075.5452 , 1105.8611 ,\n",
+      "       1105.8611 , 1136.1669 , 1136.1669 , 1136.1669 , 1166.4724 ,\n",
+      "       1166.4724 , 1166.4724 , 1166.4724 , 1196.7739 , 1196.7739 ,\n",
+      "       1196.7739 , 1196.7739 , 1196.7739 , 1227.061  , 1257.3391 ,\n",
+      "       1227.061  , 1317.8835 , 1348.1545 , 1348.1545 , 1348.1545 ,\n",
+      "       1348.1545 , 1348.1545 , 1348.1545 , 1348.1545 , 1348.1545 ,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan], dtype=float32)\n",
+      "Coordinates:\n",
+      "    XTIME    (Time) datetime64[ns] 728B ...\n",
+      "  * Time     (Time) datetime64[ns] 728B 2019-04-04T12:00:00 ... 2019-04-05T03...\n",
+      "<xarray.DataArray 'ct_lwc' (Time: 91)> Size: 364B\n",
+      "array([      nan,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan,       nan,       nan,  803.0155,  954.3343, 1075.5452,\n",
+      "       1227.061 , 1378.427 , 1499.5316, 1560.058 , 1620.6185, 1741.6282,\n",
+      "       1741.6282, 1802.084 , 1862.5199, 1892.7288, 1922.9358, 1922.9358,\n",
+      "       2074.0422, 2043.8057, 1983.3519, 1983.3519, 1953.1406, 1953.1406,\n",
+      "       1983.3519, 2013.5719, 2013.5719, 2013.5719, 1983.3519, 2013.5719,\n",
+      "       2013.5719, 1953.1406, 1983.3519, 1953.1406, 1953.1406, 1953.1406,\n",
+      "       2043.8057, 1953.1406, 1983.3519, 1862.5199, 1892.7288, 1862.5199,\n",
+      "       1832.3059, 1771.863 , 1741.6282, 1681.1194, 1681.1194, 1650.8711,\n",
+      "       1620.6185, 1560.058 , 1529.7927, 1469.2583, 1438.9777, 1408.7014,\n",
+      "       1378.427 ,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan], dtype=float32)\n",
+      "Coordinates:\n",
+      "    XTIME    (Time) datetime64[ns] 728B ...\n",
+      "  * Time     (Time) datetime64[ns] 728B 2019-04-04T12:00:00 ... 2019-04-05T03...\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds_stat['cb_lwc'] = (ds_stat['CSP_LWC']>0).idxmax(dim = 'bottom_top')\n",
+    "ds_stat['cb_lwc'] = ds_stat['cb_lwc'].where(ds_stat['cb_lwc']>ds_stat['bottom_top'][0])\n",
+    "print(ds_stat['cb_lwc'])\n",
+    "\n",
+    "ds_stat['ct_lwc'] = ((ds_stat['CSP_LWC'].isel(bottom_top = slice(None, None, -1)))>0).idxmax(dim='bottom_top')\n",
+    "ds_stat['ct_lwc'] = ds_stat['ct_lwc'].where(ds_stat['ct_lwc']<ds_stat['bottom_top'][-1])\n",
+    "print(ds_stat['ct_lwc'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aiDQaDXr06IGoqCgAQPfu3XHq1CmsXLkSr732mlSusgXjqlss7v4ylZV/0HHmzJmDGTNmSK9zc3Ph7u5e/UkRERHVkIWFBby9vVFSUmLoqjRa5ubmdbIWmkEDkaurKzp16qSzrWPHjti4cSMASGlPpVLB1dVVKpOZmSn1GimVSpSUlCA7O1unlygzMxPBwcFSmRs3buh9/s2bN/V6nyrI5XLI5fJHODsiIqLqmZiYwNLS0tDVMHoGvWD55JNP4uzZszrbzp07Bw8PDwCAp6cnlEolYmNjpf0lJSWIi4uTwo6/vz/Mzc11ymRkZODkyZNSmaCgIKjVap0pxQ8ePAi1Wi2VISIiIuNl0B6it956C8HBwYiKisLIkSNx6NAhrFmzBmvWrAGgvcwVGRmJqKgoeHt7w9vbG1FRUbC2tkZ4eDgAQKFQYOzYsZg5cyYcHR3h4OCAt99+G35+fujfvz8Aba/T4MGDMW7cOKxevRoAMH78eAwbNqzSAdVERERkZOrj9rfa+P3334Wvr6+Qy+WiQ4cOYs2aNTr7NRqNmDdvnlAqlUIul4unnnpKnDhxQqdMYWGhmDJlinBwcBBWVlZi2LBh4vLlyzplbt26JV599VVhZ2cn7OzsxKuvviqys7NrXE/edk9ERNT41PT7WyYEZ4SqidzcXCgUCqjVatjb2xu6OkRERFQDNf3+5qQHREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoGTQQzZ8/HzKZTOehVCql/UIIzJ8/H25ubrCyskJoaChOnTqlc4zi4mJMnToVTk5OsLGxQVhYGK5evapTJjs7GxEREVAoFFAoFIiIiEBOTs7jOEUiIiJqBAzeQ9S5c2dkZGRIjxMnTkj7lixZgqVLl2LFihU4fPgwlEolBgwYgLy8PKlMZGQkNm/ejOjoaOzfvx/5+fkYNmwYysvLpTLh4eFITk5GTEwMYmJikJycjIiIiMd6nkRERNRwmRm8AmZmOr1CFYQQ+OSTT/Dee+/hueeeAwB88803cHFxwQ8//IAJEyZArVZj7dq1+O6779C/f38AwPfffw93d3fs2LEDgwYNwunTpxETE4PExEQEBAQAAL788ksEBQXh7Nmz8PHxeXwnS0RERA2SwXuIUlNT4ebmBk9PT7z88stIS0sDAKSnp0OlUmHgwIFSWblcjpCQEMTHxwMAkpKSUFpaqlPGzc0Nvr6+UpmEhAQoFAopDAFAYGAgFAqFVKYyxcXFyM3N1XkQERFR02TQQBQQEIBvv/0Wf/75J7788kuoVCoEBwfj1q1bUKlUAAAXFxed97i4uEj7VCoVLCws0Lx58weWcXZ21vtsZ2dnqUxlFi5cKI05UigUcHd3f6RzJSIioobLoIFoyJAheP755+Hn54f+/fvjjz/+AKC9NFZBJpPpvEcIobftfveXqax8dceZM2cO1Gq19Lhy5UqNzomIiIgaH4NfMruXjY0N/Pz8kJqaKo0rur8XJzMzU+o1UiqVKCkpQXZ29gPL3LhxQ++zbt68qdf7dC+5XA57e3udBxERETVNDSoQFRcX4/Tp03B1dYWnpyeUSiViY2Ol/SUlJYiLi0NwcDAAwN/fH+bm5jplMjIycPLkSalMUFAQ1Go1Dh06JJU5ePAg1Gq1VIaIiIiMm0HvMnv77bfx7LPPonXr1sjMzMS///1v5ObmYvTo0ZDJZIiMjERUVBS8vb3h7e2NqKgoWFtbIzw8HACgUCgwduxYzJw5E46OjnBwcMDbb78tXYIDgI4dO2Lw4MEYN24cVq9eDQAYP348hg0bxjvMiIiICICBA9HVq1fxyiuvICsrCy1atEBgYCASExPh4eEBAJg9ezYKCwsxadIkZGdnIyAgANu3b4ednZ10jGXLlsHMzAwjR45EYWEh+vXrh/Xr18PU1FQqs2HDBkybNk26Gy0sLAwrVqx4vCdLREREDZZMCCEMXYnGIDc3FwqFAmq1muOJiIiIGomafn83qDFERERERIbAQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyema1fUNxcTEOHTqEixcvoqCgAC1atED37t3h6elZH/UjIiIiqnc1DkTx8fH47LPP8Ouvv6KkpATNmjWDlZUVbt++jeLiYrRt2xbjx4/HxIkTYWdnV591JiIiIqpTNbpkNnz4cLzwwgto2bIl/vzzT+Tl5eHWrVu4evUqCgoKkJqaivfffx87d+5E+/btERsbW9/1JiIiIqozNeohGjhwIH7++WdYWFhUur9t27Zo27YtRo8ejVOnTuH69et1WkkiIiKi+lSjHqLJkydXGYbu17lzZwwYMKDWFVm4cCFkMhkiIyOlbUIIzJ8/H25ubrCyskJoaChOnTql877i4mJMnToVTk5OsLGxQVhYGK5evapTJjs7GxEREVAoFFAoFIiIiEBOTk6t60hERERN0yPdZZafn4/c3Fydx8M4fPgw1qxZgy5duuhsX7JkCZYuXYoVK1bg8OHDUCqVGDBgAPLy8qQykZGR2Lx5M6Kjo7F//37k5+dj2LBhKC8vl8qEh4cjOTkZMTExiImJQXJyMiIiIh7upImIiKjpEbWUlpYmnnnmGWFtbS1MTEykh0wmEyYmJrU9nMjLyxPe3t4iNjZWhISEiOnTpwshhNBoNEKpVIpFixZJZYuKioRCoRCrVq0SQgiRk5MjzM3NRXR0tFTm2rVrwsTERMTExAghhEhJSREARGJiolQmISFBABBnzpypcT3VarUAINRqda3PkYiIiAyjpt/fte4hevXVV5GdnY2vv/4aO3fuxK5du7Br1y7s3r0bu3btqnUgmzx5MoYOHYr+/fvrbE9PT4dKpcLAgQOlbXK5HCEhIYiPjwcAJCUlobS0VKeMm5sbfH19pTIJCQlQKBQICAiQygQGBkKhUEhliIiIyLjVeh6i48ePIykpCT4+Po/84dHR0fjrr79w+PBhvX0qlQoA4OLiorPdxcUFly5dkspYWFigefPmemUq3q9SqeDs7Kx3fGdnZ6lMZYqLi1FcXCy9ftjLgURERNTw1bqHqGfPnrhy5cojf/CVK1cwffp0fP/997C0tKyynEwm03kthNDbdr/7y1RWvrrjLFy4UBqErVAo4O7u/sDPJCIiosar1j1EX331FSZOnIhr167B19cX5ubmOvvvHxhdlaSkJGRmZsLf31/aVl5ejr1792LFihU4e/YsAG0Pj6urq1QmMzNT6jVSKpUoKSlBdna2Ti9RZmYmgoODpTI3btzQ+/ybN2/q9T7da86cOZgxY4b0Ojc3l6GIiIioiap1ILp58yYuXLiA119/Xdomk8mkHpd77+56kH79+uHEiRM6215//XV06NAB77zzDtq2bQulUonY2Fh0794dAFBSUoK4uDgsXrwYAODv7w9zc3PExsZi5MiRAICMjAycPHkSS5YsAQAEBQVBrVbj0KFD6NWrFwDg4MGDUKvVUmiqjFwuh1wur2GrEBERUWNW60D0xhtvoHv37vjxxx/h4uJS7eWrqtjZ2cHX11dnm42NDRwdHaXtkZGRiIqKgre3N7y9vREVFQVra2uEh4cDABQKBcaOHYuZM2fC0dERDg4OePvtt+Hn5ycN0u7YsSMGDx6McePGYfXq1QCA8ePHY9iwYXUyDoqIiIgav1oHokuXLmHLli3w8vKqj/romD17NgoLCzFp0iRkZ2cjICAA27dv11krbdmyZTAzM8PIkSNRWFiIfv36Yf369TA1NZXKbNiwAdOmTZPuRgsLC8OKFSvqvf5ERETUOMiEEKI2b3j22WcxZswYPP/88/VVpwYpNzcXCoUCarUa9vb2hq4OERER1UBNv79r3UP07LPP4q233sKJEyfg5+enN6g6LCys9rUlIiIiMqBa9xCZmFR9p35tBlU3NuwhIiIianzqrYdIo9E8UsWIiIiIGppHWtyViIiIqCmoUSCKjo6u8QGvXLmCAwcOPHSFiIiIiB63GgWilStXokOHDli8eDFOnz6tt1+tVmPbtm0IDw+Hv78/bt++XecVJSIiIqovNRpDFBcXh61bt+Kzzz7D3LlzYWNjAxcXF1haWiI7OxsqlQotWrTA66+/jpMnT1a6mCoRERFRQ1Xru8xu3bqF/fv34+LFiygsLISTkxO6d++O7t27P/AOtMaOd5kRERE1PvV2l5mjoyOGDx/+SJUjIiIiakiabpcOERERUQ0xEBEREZHRYyAiIiIio8dAREREREav1oHoww8/REFBgd72wsJCfPjhh3VSKSIiIqLHqda33ZuamiIjI0NvrqFbt27B2dmZi7sSERFRg1HT7+9a9xAJISCTyfS2Hzt2DA4ODrU9HBEREZHB1XgeoubNm0Mmk0Emk6F9+/Y6oai8vBz5+fmYOHFivVSSiIiIqD7VOBB98sknEELgjTfewIIFC6BQKKR9FhYWaNOmDYKCguqlkkRERET1qcaBaPTo0QAAT09PBAcHw9zcvN4qRURERPQ41XrpjpCQEGg0Gpw7dw6ZmZnQaDQ6+5966qk6qxwRERHR41DrQJSYmIjw8HBcunQJ99+gJpPJmuxdZkRERNR01ToQTZw4ET169MAff/wBV1fXSu84IyIiImpMah2IUlNT8csvv8DLy6s+6kNERET02NV6HqKAgACcP3++PupCREREZBA16iE6fvy49Hzq1KmYOXMmVCoV/Pz89O4269KlS93WkIiIiKie1WjpDhMTE8hkMr1B1NJB7u5ryoOquXQHERFR41PT7+8a9RClp6fXWcWIiIiIGpoaBSIPD4/6rgcRERGRwdT6LrMtW7ZUul0mk8HS0hJeXl7w9PR85IoRERERPS61DkQjRoyodDzRveOIevfujV9//RXNmzevs4oSERER1Zda33YfGxuLnj17IjY2Fmq1Gmq1GrGxsejVqxe2bt2KvXv34tatW3j77bfro75EREREda7WPUTTp0/HmjVrEBwcLG3r168fLC0tMX78eJw6dQqffPIJ3njjjTqtKBEREVF9qXUP0YULFyq9bc3e3h5paWkAAG9vb2RlZT167YiIiIgeg1oHIn9/f8yaNQs3b96Utt28eROzZ89Gz549AWiX92jVqlXd1ZKIiIioHtX6ktnatWsxfPhwtGrVCu7u7pDJZLh8+TLatm2L3377DQCQn5+PDz74oM4rS0RERFQfajRT9f2EEPjzzz9x7tw5CCHQoUMHDBgwACYmte5wajQ4UzUREVHjU9Pv74cKRMaIgYiIiKjxqdOlO5YvX47x48fD0tISy5cvf2DZadOm1a6mRERERAZWox4iT09PHDlyBI6Ojg+chVomk0l3mjU17CEiIiJqfOptcVcu9EpERERNzUOPgi4pKcHZs2dRVlZWl/UhIiIieuxqHYgKCgowduxYWFtbo3Pnzrh8+TIA7dihRYsW1XkFiYiIiOpbrQPRnDlzcOzYMezZsweWlpbS9v79++Onn36q1bFWrlyJLl26wN7eHvb29ggKCsL//vc/ab8QAvPnz4ebmxusrKwQGhqKU6dO6RyjuLgYU6dOhZOTE2xsbBAWFoarV6/qlMnOzkZERAQUCgUUCgUiIiKQk5NT21MnIiKiJqrWgejXX3/FihUr0Lt3b8hkMml7p06dcOHChVodq1WrVli0aBGOHDmCI0eO4Omnn8bw4cOl0LNkyRIsXboUK1aswOHDh6FUKjFgwADk5eVJx4iMjMTmzZsRHR2N/fv3Iz8/H8OGDUN5eblUJjw8HMnJyYiJiUFMTAySk5MRERFR21MnIiKipkrUkpWVlbhw4YIQQghbW1vpeXJysrC3t6/t4fQ0b95cfPXVV0Kj0QilUikWLVok7SsqKhIKhUKsWrVKCCFETk6OMDc3F9HR0VKZa9euCRMTExETEyOEECIlJUUAEImJiVKZhIQEAUCcOXOmxvVSq9UCgFCr1Y96ikRERPSY1PT7u9Y9RD179sQff/whva7oJfryyy8RFBT00MGsvLwc0dHRuHPnDoKCgpCeng6VSoWBAwdKZeRyOUJCQhAfHw8ASEpKQmlpqU4ZNzc3+Pr6SmUSEhKgUCgQEBAglQkMDIRCoZDKVKa4uBi5ubk6DyIiImqaar2W2cKFCzF48GCkpKSgrKwMn376KU6dOoWEhATExcXVugInTpxAUFAQioqKYGtri82bN6NTp05SWHFxcdEp7+LigkuXLgEAVCoVLCws0Lx5c70yKpVKKuPs7Kz3uc7OzlKZqs5zwYIFtT4fIiIianxq3UMUHByMAwcOoKCgAO3atcP27dvh4uKChIQE+Pv717oCPj4+SE5ORmJiIt58802MHj0aKSkp0v57xykB2oHW92+73/1lKitf3XHmzJkDtVotPa5cuVLTUyIiIqJGptY9RADg5+eHb775pk4qYGFhAS8vLwBAjx49cPjwYXz66ad45513AGh7eFxdXaXymZmZUq+RUqlESUkJsrOzdXqJMjMzERwcLJW5ceOG3ufevHlTr/fpXnK5HHK5/NFPkIiIiBq8GvcQ3T+epqrHoxJCoLi4GJ6enlAqlYiNjZX2lZSUIC4uTgo7/v7+MDc31ymTkZGBkydPSmWCgoKgVqtx6NAhqczBgwehVqulMkRERGTcatxD1KxZswdeYqq4BHXv7e7VmTt3LoYMGQJ3d3fk5eUhOjoae/bsQUxMDGQyGSIjIxEVFQVvb294e3sjKioK1tbWCA8PBwAoFAqMHTsWM2fOhKOjIxwcHPD222/Dz88P/fv3BwB07NgRgwcPxrhx47B69WoAwPjx4zFs2DD4+PjUuK5ERETUdNU4EO3evVt6LoTAM888g6+++gotW7Z86A+/ceMGIiIikJGRAYVCgS5duiAmJgYDBgwAAMyePRuFhYWYNGkSsrOzERAQgO3bt8POzk46xrJly2BmZoaRI0eisLAQ/fr1w/r162FqaiqV2bBhA6ZNmybdjRYWFoYVK1Y8dL2JiIioaanRaveVsbOzw7Fjx9C2bdu6rlODxNXuiYiIGp+afn8/9OKuRERERE0FAxEREREZvUcKRNXNB0RERETUGNR4UPVzzz2n87qoqAgTJ06EjY2NzvZNmzbVTc2IiIiIHpMaByKFQqHzetSoUXVeGSIiIiJDqHEgWrduXX3Wg4iIiMhgOKiaiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyegxEREREZPQYiIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIiIjB4DERERERk9BiIiIiIyemaGrgARkUHlZwK/TgLMLYF2TwNt+wIOnoauFRE9ZgbtIVq4cCF69uwJOzs7ODs7Y8SIETh79qxOGSEE5s+fDzc3N1hZWSE0NBSnTp3SKVNcXIypU6fCyckJNjY2CAsLw9WrV3XKZGdnIyIiAgqFAgqFAhEREcjJyanvUySihkyjATaNB87HAqd/B7a+BSzvBnzaVfs8ZQtQUmDoWlKFwmxtgCWqBwYNRHFxcZg8eTISExMRGxuLsrIyDBw4EHfu3JHKLFmyBEuXLsWKFStw+PBhKJVKDBgwAHl5eVKZyMhIbN68GdHR0di/fz/y8/MxbNgwlJeXS2XCw8ORnJyMmJgYxMTEIDk5GREREY/1fImogTmwDEjbDZhbA33eBloHAyZmQPZF4MjXwH8jgM+eAJLWA+Vlhq6tcUvdoQ2qy3yB5B8NXRtqgmRCCGHoSlS4efMmnJ2dERcXh6eeegpCCLi5uSEyMhLvvPMOAG1vkIuLCxYvXowJEyZArVajRYsW+O677/DSSy8BAK5fvw53d3ds27YNgwYNwunTp9GpUyckJiYiICAAAJCYmIigoCCcOXMGPj4+1dYtNzcXCoUCarUa9vb29dcIRPR4XE4E1j0DiHJg+OdA91Ha7cV5wMUDwIVdwJk/gNy7vc1O7YF+84AOQwGZ7PHVs6wEuHpYG9xyLtfuvYpW2kuA7r0AM3nlZe5kAWl7gEsHgJI7+vtNzACfZx7/eVcQAohfDuyYDwjN39sDJwED/gWYcuQHPVhNv78b1E+SWq0GADg4OAAA0tPToVKpMHDgQKmMXC5HSEgI4uPjMWHCBCQlJaG0tFSnjJubG3x9fREfH49BgwYhISEBCoVCCkMAEBgYCIVCgfj4+EoDUXFxMYqLi6XXubm5dX6+RGQgBbeBX8Zqw5DfSKDbq3/vk9sBPoO1j4H/Ag6vBfb+B8g6B/z0KuAeAPSfD7h20z+uiWnVwaOmhNB+1oXd2lB2cT9QWklQqal9H2t7wDyeBNr1BTxDgDs3tQHrwm5Adbz6YyRvuHveCwCPoOrrX1fBqaQA2DIVOPmL9vUTrwG2SmDvEiDxC+DGKeDF9YC1Q9V1AQwT5KjRaTCBSAiBGTNmoHfv3vD19QUAqFQqAICLi4tOWRcXF1y6dEkqY2FhgebNm+uVqXi/SqWCs7Oz3mc6OztLZe63cOFCLFiw4NFOiogaHiGA3yZre34c2gHDllb9hWkmB4ImAd1fBQ4sBxI+B64cBNYNqfr4Sj9tr0y7vkDrIMDcqvo65d/U9tJUhJS867r7rR2BtqGAsos2dNXoPDWA6oT2uHduasdJnY+tvKyLr/b4dkr9fbnXgSPr7p73YG1vUb95gHMH7f6SO9oetYq6Z50FXLv+3QbuAQ8XEnOuaANoxjFtL9XgRUDP/9P+Wyn9gM0TgfQ44Mu+wMs/Ai6dtP+2N89og+SF3dpeL7fuQPh/Ablt7etARqXBBKIpU6bg+PHj2L9/v94+2X2/rIQQetvud3+Zyso/6Dhz5szBjBkzpNe5ublwd3d/4GcSUSNwcBVwdhtgaqHtXZDbVf8eSwXQ7wPtF3LcIuDo94CmijFFqhPaR/xywMxSG4o8nqw8GOWrtIFFdUJ3u6lc2xNTESpc/ACThxzyqdEAmae0ASFtN3ApHrBspj1u2753g5DLg48RPE173n99p227czFApxHaoHU5EdCU6pa/flT72L/0bu9UMNA6EDCrQTgEtMdL+Fx7fGtHYOS3QJvef+/vFAY4tgN+fEU73uur/toevUvxQF6G7rEuHdAGq/D/PnrvHTVpDSIQTZ06FVu2bMHevXvRqlUrabtSqf1rRaVSwdXVVdqemZkp9RoplUqUlJQgOztbp5coMzMTwcHBUpkbN27ofe7Nmzf1ep8qyOVyyOX8z0PUpFz7C9j+gfb5wP8HuHap3fvtXYFnPwWG/Ec/BABAUa72Elfa3ctdeRna52m7qz+2ix/QLlR7639Ne5ZqwsRE26Oi9AOenPZwl5EqzjtwMrBzAXBmK3Bq09/7Fa21dW/bV/s5V4/cPe89QP4N4PwO7aO2XPyAV34AmrWuZF9nYPwe4Ocx2p6ikxu1280stQGs3dOAwl07pULaHmDTOOCFdTXvYSOjY9BAJITA1KlTsXnzZuzZsweenrpzf3h6ekKpVCI2Nhbdu3cHAJSUlCAuLg6LFy8GAPj7+8Pc3ByxsbEYOXIkACAjIwMnT57EkiVLAABBQUFQq9U4dOgQevXqBQA4ePAg1Gq1FJqIqJErLwOu/6XtCUmPA/IquRx+56Y2yHQYBvQa9/CfZWYBwEJ/u4UN0OVF7UMI4OZZbTDIOKY7ILhCxdietiGArf5l/XrxKONpWrQHXt4AXDkEnNoMOLTVBg+HtrrHdfIGur2ibYPMFO2/yY2TlbdBVZp5AL0jtW1aFWsHYNQmIOEzbRhtGwK4B2rnlKpgqQB+GAmk/AZsjQSeXc4xRVQpg95lNmnSJPzwww/47bffdAY2KxQKWFlp/zpavHgxFi5ciHXr1sHb2xtRUVHYs2cPzp49Czs7bVf3m2++ia1bt2L9+vVwcHDA22+/jVu3biEpKQmmptq/BoYMGYLr169j9erVAIDx48fDw8MDv//+e43qyrvMiBoYIYDbadqemLQ9QPo+oFhd/fsc2gLjdgFWzasvS01Dym/aniShAZ6MBAZUMj60rER7aVHhDtg4Pe4aUj2q6fe3QQNRVeN31q1bhzFjxgDQ9iItWLAAq1evRnZ2NgICAvD5559LA68BoKioCLNmzcIPP/yAwsJC9OvXD1988YXOmJ/bt29j2rRp2LJlCwAgLCwMK1asQLNmzWpUVwYiogag4PY9g4/3AOr7bkO3bKbtJWjbF2jhA+C+3zEymfbusHt7EMg4JK0Hfp+ufT7gQ+24qJtn/h5bdfHA33fzKbv8PcaqdRB/Xhq5RhGIGhMGIiIDKS0CjqwFTvwMXE8GcM+vLBNz7V1M7e4OPnbtxjEiVLX9y7TzGQGArYt2fNO95Ar9XsaKgfHtntb+jDl3fvgB7mQQjXIeIiIiiaYcOP4TsOv//T05IgC06Pj3X+8ewbydmmqu91vaXsb45dowVDEAu+JuPufOQMHdiSoreo7uHRgfC8CmhfbOvIp17+xdq/lQaizYQ1RD7CEiekyEAFJjtX/JZ95dt9C+JdBnBuAzlF9A9GiE0M5AbmFT/eUwaV6jSi6rVeg0XDsvk2O7+q03PTReMqtjDEREj8HVJCD2n8Clu/ORWSqAPjOBXuPr7jZ0oodVVgJcPfR3QLr2FwChnTjyidFA6LuP725BqjEGojrGQERUj25d0M5vk/Kb9rWpHAiYoL3EUdWyDESGduOUticzdbv2tbkNEDwFCJ5aswk/6bFgIKpjDERE9SDvBhC3WHsHkCgHIAO6vgL0nQs048zw1Eik7wN2zAOuJWlfWzsC7YfcHesWytv4DYyBqI4xEBE9BE25dg2s0gL9fZcPapdnqBiT0X4w0O+f2hmIiRobIbQ9nDs/BG5f0N1XcRu/1wDtEiScGPKxYiCqYwxERLVUVqJdQ6rickJVWvbQTpR371pVRI1VeSmQvvfvubJu3LdOnXuAdh6k1oEGqZ4xYiCqYwxERLWg0WjXjjr5i/bWZqf2+mXk9kDAeKBjGP9ipqYrP/Pubfy7tD1IFb2lPs9o705z7mDQ6hkDBqI6xkBEVENCANveBg5/pZ04MTwa8Opv6FoRGV5uBhC3CPjrO+2YOZkJ0O1V7c0Dlgr98ha2nCW7DjAQ1TEGIqIa2h2lHSgNGfDCWsD3eUPXiKhhuXlOe1flma0PLmdirr201jaUM7E/AgaiOsZARFQDiauAmHe0z4d+DPT8P8PWh6ghu3II2LHg73m3qmPVHPAM+Xum9uYe9Vu/JoKBqI4xEBFV41g0sHmC9nnf94GQWYatD1FjJgRwO0079ihtj3agdnGubhmHdn+HI88+lV92IwaiusZARFSFkgLg4ErtmmOiHAicBAyK4kBporpUXqad5yhtt3am7KuH787ddZfMFPDqBzz9AeDaxXD1bIAYiOoYAxHRfcrLgOQNwJ6F2gUwAe2kisO/4GrgRPWtSA1c3K8NRxd23TP3kQzoMhLo+x4vqd3FQFTHGIiI7hICOPs/7ZIFWWe12xStgaffB/xeZBgiMoSsVO0fJyc3al+bWmjXAOwz0+iXv2EgqmMMRETQzi69Yx5wOUH72qo58NQs7eBpM7lh60ZE2gVnd8zTjjkCALkC6D4K8HoaaB0MWFgbtn4GwEBUxxiIyKjdf5uwmRUQNAl4cjoHchI1NEIAF3YCsfN1Z8o2lWtv468YiK3sYhQ9ugxEdYyBiIxSboa2G/7od4DQaCeS6z4KCJ0D2LsZunZE9CAaDXB2G3AuRjvWKPeq7n4XX6D/Au1g7CZ8EwQDUR1jICKjUl6qnVwxfgVQVqjd5jMU6D8PaOFj2LoRUe0JAdw6//cg7PS9fy+s7PmUNhi1fMKwdawnDER1jIGIjIZGA2weD5z4Wfuai1ESNT0Ft4F9HwOH1gDlJdptnZ8D+n0AOLQ1bN3qGANRHWMgIqMgBPC/2dpfkiZmwIhVgN8LTbo7ncioZV/SLrdz/CcAQvv/vl0/oN3T2rFGTu0b/f9/BqI6xkBERmHPIu2YIciA574Eurxo6BoR0eOgOqGdSuP8Dt3tdm7aYNTuae2aajZOhqjdI2EgqmMMRNTkHVwD/O/uchtD/gMEjDdsfYjo8VOd1IaitN3ApQSgvFh3v9JPe4dau6eB1kGAuaVh6lkLDER1jIGImrTjPwOb7i7EGjoHCH3XsPUhIsMrLQQuxd9dLmSP7i38AGBmqQ1FFZfXXHwb5OU1BqI6xkBETda57UD0K4CmDOg1ARiyuEH+UiMiA8vP1C40e2G3NiRVLNlTwabF3d6ju/Mc2bsapJr3YyCqYwxE1CSpTgJf9dfeWu/3IvCPNUYxURsRPSIhgJtn/g5HF/cDpQW6ZfrPB3q/ZZDq3aum399mj7FORNSQFOcDv7yuDUPtngZGrGQYImrALt8qwP7zWVDlFhm6KneZAugPOPeHqVMJXPNOwCPnIDxyDsIl/zQumrWDp6GrWAsMRETGatssIOuc9i6S574CTM0NXSMiuoe6sBQJF7KwL1X7uHy7oPo3GZQ9gAEABqAZ8jAXnRmIiKiBOxYNHPtBuxTH818BNo6GrhER3ZVbVIo5G0/gfyczoLlnUIuZiQxPtG6O9kpbyNDwx/m1VTY3dBVqhYGIyNhkpQJbZ2ifh84B2jxp2PoQkeTCzXyM+/YI0m5ql9Vo18IGfbxboLeXEwLbOcJWzq/t+sKWJTImpUXAz69r1zDyfAroM9PQNSKiu3afzcS0H48ir6gMrgpLrBzlj27uzQxdLaPBQERkTLa/r51LxNpJOxO1iamha0TUpBWVliPpUjb2pWbhek4h/D2ao7e3E9o62UB2d3oLIQRW703D4pgzEALo4dEcK0f5o4Wd3MC1Ny4MRETGImULcPhL7fN/rAbslIatD1ETJITA2Rt52J+ahb2pWTiUfgtFpRpp/5Zj1wEAbgpL7aUwbyfEptyQtr/Syx0LwnxhYcY7Ph83BiIiY5B9CdgyRfv8yemAd3/D1oeoCcnMLcL+81nYn5qFfeezcDNPd7kLZzs5ens7wcPBBgfTb+HIxWxcVxfhpyNX8NORKwC0A6bnhXXGqIDWUs8RPV4MRERNXXkpsHEsUKQGWvUEnv7A0DUiqldCCJxR5SH+wi2oC0rq7XNyi8qQmHYLZ1R5OtstzU0Q2NYRvb2c0Me7Bdq72N4TcrxRWFKOg+m3sD81C/vPZ+FOSRn+80JXBLbl3Z6GxEBE1NTt+hdw9TBgqQCeX8v5hqhJyswtwr67AWNfahay8ourf1MdkckAXzcF+ng7obe3E/w9mkNuVvX4PCsLU4T6OCPUx/mx1ZGqx0BE1JSl7gAOfKp9HrYCaO5h2PqQ0SvXCJy6rr472eBNnM+8A+DRVpDSCOD2Hd2eoIpeGg8H60c69oOYmZqgm3szPOnlBAcbi3r7HHo8GIiImqrcDGDzBO3znv8HdAozbH2oybh9pwTHruagtExTfeG7bt0pwf7zWThwPgs5BaV1Xqfa9tIQ3Y+BiKgp0pQDm8YBBVmAix8w8P8ZukbUiBWX/X3r+P7ULJy8rsajLAtuJzdDYDtH9PF2Qnf35jA3e/RBxC52lmjOXhp6BAxERE3Rvo+Bi/sAcxvgxXWAuaWha0QNkBACF28VYF/qTRxMv42C4jK9MoWl5Th2RY3C0nKd7V7OtrC3rPlXiKW5KXq2cUAfbyd0dW8Gc1PeVk4NCwMRUVNz8QCwZ6H2+dCPASdvw9aHDKK4rFxvXA0AlJULHL+qxr7Um9iXmoVrOYU1Op6TrRx9vJ20l6S8nOBsz5BNTQsDEVFTknNFe4u90ABdXwG6vWLoGpEB5BeXod/He3Ajt/o7rcxNZdrZk72c4FJJyDGRydC5pT18XOw4Pw41aQbts9y7dy+effZZuLm5QSaT4ddff9XZL4TA/Pnz4ebmBisrK4SGhuLUqVM6ZYqLizF16lQ4OTnBxsYGYWFhuHr1qk6Z7OxsREREQKFQQKFQICIiAjk5OfV8dkSP2Z0s4LsRQF4G0KID8MxHhq4RGcih9FtSGDI3lek92rvY4o0nPbHu9Z44Nm8goscHYcrT3nixh7ve43n/VuigtGcYoibPoD1Ed+7cQdeuXfH666/j+eef19u/ZMkSLF26FOvXr0f79u3x73//GwMGDMDZs2dhZ2cHAIiMjMTvv/+O6OhoODo6YubMmRg2bBiSkpJgaqq9wyA8PBxXr15FTEwMAGD8+PGIiIjA77///vhOlqg+FeUC3z8P3DoPKNyBUZsAua2ha0UGcjDtNgDgpR7uWPxCFwPXhqiREA0EALF582bptUajEUqlUixatEjaVlRUJBQKhVi1apUQQoicnBxhbm4uoqOjpTLXrl0TJiYmIiYmRgghREpKigAgEhMTpTIJCQkCgDhz5kyN66dWqwUAoVarH/YUiepHSaEQ64YKMc9eiMVthbiZaugakYGFfbZPeLyzVWxMumLoqhAZXE2/vxvsMP/09HSoVCoMHDhQ2iaXyxESEoL4+HgAQFJSEkpLS3XKuLm5wdfXVyqTkJAAhUKBgIAAqUxgYCAUCoVUpjLFxcXIzc3VeRA1OOVl2jFDF/cBFnbAqF8AJy9D14pqoKCkDCeuquv8uPnFZTh5Xfv7KoBLQRDVWIMNRCqVCgDg4uKis93FxUXap1KpYGFhgebNmz+wjLOz/vTozs7OUpnKLFy4UBpzpFAo4O7u/kjnQ1TnhAB+nw6c2QqYyoFXfgTcuhu6VlQDGo3A6K8P4dkV+7H7bGadHvvIxdso1wi0am6Fls2s6vTYRE1Zgw1EFe4fyCeEqHZw3/1lKitf3XHmzJkDtVotPa5cuVLLmhPVsx3zgeTvAZkJ8MLXgGcfQ9eIaij68BUcvpgNANiYdLWa0lq/JF3F0thz0GgePCPiwXTt+KEAT/YOEdVGgw1ESqUSAPR6cTIzM6VeI6VSiZKSEmRnZz+wzI0bN/SOf/PmTb3ep3vJ5XLY29vrPIgajNNbgQOfaJ+HfQZ0HGbQ6lDNZeUXY3HMGen1ztOZKCwpf8A7tO95d+NxLN+ZivgLtx5Y9mCadn9AW4dHryyREWmwgcjT0xNKpRKxsbHStpKSEsTFxSE4OBgA4O/vD3Nzc50yGRkZOHnypFQmKCgIarUahw4dksocPHgQarVaKkPUqORcBn6bpH0ePBXoPsqw9aFaidp2GurCUnR0tUer5lYoLC2v9rLZluTrKLvbM/THietVlisoKcPxu+OSAtlDRFQrBg1E+fn5SE5ORnJyMgDtQOrk5GRcvnwZMpkMkZGRiIqKwubNm3Hy5EmMGTMG1tbWCA8PBwAoFAqMHTsWM2fOxM6dO3H06FGMGjUKfn5+6N+/PwCgY8eOGDx4MMaNG4fExEQkJiZi3LhxGDZsGHx8fAx16kQPp7wU+GUsUKQGWvoDT//zsX10SZkGiWm3sHZ/Ok5eq/vBwI1dXlEpNhy8hMy8oirLJFy4hU1/XYNMBkT9wxdDu7gCAP44nvHAY2/86+/LajEnVSgtr3xR1b8u5aBMI+CqsIS7A8cPEdWGQechOnLkCPr27Su9njFjBgBg9OjRWL9+PWbPno3CwkJMmjQJ2dnZCAgIwPbt26U5iABg2bJlMDMzw8iRI1FYWIh+/fph/fr10hxEALBhwwZMmzZNuhstLCwMK1aseExnSVSHdv8/4OohQK7Qjhsyq7/FLIUQOJ+Zj72pWdhfsdbVPZd2wrq64e2BPmjtaF1vdWhMZv9yHP87qcLynalYNcof3Vvr3uxRUqbB+7+eAACE92qN7q2bw8zEBKvj0rDzzA0UlJTB2kL/V/IZVS5OXc+FuakMNnIzZBeUIuHCLTzVvoVe2YPp2stlgW0dOZEiUS3JhHiUNYuNR25uLhQKBdRqNccTkWGc3wl8/5z2+YvfAJ1H1PlHZOUX48D5LOw9l4UD57OgytXt7XCytYCXsy0S7078Z24qw6sBHpj6tBccbeUAtEHqws18aWX049fU1Q4EvpeN3AwBng7ofXfNrIrjNmTHruRg+OcHpNcWpiaIes4PL/i3krZ9vvs8/vPnWTjZWmDnjFAorM0hhEDIf/bg8u0CrAjvjmFd3PSOHbXtNNbsTcOgzi5wspVjw8HLVU64OHJVAg5dvI1Fz/nh5V6t6+dkiRqZmn5/cy0zosYg7waweYL2eY836iwMFZWW4/DF29ifmoW9qVk4naE735bczAS9PB3Q28sJfbxboIPSDiYmMpy8psbimDPYl5qF9fEX8UvSVYwK9EBWfjH2p+oHqdq4dacEl28X4Oe7d191crVHn/ZOGObnBr9Wikc63/rynz/PAgCG+rmitFyD7Sk38PbPx5ByPRdzn+mA6zlFWL4zFQDw3tCOUFibA9DeATu0iytW7rmAP45n6AWisnINNh+9BgB4/olWsLU0w4aDlxFzSoV//8NXZ8X4otJyJF/JAcD5h4geBgMRUUOnKQc2/R9w5ybg4gsMinr4Q2kETqtysT81C/tSs3Do4m2UlOmOR+nkaq9d0dzbCT3bOMDS3FTvOL4tFfhubAD2p2Zh4f9O49T1XKyKuyDttzAzQa822l6eAE8H2Mhr/qtGpS7C/vPa+p3OyEXK3ceXe9Pww7hABDawL/v481nYfz4L5qYyvDukA1o2s8KnO1Px6c5UfH0gHWdv5MJEJkNxmQZBbR0xoltLnfcP9dMGol1nMnGnuEynrfafz8LNvGI0tzZHqI8zTE1kcLK1QFZ+CQ6cz0Koz99zrB29nIOScg2c7eRow8uYRLXGQETU0O1bCqTvBcytgRfWAea1GyyrUhdhX+pN7EvVXga7dadEZ7/S3hK9vZ3Qx9sJT3o5wakWl6h6ezvh93a98fvx69h6PAOeTjbo7eWEXp6VB6maaO9iJ42PuZmnvYT30+ErSEi7hfd/PYlt0/rAwqxh3CArhMDiu71D4b1aw91BG0TeGtAeHV3tMOO/x3DgvHZcj7mpDP8a4as3tqezmz3aOFrj4q0C7DyTibCuf/cSbfxL2zs0vFtL6ZyH+Lriu8RL+ON4hk4gqhg/FMDxQ0QPhYGIqCErzAbiP9M+H/ox0KK9tKuotBxZ+cV6bxECSM3Mw767vUDnM/N19ltbmCKwrSN6eznhqfZOaNfC9pG+QE1MZBjerSWG39fzURda2MkxontL9PVxRr+le3A+Mx9f7kvD5L4PtzxJfnEZ5GYmOpeaHsX2lBs4diUHVuammPK0t86+wb6uaONkg3HfHsGV24V4M6QdvJz1F9ytuGz2+e4L+OP4dSkQ5RaVYvsp7Txszz3xd9sO7aINRH+eUuH//cNPCkoVC7oGeHL+IaKHwUBE1JBZNQfG7QJOboSmyys4cz0X+1JvYv/5LBxKv43isspvv76XiQzwa9UMfby0l8GeaN28wfSw1JTC2hzvDe2It346huU7U/FsF7ca3d1WUqZB0qVs7D+v7SE7cU0NHxc7RI8PRDPrR7tDr1wj8NHd3qGxvT3Rwk6/Z62D0h7bpvXBqeu5DwwqQ/3c8PnuC9h99ibyi8tgKzfDtuMZKC7TwNvZFn4t/x471bONA1rYyaXes74dnFFcVo6/LmsnqA3khIxED4WBiKgBK9cIbL5kiX2qgTgQtQNZ+bqXuyzMTGBSSeeOs50lnvTSXgYLbuf4yF/+DcGIbi3x38NXkZB2C/O2nMTXY3pWuSzP5qPX8Pux63pTBQDAGVUexqw7jA3/F1CrsU33+/XoNaRm5kNhZY5xT7WtspydpXm14546utqhrZMN0rLuYOfpGxjerSU23b1c9twTrXTO09REhmd8lfgm4RK2Hs9A3w7OOH5VjeIyDZxsLdCuhX4vFBFVj4GIqAEzkQFLt5/FdbX2ri1rC1MEeDqgj3cL9PF2gpfzo13uakxkMu0YnCGf7sXuszfx5ykVBvu66pQpKi3H3E0nsOnunVmAdqqA3l5O6O3dAq2aW2Hi90lIvpKDid8n4avRPSA3q/1Yp5IyDZbtOAcAeDO0HRRW5o98bkO7uOKzXeex9XgGurs3x6GLtyGTAf/orn8pcmgXN3yTcAnbU1QoLvOVluvo5elgND8PRHWNgYioAZPJZHg10AMFJWXo492iUV7uqktezraYGNIOn+06j/lbUtDbuwVs7/byqNRFmPDdERy7qoapiQyTQtthiK+rNFVAhXVjeuLVrw5iX2oWZvx0DMtf6Q7TyrrZHuDHQ5dxNbsQznZyjA5qUyfnVhGI4s7eRGuHiwCA3l5OUCos9cr28GgOZzs5MvO00xxwQVeiR2e8v1mJGonJfb0wa1AHBLZ1NOowVGFyXy+0drCGKrcIn8Rqe2mSLt3Gsyv249hVNZpZm+O7N3ph5kAfdHKz1wlDANC9dXOsjvCHuakMf5zIwPu/nkRt5qctKCnDZ7vOAwCm9fOGlcXD3U13Px8XO7RrYYOScg2+PpAOQDv3UGVMTGR4xk/bO/Zr8nUkXdKOH+KCrkQPj79diahRsTQ3xYfDOwMA1sVfxEd/nsXLaxJxM68YHZR2+H1KbwR7OT3wGH28W+DTl7tDJtP29ny0/WyNP3/dgYvIyi+Gh6M1Xurp/kjnci/tZTPtHWZCADYWphjUWVll+WF310Hbevw6CkrK0czaHO2d7aosT0QPxkBERI1OqI8zhvq5olwjsGL3eZSWCwzxVWLjm8HSXEDVecbPFVH/8AMAfL77Atbd7ZV5kJyCEmkCyhkD2tfZ7fsVKkJORf0e1Pv0ROvmUNpboqJzq1cbB73eMCKqOQYiImqUPhjWCXaW2vFDMwe0xxevPlHru8Ze6dUaswf7AAAWbjuD9Kw7Dyy/Mu4C8orK0EFph2crWXfsUbV3sYNfSwVkMuDlXg/ufbr3shnA5TqIHhUHVRNRo6RUWGLbtD4oKi2Ht8vDXyp6M6QdDqbdRty5m/jg15P4bmyvSu/UupFbhPUHLgIAZg3yqbfemK/H9IRKXVSjdduGdnGVxhtxQkaiR8MeIiJqtNwdrB8pDAHasTsfDu8MuZkJ9p/Pwu/HMyott3xnKorLNPD3aI6nOzhXWqYutLCT13gR2+7uzfCMnxKDOrugo2vVq3gTUfUYiIjI6Hk42mDK3eVA/rU1BerCUp39l27dwU+HrwAAZg/yaTBz/ZiYyPDFq/5YHdGj1lMHEJEuBiIiIgDjQ9qibQsb3Mwrxsf33XW2NPYcyjQCIe1bcKwOURPFQEREBEBuZop/D/cFAHyXeAnHr+YAAE5n5GLLsesAtGOHiKhpYiAiIror2MsJI7q5QQjgvc0npQVchdAOYPZtWbOxPUTU+DAQERHd472h2tv5T1xTY9Yvx7DzTCZMTWSYOaC9oatGRPWIgYiI6B4t7OSYPbgDAEgrzr/o3wptuYo8UZPGQEREdJ/wXq3R1b0ZAMDCzATT+3sbtkJEVO8YiIiI7mNqIsPi5/3QtoUNZg/ygavCytBVIqJ6xpmqiYgq0UFpj10zQw1dDSJ6TNhDREREREaPgYiIiIiMHgMRERERGT0GIiIiIjJ6DERERERk9BiIiIiIyOgxEBEREZHRYyAiIiIio8dAREREREaPgYiIiIiMHgMRERERGT0GIiIiIjJ6DERERERk9BiIiIiIyOgxEBEREZHRYyAiIiIio8dAREREREaPgYiIiIiMHgMRERERGT2jCkRffPEFPD09YWlpCX9/f+zbt8/QVSIiIqIGwGgC0U8//YTIyEi89957OHr0KPr06YMhQ4bg8uXLhq4aERERGZhMCCEMXYnHISAgAE888QRWrlwpbevYsSNGjBiBhQsXVvv+3NxcKBQKqNVq2Nvb12dViYiIqI7U9Pvb7DHWyWBKSkqQlJSEd999V2f7wIEDER8fX+l7iouLUVxcLL1Wq9UAtA1LREREjUPF93Z1/T9GEYiysrJQXl4OFxcXne0uLi5QqVSVvmfhwoVYsGCB3nZ3d/d6qSMRERHVn7y8PCgUiir3G0UgqiCTyXReCyH0tlWYM2cOZsyYIb3WaDS4ffs2HB0dq3zP45Kbmwt3d3dcuXKFl+/uw7apGtumamybqrFtKsd2qVpDaxshBPLy8uDm5vbAckYRiJycnGBqaqrXG5SZmanXa1RBLpdDLpfrbGvWrFl9VfGh2NvbN4gftoaIbVM1tk3V2DZVY9tUju1StYbUNg/qGapgFHeZWVhYwN/fH7GxsTrbY2NjERwcbKBaERERUUNhFD1EADBjxgxERESgR48eCAoKwpo1a3D58mVMnDjR0FUjIiIiAzOaQPTSSy/h1q1b+PDDD5GRkQFfX19s27YNHh4ehq5arcnlcsybN0/vkh6xbR6EbVM1tk3V2DaVY7tUrbG2jdHMQ0RERERUFaMYQ0RERET0IAxEREREZPQYiIiIiMjoMRARERGR0WMgesz27t2LZ599Fm5ubpDJZPj111+rLDthwgTIZDJ88skn1R73xIkTCAkJgZWVFVq2bIkPP/xQb92WuLg4+Pv7w9LSEm3btsWqVase8WzqVk3a5vTp0wgLC4NCoYCdnR0CAwNx+fLlBx63sbdNde2Sn5+PKVOmoFWrVrCyskLHjh11FjGuSmNvF0C7xE7Pnj1hZ2cHZ2dnjBgxAmfPntUpI4TA/Pnz4ebmBisrK4SGhuLUqVPVHruxt091bVNaWop33nkHfn5+sLGxgZubG1577TVcv3692mM35raJj4+HqakpBg8eXK+fs3HjRnTq1AlyuRydOnXC5s2bdfbPnz8fMplM56FUKuu1TjX1xRdfwNPTE5aWlvD398e+ffsAGMHPjKDHatu2beK9994TGzduFADE5s2bKy23efNm0bVrV+Hm5iaWLVv2wGOq1Wrh4uIiXn75ZXHixAmxceNGYWdnJz766COpTFpamrC2thbTp08XKSkp4ssvvxTm5ubil19+qcOzezTVtc358+eFg4ODmDVrlvjrr7/EhQsXxNatW8WNGzeqPGZTaJvq2uX//u//RLt27cTu3btFenq6WL16tTA1NRW//vprlcdsCu0ihBCDBg0S69atEydPnhTJycli6NChonXr1iI/P18qs2jRImFnZyc2btwoTpw4IV566SXh6uoqcnNzqzxuU2if6tomJydH9O/fX/z000/izJkzIiEhQQQEBAh/f/8HHrext83YsWPF9OnThY2Njbh06dIjHausrEyUl5frbY+PjxempqYiKipKnD59WkRFRQkzMzORmJgolZk3b57o3LmzyMjIkB6ZmZmPVJ+6EB0dLczNzcWXX34pUlJSdNqqqf/MMBAZUFWB6OrVq6Jly5bi5MmTwsPDo9pA9MUXXwiFQiGKioqkbQsXLhRubm5Co9EIIYSYPXu26NChg877JkyYIAIDAx/5POpDZW3z0ksviVGjRtXqOE2tbSprl86dO4sPP/xQZ9sTTzwh3n///SqP09TapUJmZqYAIOLi4oQQQmg0GqFUKsWiRYukMkVFRUKhUIhVq1ZVeZym2D73t01lDh06JAA8MCg05rbJz88XdnZ24syZM+Kll14SCxYskPbt3r1bABBbt24VXbp0EXK5XPTq1UscP35cKrNu3TqhUCjE77//Ljp27ChMTU1FWlqa3ueMHDlSDB48WGfboEGDxMsvvyy9njdvnujatWvdn+Qj6tWrl5g4caLOtg4dOoh333230vJN6WeGl8waGI1Gg4iICMyaNQudO3eutMyYMWMQGhoqvU5ISEBISIjOJFiDBg3C9evXcfHiRanMwIEDdY4zaNAgHDlyBKWlpXV+HnVNo9Hgjz/+QPv27TFo0CA4OzsjICBA7/KRMbZN7969sWXLFly7dg1CCOzevRvnzp3DoEGDpDLG0i5qtRoA4ODgAABIT0+HSqXSOQ+5XI6QkBDEx8dL24yhfe5vm6rKyGQynXUbm1Lb/PTTT/Dx8YGPjw9GjRqFdevW6V22mTVrFj766CMcPnwYzs7OCAsL06lzQUEBFi5ciK+++gqnTp2Cs7Oz3udUdf73/swBQGpqKtzc3ODp6YmXX34ZaWlpdXi2tVdSUoKkpCS9ug8cOFCv7hWa0s8MA1EDs3jxYpiZmWHatGlVlnF1dUXr1q2l1yqVSm+R2orXFQvaVlWmrKwMWVlZdVX9epOZmYn8/HwsWrQIgwcPxvbt2/GPf/wDzz33HOLi4qRyxtg2y5cvR6dOndCqVStYWFhg8ODB+OKLL9C7d2+pjDG0ixACM2bMQO/eveHr6wvg73Op7DzuXey5qbdPZW1zv6KiIrz77rsIDw/XWZCzKbXN2rVrMWrUKADA4MGDkZ+fj507d+qUmTdvHgYMGAA/Pz988803uHHjhs74n9LSUnzxxRcIDg6Gj48PbGxs9D6nqvO/92cuICAA3377Lf788098+eWXUKlUCA4Oxq1bt+rylGslKysL5eXl1da9QlP7mTGapTsag6SkJHz66af466+/IJPJqiy3cOFCvW33l6/4q+fe7TUp01BpNBoAwPDhw/HWW28BALp164b4+HisWrUKISEhAIyzbZYvX47ExERs2bIFHh4e2Lt3LyZNmgRXV1f0798fgHG0y5QpU3D8+HHs379fb19l53HvtqbePg9qG0D7Jf/yyy9Do9Hgiy++0NnXVNrm7NmzOHToEDZt2gQAMDMzw0svvYSvv/5a+n8CAEFBQdJzBwcH+Pj44PTp09I2CwsLdOnSBQBw+fJldOrUSdo3d+5czJ07F0D1P3NDhgyRnvv5+SEoKAjt2rXDN998gxkzZtTFKT+06uoONM2fGQaiBmTfvn3IzMzUSdbl5eWYOXMmPvnkE6lr8X5KpVIvvWdmZgL4O4VXVcbMzAyOjo51eBb1w8nJCWZmZjq/fACgY8eOVf6SB5p+2xQWFmLu3LnYvHkzhg4dCgDo0qULkpOT8dFHH+n8or9XU2uXqVOnYsuWLdi7dy9atWolba+4a0elUsHV1VXanpmZqffX6L2aUvtU1TYVSktLMXLkSKSnp2PXrl06f+lXprG2zdq1a1FWVoaWLVtK24QQMDc3R3Z29gPfe+8XspWVlfTazc0NycnJ0r6Ky5FVnf+DfuZsbGzg5+eH1NTUGp9TXXNycoKpqWm1dW+qPzO8ZNaARERE4Pjx40hOTpYebm5umDVrFv78888q3xcUFIS9e/eipKRE2rZ9+3a4ubmhTZs2UpnY2Fid923fvh09evSAubl5vZxPXbKwsEDPnj31bqk+d+7cAxfobeptU1paitLSUpiY6P5XNjU1lXrVKtNU2kUIgSlTpmDTpk3YtWsXPD09dfZ7enpCqVTqnEdJSQni4uIQHBxc5XGbQvtU1zbA319sqamp2LFjR42+eBpj25SVleHbb7/Fxx9/rPP79dixY/Dw8MCGDRuksomJidLz7OxsnDt3Dh06dKj0uGZmZvDy8pIeFYGoqvN/0M9ccXExTp8+rRPcHzcLCwv4+/vr1T02Nlaqe5P+mXlsw7dJCCFEXl6eOHr0qDh69KgAIJYuXSqOHj1a5Qj9yu4ye/fdd0VERIT0OicnR7i4uIhXXnlFnDhxQmzatEnY29tXekvjW2+9JVJSUsTatWsbzG2wFaprm02bNglzc3OxZs0akZqaKj777DNhamoq9u3bJx2jKbZNde0SEhIiOnfuLHbv3i3S0tLEunXrhKWlpfjiiy+kYzTFdhFCiDfffFMoFAqxZ88enduXCwoKpDKLFi0SCoVCbNq0SZw4cUK88sorerfdN8X2qa5tSktLRVhYmGjVqpVITk7WKVNcXCwdpym0zebNm4WFhYXIycnR2zd37lzRrVs36S6zzp07ix07dogTJ06IsLAw0bp1a6k9Ku4yq86BAweEqampWLRokTh9+rRYtGiR3m33M2fOFHv27BFpaWkiMTFRDBs2TNjZ2YmLFy/W2Xk/jIrb7teuXStSUlJEZGSksLGxERcvXmzyPzMMRI9ZxX+6+x+jR4+utHxlgWj06NEiJCREZ9vx48dFnz59hFwuF0qlUsyfP1+6nbHCnj17RPfu3YWFhYVo06aNWLlyZR2e2aOrSdusXbtWeHl5CUtLS9G1a1e9uXaaYttU1y4ZGRlizJgxws3NTVhaWgofHx/x8ccf65xjU2wXIUSl7QJArFu3Tiqj0WjEvHnzhFKpFHK5XDz11FPixIkTOsdpiu1TXdukp6dXWWb37t3ScZpC2wwbNkw888wzle5LSkoSAMTHH38sAIjff/9ddO7cWVhYWIiePXuK5ORkqWxNA5EQQvz888/Cx8dHmJubiw4dOoiNGzfq7K+YD8vc3Fy4ubmJ5557Tpw6deqhz7Euff7558LDw0NYWFiIJ554Qpqqoan/zMiEuO+eQyIiIiOzZ88e9O3bF9nZ2Tq3kJPx4BgiIiIiMnoMRERERGT0eMmMiIiIjB57iIiIiMjoMRARERGR0WMgIiIiIqPHQERERERGj4GIiIioAVm4cCF69uwJOzs7ODs7Y8SIEXrLFgkhMH/+fLi5ucHKygqhoaE4deqUTpk1a9YgNDQU9vb2kMlkyMnJ0fusv/76CwMGDECzZs3g6OiI8ePHIz8/v0b1vHr1KiwsLKpc2qQuxcXFwd/fH5aWlmjbti1WrVqls3/9+vWQyWR6j6Kiohp/BgMRETUo8+fPR7du3Qz2+R988AHGjx9vkM8uLi5G69atkZSUZJDPp4YhLi4OkydPRmJiImJjY1FWVoaBAwfizp07UpklS5Zg6dKlWLFiBQ4fPgylUokBAwYgLy9PKlNQUIDBgwdj7ty5lX7O9evX0b9/f3h5eeHgwYOIiYnBqVOnMGbMmBrVc/369Rg5ciQKCgpw4MCBRzpnQBvyysrK9Lanp6fjmWeeQZ8+fXD06FHMnTsX06ZNw8aNG3XK2dvbIyMjQ+dhaWlZqwoQET0WqGLa/4rH6NGjRV5ensjKyjJI/VQqlbCzsxPp6enStpCQEDF9+nS9sps3bxYVv0JDQkIeeF4eHh5CCO0yK1OmTBGenp7CwsJCtGrVSgwbNkzs2LFDOu6nn34q+vXrV5+nSY1MZmamACAtoaHRaIRSqRSLFi2SyhQVFQmFQiFWrVql9/6K5X+ys7N1tq9evVo4OzuL8vJyaVvFmompqakPrJNGoxFt27YVMTEx4p133hGvv/66zv6KZT5+/PFHERQUJORyuejUqZPOEh8V9YqJiRH+/v7C3Nxc7Nq1S++zZs+eLTp06KCzbcKECSIwMFB6XZtlVarCHiIiemzu/cvtk08+0fuL7tNPP4WtrW2NVtCuD2vXrkVQUJC0AndNbdq0STqHQ4cOAQB27NghbTt8+DAuXrwIf39/7Nq1C0uWLMGJEycQExODvn37YvLkydKxXn31Vezbtw+nT5+uy1OjRkytVgMAHBwcAGh7TFQqFQYOHCiVkcvlCAkJQXx8fI2PW1xcDAsLC5iY/B0FrKysAAD79+9/4Ht3796NgoIC9O/fHxEREfjvf/+r0ztVYdasWZg5cyaOHj2K4OBghIWF4datWzplZs+ejYULF+L06dPo0qWL3jESEhJ0zhUABg0ahCNHjqC0tFTalp+fDw8PD7Rq1QrDhg3D0aNHq2+EezAQEdFjo1QqpYdCoYBMJtPbdv8lszFjxmDEiBGIioqCi4sLmjVrhgULFqCsrAyzZs2Cg4MDWrVqha+//lrns65du4aXXnoJzZs3h6OjI4YPH46LFy8+sH7R0dEICwur9Xk5ODhI59CiRQsAgKOjo862SZMmQSaT4dChQ3jhhRfQvn17dO7cGTNmzEBiYqJ0LEdHRwQHB+PHH3+sdT2o6RFCYMaMGejduzd8fX0BACqVCgDg4uKiU9bFxUXaVxNPP/00VCoV/vOf/6CkpATZ2dnS5bWMjIwHvnft2rV4+eWXYWpqis6dO8PLyws//fSTXrkpU6bg+eefR8eOHbFy5UooFAqsXbtWp8yHH36IAQMGoF27dpX+MaRSqSo917KyMmRlZQEAOnTogPXr12PLli348ccfYWlpiSeffBKpqak1bg8GIiJq8Hbt2oXr169j7969WLp0KebPn49hw4ahefPmOHjwICZOnIiJEyfiypUrALRjJ/r27QtbW1vs3bsX+/fvh62tLQYPHoySkpJKPyM7OxsnT55Ejx496rz+t2/fRkxMDCZPngwbGxu9/fcvJtqrVy/s27evzutBjc+UKVNw/PjxSgOyTCbTeS2E0Nv2IJ07d8Y333yDjz/+GNbW1lAqlWjbti1cXFxgamoqlbG1tYWtrS2GDBkCAMjJycGmTZswatQo6VijRo3S+6MEAIKCgqTnZmZm6NGjh17v573/5yo+y9bWFhMnTnzgud67PTAwEKNGjULXrl3Rp08f/Pe//0X79u3x2Wef1bg9zGpckojIQBwcHLB8+XKYmJjAx8cHS5YsQUFBgfTX7Jw5c7Bo0SIcOHAAL7/8MqKjo2FiYoKvvvpK+oW5bt06NGvWDHv27NHrfgeAS5cuQQgBNze3Oq//+fPnIYSo8d04LVu2rLY3i5q+qVOnYsuWLdi7dy9atWolbVcqlQC0PSeurq7S9szMTL2elOqEh4cjPDwcN27cgI2NDWQyGZYuXQpPT08AwLZt26TLUhWX03744QcUFRUhICBAOo4QAhqNBikpKejUqdMDP/P+cHPvHwnJycnSc3t7e+l87+/5yszMhJmZWZWX101MTNCzZ0/2EBFR09K5c2edcQ4uLi7w8/OTXpuamsLR0RGZmZkAgKSkJJw/fx52dnbSX5sODg4oKirChQsXKv2MwsJCAKjdXSk1dP9fs9WxsrJCQUFBndeDGgchBKZMmYJNmzZh165dUjip4OnpCaVSidjYWGlbSUkJ4uLiEBwc/FCf6eLiAltbW/z000+wtLTEgAEDAAAeHh7w8vKCl5cXWrZsCUB7uWzmzJlITk6WHseOHUPfvn31eonuvRxcVlaGpKSkB/5hUPFZXl5ecHZ2BqDtZbr3XAFg+/bt6NGjB8zNzSs9jhACycnJOoGxOuwhIqIG7/5fejKZrNJtGo0GAKDRaODv748NGzboHatijM/9nJycAGgvnd1bxt7eXhrUeq+cnBzpL9jqeHt7QyaT4fTp0xgxYkS15W/fvl1lPanpmzx5Mn744Qf89ttvsLOzk3pHFAoFrKysIJPJEBkZiaioKHh7e8Pb2xtRUVGwtrZGeHi4dByVSgWVSoXz588DAE6cOAE7Ozu0bt1aGqC9YsUKBAcHw9bWFrGxsZg1axYWLVqkdxm3QnJyMv766y9s2LBBL9i88soreO+997Bw4UJp2+effw5vb2907NgRy5YtQ3Z2Nt54441atcfEiROxYsUKzJgxA+PGjUNCQgLWrl2rcxlxwYIFCAwMhLe3N3Jzc7F8+XIkJyfj888/r/HnsIeIiJqcJ554AqmpqXB2dtb5i9PLywsKhaLS97Rr1w729vZISUnR2d6hQwccOXJEr/zhw4fh4+NTo/o4ODhg0KBB+Pzzz3Xmkqlw/4R5J0+eRPfu3Wt0bGp6Vq5cCbVajdDQULi6ukqPewctz549G5GRkZg0aRJ69OiBa9euYfv27bCzs5PKrFq1Ct27d8e4ceMAAE899RS6d++OLVu2SGUOHTqEAQMGwM/PD2vWrMHq1asxbdq0Kuu2du1adOrUqdJenhEjRuD27dv4/fffpW2LFi3C4sWL0bVrV+zbtw+//fab9MdHTXl6emLbtm3Ys2cPunXrhn/9619Yvnw5nn/+ealMTk4Oxo8fj44dO2LgwIG4du0a9u7di169etX8gx7ppn0ioodU1bwh8+bNE127dpVejx49WgwfPlynTGVzA3l4eIhly5YJIYS4c+eO8Pb2FqGhoWLv3r0iLS1N7NmzR0ybNk1cuXKlyjo999xzYubMmTrb0tPThZWVlZg0aZJITk4WZ8+eFStWrBByuVz897//1TtGxfwrR48e1dmelpYmlEql6NSpk/jll1/EuXPnREpKivj000/15ljx8PAQ3377bZX1JGroqvp/0JCxh4iImhxra2vs3bsXrVu3xnPPPYeOHTvijTfeQGFh4QMvc40fPx7R0dHSpTcAaNOmDfbt24cLFy5g4MCB6NmzJ9avX4/169fjxRdfrHGdPD098ddff6Fv376YOXMmfH19MWDAAOzcuRMrV66UyiUkJECtVuOFF154uJMnoociE+LuaD8iIiMnhEBgYCAiIyPxyiuvGKQOL774Irp3717lcgtEjcHFixfh6emJo0ePGnQpntrgoGoiortkMhnWrFmD48ePG+Tzi4uL0bVrV7z11lsG+XyiutKmTRs0tv4W9hARERGR0eMYIiIiIjJ6DERERERk9BiIiIiIyOgxEBEREZHRYyAiIiIio8dAREREREaPgYiIiIiMHgMRERERGT0GIiIiIjJ6/x8cUxhbNzEaPQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_stat['cb_lwc'].plot(label='base',ylim = (0,7000),xlim = (ds_stat['CSP_Z'].Time[6],ds_stat['CSP_Z'].Time[-1]))\n",
+    "ds_stat['ct_lwc'].plot(label='top',ylim = (0,7000),xlim = (ds_stat['CSP_Z'].Time[6],ds_stat['CSP_Z'].Time[-1]))\n",
+    "plt.legend()\n",
+    "plt.ylabel('Height (m)')\n",
+    "plt.xlabel('Time (UTC)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 'cb_ql' (Time: 91)> Size: 364B\n",
+      "array([       nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan,        nan,        nan,  742.4813 ,  772.7549 ,\n",
+      "        772.7549 ,  803.0155 ,  833.27167,  863.53253,  893.79333,\n",
+      "        833.27167,  863.53253,  863.53253,  863.53253,  833.27167,\n",
+      "        651.6837 ,  742.4813 ,  772.7549 ,  651.6837 ,  469.91293,\n",
+      "        681.9462 ,  893.79333,  803.0155 ,  984.625  ,  651.6837 ,\n",
+      "        954.3343 ,  621.4132 , 1075.5452 , 1075.5452 , 1105.8611 ,\n",
+      "       1105.8611 , 1136.1669 , 1136.1669 , 1136.1669 , 1166.4724 ,\n",
+      "       1166.4724 , 1166.4724 , 1166.4724 , 1196.7739 , 1196.7739 ,\n",
+      "       1196.7739 , 1196.7739 , 1196.7739 , 1227.061  , 1257.3391 ,\n",
+      "       1227.061  , 1317.8835 , 1348.1545 , 1348.1545 , 1348.1545 ,\n",
+      "       1348.1545 , 1348.1545 , 1348.1545 , 1348.1545 , 1348.1545 ,\n",
+      "              nan,        nan,        nan,        nan,        nan,\n",
+      "              nan], dtype=float32)\n",
+      "Coordinates:\n",
+      "    XTIME    (Time) datetime64[ns] 728B ...\n",
+      "  * Time     (Time) datetime64[ns] 728B 2019-04-04T12:00:00 ... 2019-04-05T03...\n",
+      "<xarray.DataArray 'ct_ql' (Time: 91)> Size: 364B\n",
+      "array([      nan,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan,       nan,       nan,  803.0155,  954.3343, 1075.5452,\n",
+      "       1227.061 , 1378.427 , 1499.5316, 1560.058 , 1620.6185, 1741.6282,\n",
+      "       1741.6282, 1802.084 , 1862.5199, 1892.7288, 1922.9358, 1922.9358,\n",
+      "       2074.0422, 2043.8057, 1983.3519, 1983.3519, 1953.1406, 1953.1406,\n",
+      "       1983.3519, 2013.5719, 2013.5719, 2013.5719, 1983.3519, 2013.5719,\n",
+      "       2013.5719, 1953.1406, 1983.3519, 1953.1406, 1953.1406, 1953.1406,\n",
+      "       2043.8057, 1953.1406, 1983.3519, 1862.5199, 1892.7288, 1862.5199,\n",
+      "       1832.3059, 1771.863 , 1741.6282, 1681.1194, 1681.1194, 1650.8711,\n",
+      "       1620.6185, 1560.058 , 1529.7927, 1469.2583, 1438.9777, 1408.7014,\n",
+      "       1378.427 ,       nan,       nan,       nan,       nan,       nan,\n",
+      "             nan], dtype=float32)\n",
+      "Coordinates:\n",
+      "    XTIME    (Time) datetime64[ns] 728B ...\n",
+      "  * Time     (Time) datetime64[ns] 728B 2019-04-04T12:00:00 ... 2019-04-05T03...\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds_stat['cb_ql'] = (ds_stat['CSP_LWC']>0).idxmax(dim = 'bottom_top')\n",
+    "ds_stat['cb_ql'] = ds_stat['cb_ql'].where(ds_stat['cb_ql']>ds_stat['bottom_top'][0])\n",
+    "print(ds_stat['cb_ql'])\n",
+    "\n",
+    "ds_stat['ct_ql'] = ((ds_stat['CSP_LWC'].isel(bottom_top = slice(None, None, -1)))>0).idxmax(dim='bottom_top')\n",
+    "ds_stat['ct_ql'] = ds_stat['ct_ql'].where(ds_stat['ct_ql']<ds_stat['bottom_top'][-1])\n",
+    "print(ds_stat['ct_ql'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_stat['cb_ql'].plot(label='base',ylim = (0,7000),xlim = (ds_stat['CSP_Z'].Time[6],ds_stat['CSP_Z'].Time[-1]))\n",
+    "ds_stat['ct_ql'].plot(label='top',ylim = (0,7000),xlim = (ds_stat['CSP_Z'].Time[6],ds_stat['CSP_Z'].Time[-1]))\n",
+    "plt.legend()\n",
+    "plt.ylabel('Height (m)')\n",
+    "plt.xlabel('Time (UTC)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## They're the same - how fun!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "op": "addrange",
+         "valuelist": [
+          "Python 3"
+         ]
+        },
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "display_name",
+       "op": "patch"
+      }
+     ],
+     "key": "kernelspec",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "op": "addrange",
+         "valuelist": [
+          "Python3"
+         ]
+        },
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "display_name",
+       "op": "patch"
+      }
+     ],
+     "key": "kernelspec",
+     "op": "patch"
+    }
+   ]
+  },
+  "toc-autonumbering": false
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/moisture_divergence.ipynb b/notebooks/moisture_divergence.ipynb
new file mode 100644
index 0000000..ce55678
--- /dev/null
+++ b/notebooks/moisture_divergence.ipynb
@@ -0,0 +1,56175 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4f968ada-aedc-4232-b7c3-ac0aee4fa868",
+   "metadata": {},
+   "source": [
+    "# Load in necessary packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "c720c3c6-2594-4b05-8328-cbce56bcafb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Libraries required for moisture convergence visualization\n",
+    "from datetime import datetime\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import xwrf\n",
+    "import glob\n",
+    "import metpy.calc as mpcalc\n",
+    "import math\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bc71071-5ded-4a69-802f-9861f7e7b9e4",
+   "metadata": {},
+   "source": [
+    "### We will first identify LASSO SGP case(s) of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "bfb08070-26f4-4cc3-976f-655ca28ba32f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 255GB\n",
+       "Dimensions:                    (Time: 91, y: 250, x: 250, soil_layers_stag: 5,\n",
+       "                                z: 226, x_stag: 251, y_stag: 251, z_stag: 227,\n",
+       "                                force_layers: 751)\n",
+       "Coordinates: (12/15)\n",
+       "    CLAT                       (y, x) float32 250kB dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;\n",
+       "    XLAT                       (y, x) float32 250kB dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;\n",
+       "    XLONG                      (y, x) float32 250kB dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;\n",
+       "    XTIME                      (Time) datetime64[ns] 728B dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;\n",
+       "    XLAT_U                     (y, x_stag) float32 251kB dask.array&lt;chunksize=(125, 126), meta=np.ndarray&gt;\n",
+       "    XLONG_U                    (y, x_stag) float32 251kB dask.array&lt;chunksize=(125, 126), meta=np.ndarray&gt;\n",
+       "    ...                         ...\n",
+       "  * z_stag                     (z_stag) float32 908B 1.0 0.9959 ... 0.002178 0.0\n",
+       "  * Time                       (Time) datetime64[ns] 728B 2019-04-04T12:00:00...\n",
+       "  * y_stag                     (y_stag) float64 2kB -1.25e+04 ... 1.25e+04\n",
+       "  * y                          (y) float64 2kB -1.245e+04 ... 1.245e+04\n",
+       "  * x                          (x) float64 2kB -1.245e+04 ... 1.245e+04\n",
+       "  * x_stag                     (x_stag) float64 2kB -1.25e+04 ... 1.25e+04\n",
+       "Dimensions without coordinates: soil_layers_stag, force_layers\n",
+       "Data variables: (12/251)\n",
+       "    Times                      (Time) |S19 2kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    LU_INDEX                   (Time, y, x) float32 23MB dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;\n",
+       "    ZS                         (Time, soil_layers_stag) float32 2kB dask.array&lt;chunksize=(1, 5), meta=np.ndarray&gt;\n",
+       "    DZS                        (Time, soil_layers_stag) float32 2kB dask.array&lt;chunksize=(1, 5), meta=np.ndarray&gt;\n",
+       "    VAR_SSO                    (Time, y, x) float32 23MB dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;\n",
+       "    U                          (Time, z, y, x_stag) float32 5GB dask.array&lt;chunksize=(1, 226, 125, 126), meta=np.ndarray&gt;\n",
+       "    ...                         ...\n",
+       "    geopotential               (Time, z_stag, y, x) float32 5GB dask.array&lt;chunksize=(1, 227, 125, 125), meta=np.ndarray&gt;\n",
+       "    geopotential_height        (Time, z_stag, y, x) float32 5GB dask.array&lt;chunksize=(1, 227, 125, 125), meta=np.ndarray&gt;\n",
+       "    wind_east                  (Time, z, y, x) float32 5GB dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;\n",
+       "    wind_north                 (Time, z, y, x) float32 5GB dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;\n",
+       "    wind_east_10               (Time, y, x) float32 23MB dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;\n",
+       "    wind_north_10              (Time, y, x) float32 23MB dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;\n",
+       "Attributes: (12/142)\n",
+       "    TITLE:                                  OUTPUT FROM WRF V3.8.1 MODEL\n",
+       "    START_DATE:                            2019-04-04_12:00:00\n",
+       "    SIMULATION_START_DATE:                 2019-04-04_12:00:00\n",
+       "    WEST-EAST_GRID_DIMENSION:              251\n",
+       "    SOUTH-NORTH_GRID_DIMENSION:            251\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:             227\n",
+       "    ...                                    ...\n",
+       "    config_aerosol:                        NA\n",
+       "    config_forecast_time:                  15.0 h\n",
+       "    config_boundary_method:                Periodic\n",
+       "    config_microphysics:                   Thompson (mp_physics=8)\n",
+       "    config_nickname:                       runlas20190404v1addhm\n",
+       "    simulation_origin_host:                cumulus-login2.ccs.ornl.gov</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5b35e744-fe8c-42ea-890e-7fd24b65d279' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5b35e744-fe8c-42ea-890e-7fd24b65d279' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Time</span>: 91</li><li><span class='xr-has-index'>y</span>: 250</li><li><span class='xr-has-index'>x</span>: 250</li><li><span>soil_layers_stag</span>: 5</li><li><span class='xr-has-index'>z</span>: 226</li><li><span class='xr-has-index'>x_stag</span>: 251</li><li><span class='xr-has-index'>y_stag</span>: 251</li><li><span class='xr-has-index'>z_stag</span>: 227</li><li><span>force_layers</span>: 751</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6e23eb9c-38a7-4906-a780-2c5b06eb3416' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e23eb9c-38a7-4906-a780-2c5b06eb3416' class='xr-section-summary' >Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>CLAT</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;</div><input id='attrs-1919808f-cc4d-43bb-a8f4-4c84407aa476' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1919808f-cc4d-43bb-a8f4-4c84407aa476' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8ba69bd-7182-4aa0-bccc-59220569f46b' class='xr-var-data-in' type='checkbox'><label for='data-a8ba69bd-7182-4aa0-bccc-59220569f46b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>COMPUTATIONAL GRID LATITUDE, SOUTH IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 244.14 kiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (250, 250) </td>\n",
+       "                        <td> (125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">250</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLAT</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;</div><input id='attrs-e81f62a9-5cc1-4293-90e9-0d221e35b08a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e81f62a9-5cc1-4293-90e9-0d221e35b08a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-208618c4-84d7-4b29-ad4d-01ccc370a5aa' class='xr-var-data-in' type='checkbox'><label for='data-208618c4-84d7-4b29-ad4d-01ccc370a5aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LATITUDE, SOUTH IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 244.14 kiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (250, 250) </td>\n",
+       "                        <td> (125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">250</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLONG</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;</div><input id='attrs-55f8dbf4-4342-4c0d-b179-767f8622dab8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55f8dbf4-4342-4c0d-b179-767f8622dab8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26410bb8-e2f3-4bb4-bac2-228660570e32' class='xr-var-data-in' type='checkbox'><label for='data-26410bb8-e2f3-4bb4-bac2-228660570e32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LONGITUDE, WEST IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 244.14 kiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (250, 250) </td>\n",
+       "                        <td> (125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">250</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XTIME</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-16e3d42a-4f99-4fd9-b7e8-72065aacde0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16e3d42a-4f99-4fd9-b7e8-72065aacde0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe5dbbc4-e990-4f4a-808d-b3c3f35b3058' class='xr-var-data-in' type='checkbox'><label for='data-fe5dbbc4-e990-4f4a-808d-b3c3f35b3058' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>minutes since 2019-04-04 12:00:00</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 728 B </td>\n",
+       "                        <td> 48 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLAT_U</span></div><div class='xr-var-dims'>(y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(125, 126), meta=np.ndarray&gt;</div><input id='attrs-9c94f7da-0081-458b-8673-460c51a5a5b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c94f7da-0081-458b-8673-460c51a5a5b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9858bcd1-4f7b-4b33-a608-62398d1eaebf' class='xr-var-data-in' type='checkbox'><label for='data-9858bcd1-4f7b-4b33-a608-62398d1eaebf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LATITUDE, SOUTH IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 245.12 kiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (250, 251) </td>\n",
+       "                        <td> (125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"169\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"59\" x2=\"120\" y2=\"59\" />\n",
+       "  <line x1=\"0\" y1=\"119\" x2=\"120\" y2=\"119\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"119\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"119\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"119\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,119.5219123505976 0.0,119.5219123505976\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"139.521912\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >251</text>\n",
+       "  <text x=\"140.000000\" y=\"59.760956\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,59.760956)\">250</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLONG_U</span></div><div class='xr-var-dims'>(y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(125, 126), meta=np.ndarray&gt;</div><input id='attrs-26bb4121-5e67-4b50-a70c-3a9e50fd54f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26bb4121-5e67-4b50-a70c-3a9e50fd54f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c6440c5-6e25-4805-8075-c82e0dfe63d8' class='xr-var-data-in' type='checkbox'><label for='data-1c6440c5-6e25-4805-8075-c82e0dfe63d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LONGITUDE, WEST IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 245.12 kiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (250, 251) </td>\n",
+       "                        <td> (125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"169\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"59\" x2=\"120\" y2=\"59\" />\n",
+       "  <line x1=\"0\" y1=\"119\" x2=\"120\" y2=\"119\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"119\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"119\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"119\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,119.5219123505976 0.0,119.5219123505976\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"139.521912\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >251</text>\n",
+       "  <text x=\"140.000000\" y=\"59.760956\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,59.760956)\">250</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLAT_V</span></div><div class='xr-var-dims'>(y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(126, 125), meta=np.ndarray&gt;</div><input id='attrs-6a6a72bd-8e6d-4145-b25d-9c27e3b40246' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a6a72bd-8e6d-4145-b25d-9c27e3b40246' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a21b62ad-91ec-4e99-a7cf-80f2d77a7cff' class='xr-var-data-in' type='checkbox'><label for='data-a21b62ad-91ec-4e99-a7cf-80f2d77a7cff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LATITUDE, SOUTH IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 245.12 kiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (251, 250) </td>\n",
+       "                        <td> (126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"169\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"119\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"119\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"119\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"120\" />\n",
+       "  <line x1=\"119\" y1=\"0\" x2=\"119\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"59.760956\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
+       "  <text x=\"139.521912\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,139.521912,60.000000)\">251</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLONG_V</span></div><div class='xr-var-dims'>(y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(126, 125), meta=np.ndarray&gt;</div><input id='attrs-480c34bb-a947-4576-a8c6-866df1c63caa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-480c34bb-a947-4576-a8c6-866df1c63caa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d7d488d-fbf3-4a68-8c8e-c76030c6a59b' class='xr-var-data-in' type='checkbox'><label for='data-9d7d488d-fbf3-4a68-8c8e-c76030c6a59b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LONGITUDE, WEST IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 245.12 kiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (251, 250) </td>\n",
+       "                        <td> (126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"169\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"119\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"120\" x2=\"119\" y2=\"120\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"59.760956\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
+       "  <text x=\"139.521912\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,139.521912,60.000000)\">251</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.9979 0.9938 ... 0.005743 0.001089</div><input id='attrs-48a3f7bb-d123-4719-aa4a-6bde8c442103' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48a3f7bb-d123-4719-aa4a-6bde8c442103' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8205fa12-50ca-4214-8bc3-72c524c8ac0f' class='xr-var-data-in' type='checkbox'><label for='data-8205fa12-50ca-4214-8bc3-72c524c8ac0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>eta values on half (mass) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>standard_name :</span></dt><dd>atmosphere_sigma_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([0.997944, 0.993847, 0.989777, ..., 0.01304 , 0.005743, 0.001089],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_stag</span></div><div class='xr-var-dims'>(z_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 0.9959 0.9918 ... 0.002178 0.0</div><input id='attrs-4a5c899a-a4e0-4f2e-9f07-99b83b7339fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a5c899a-a4e0-4f2e-9f07-99b83b7339fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c8d30b1-d8e1-4cc0-838a-d83cc46c7eb0' class='xr-var-data-in' type='checkbox'><label for='data-7c8d30b1-d8e1-4cc0-838a-d83cc46c7eb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>eta values on full (w) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Z</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd><dt><span>standard_name :</span></dt><dd>atmosphere_sigma_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1.      , 0.995887, 0.991808, ..., 0.009308, 0.002178, 0.      ],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Time</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-04-04T12:00:00 ... 2019-04-...</div><input id='attrs-f62ea981-9fc4-4195-8219-159b32cf96ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f62ea981-9fc4-4195-8219-159b32cf96ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8c3deb9-7c1d-473d-8ab6-fa81a0428937' class='xr-var-data-in' type='checkbox'><label for='data-e8c3deb9-7c1d-473d-8ab6-fa81a0428937' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>minutes since 2019-04-04 12:00:00</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-04-04T12:00:00.000000000&#x27;, &#x27;2019-04-04T12:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T12:20:00.000000000&#x27;, &#x27;2019-04-04T12:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T12:40:00.000000000&#x27;, &#x27;2019-04-04T12:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T13:00:00.000000000&#x27;, &#x27;2019-04-04T13:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T13:20:00.000000000&#x27;, &#x27;2019-04-04T13:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T13:40:00.000000000&#x27;, &#x27;2019-04-04T13:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T14:00:00.000000000&#x27;, &#x27;2019-04-04T14:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T14:20:00.000000000&#x27;, &#x27;2019-04-04T14:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T14:40:00.000000000&#x27;, &#x27;2019-04-04T14:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T15:00:00.000000000&#x27;, &#x27;2019-04-04T15:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T15:20:00.000000000&#x27;, &#x27;2019-04-04T15:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T15:40:00.000000000&#x27;, &#x27;2019-04-04T15:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T16:00:00.000000000&#x27;, &#x27;2019-04-04T16:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T16:20:00.000000000&#x27;, &#x27;2019-04-04T16:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T16:40:00.000000000&#x27;, &#x27;2019-04-04T16:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T17:00:00.000000000&#x27;, &#x27;2019-04-04T17:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T17:20:00.000000000&#x27;, &#x27;2019-04-04T17:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T17:40:00.000000000&#x27;, &#x27;2019-04-04T17:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T18:00:00.000000000&#x27;, &#x27;2019-04-04T18:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T18:20:00.000000000&#x27;, &#x27;2019-04-04T18:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T18:40:00.000000000&#x27;, &#x27;2019-04-04T18:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T19:00:00.000000000&#x27;, &#x27;2019-04-04T19:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T19:20:00.000000000&#x27;, &#x27;2019-04-04T19:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T19:40:00.000000000&#x27;, &#x27;2019-04-04T19:50:00.000000000&#x27;,\n",
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+       "       &#x27;2019-04-04T20:20:00.000000000&#x27;, &#x27;2019-04-04T20:30:00.000000000&#x27;,\n",
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+       "       &#x27;2019-04-05T02:20:00.000000000&#x27;, &#x27;2019-04-05T02:30:00.000000000&#x27;,\n",
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+       "       &#x27;2019-04-05T03:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_stag</span></div><div class='xr-var-dims'>(y_stag)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.25e+04 -1.24e+04 ... 1.25e+04</div><input id='attrs-285fec1c-3511-4092-a07e-eae4f62a0f53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-285fec1c-3511-4092-a07e-eae4f62a0f53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af15ec18-3d9d-49f5-82ad-238cf8eeb84f' class='xr-var-data-in' type='checkbox'><label for='data-af15ec18-3d9d-49f5-82ad-238cf8eeb84f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([-12500., -12400., -12300., ...,  12300.,  12400.,  12500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.245e+04 -1.235e+04 ... 1.245e+04</div><input id='attrs-4bfeab5d-7e9a-4be4-8145-3e6a288a7d37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bfeab5d-7e9a-4be4-8145-3e6a288a7d37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c317e692-2c66-49cd-9c5a-417662381910' class='xr-var-data-in' type='checkbox'><label for='data-c317e692-2c66-49cd-9c5a-417662381910' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-12450., -12350., -12250., ...,  12250.,  12350.,  12450.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.245e+04 -1.235e+04 ... 1.245e+04</div><input id='attrs-065da4d8-8fe7-444b-b70c-6f567aad8717' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-065da4d8-8fe7-444b-b70c-6f567aad8717' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7a9e5b9-d414-44f0-9224-086b94b2fb5b' class='xr-var-data-in' type='checkbox'><label for='data-d7a9e5b9-d414-44f0-9224-086b94b2fb5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-12450., -12350., -12250., ...,  12250.,  12350.,  12450.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x_stag</span></div><div class='xr-var-dims'>(x_stag)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.25e+04 -1.24e+04 ... 1.25e+04</div><input id='attrs-755b33db-aa2f-4255-bfb6-d5a147c44895' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-755b33db-aa2f-4255-bfb6-d5a147c44895' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8a8dcc4-df64-4c10-8a44-c5d113c80f8f' class='xr-var-data-in' type='checkbox'><label for='data-d8a8dcc4-df64-4c10-8a44-c5d113c80f8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([-12500., -12400., -12300., ...,  12300.,  12400.,  12500.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23f2f944-6866-4ea4-b282-fd40c42ed6f8' class='xr-section-summary-in' type='checkbox'  ><label for='section-23f2f944-6866-4ea4-b282-fd40c42ed6f8' class='xr-section-summary' >Data variables: <span>(251)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Times</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>|S19</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-b62760a5-02c9-4b0c-9193-40bafcaf0829' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b62760a5-02c9-4b0c-9193-40bafcaf0829' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75ddf9df-0bb1-4a5a-b44e-463035104358' class='xr-var-data-in' type='checkbox'><label for='data-75ddf9df-0bb1-4a5a-b44e-463035104358' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.69 kiB </td>\n",
+       "                        <td> 19 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> |S19 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LU_INDEX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-da71e08b-77c4-45fb-91ac-2935eebb29a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da71e08b-77c4-45fb-91ac-2935eebb29a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6275f53a-6366-4939-b05d-88be36e1d185' class='xr-var-data-in' type='checkbox'><label for='data-6275f53a-6366-4939-b05d-88be36e1d185' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LAND USE CATEGORY</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZS</span></div><div class='xr-var-dims'>(Time, soil_layers_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5), meta=np.ndarray&gt;</div><input id='attrs-95a5b064-024f-4f7d-acaa-e22c92bf6677' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95a5b064-024f-4f7d-acaa-e22c92bf6677' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d23f52b7-eef1-48f4-8927-3f8cc4d1546e' class='xr-var-data-in' type='checkbox'><label for='data-d23f52b7-eef1-48f4-8927-3f8cc4d1546e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>DEPTHS OF CENTERS OF SOIL LAYERS</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.78 kiB </td>\n",
+       "                        <td> 20 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 5) </td>\n",
+       "                        <td> (1, 5) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DZS</span></div><div class='xr-var-dims'>(Time, soil_layers_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5), meta=np.ndarray&gt;</div><input id='attrs-22e66f48-0895-4fa3-8803-52b433fc3b9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22e66f48-0895-4fa3-8803-52b433fc3b9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e42a26de-fc2e-426c-b778-079cf2e768a3' class='xr-var-data-in' type='checkbox'><label for='data-e42a26de-fc2e-426c-b778-079cf2e768a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>THICKNESSES OF SOIL LAYERS</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.78 kiB </td>\n",
+       "                        <td> 20 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 5) </td>\n",
+       "                        <td> (1, 5) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VAR_SSO</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1810528f-f1c3-4fb8-a25f-a1cebbe43004' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1810528f-f1c3-4fb8-a25f-a1cebbe43004' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e44a609-480c-4b07-ad34-1f26f0e25e23' class='xr-var-data-in' type='checkbox'><label for='data-6e44a609-480c-4b07-ad34-1f26f0e25e23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Variance of Subgrid Scale Orography MSL</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
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+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(Time, z, y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 126), meta=np.ndarray&gt;</div><input id='attrs-381bb22c-04dd-45dd-bb4d-01133ae22b0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-381bb22c-04dd-45dd-bb4d-01133ae22b0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9244349c-6fd4-4bf8-bc10-ddc9ebb36c7b' class='xr-var-data-in' type='checkbox'><label for='data-9244349c-6fd4-4bf8-bc10-ddc9ebb36c7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>x-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>x_wind</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.81 GiB </td>\n",
+       "                        <td> 13.58 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 251) </td>\n",
+       "                        <td> (1, 226, 125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V</span></div><div class='xr-var-dims'>(Time, z, y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 126, 125), meta=np.ndarray&gt;</div><input id='attrs-9a697c4e-4c7f-4ac2-91f1-b7bde621c93f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a697c4e-4c7f-4ac2-91f1-b7bde621c93f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba157987-b8f5-4565-8053-99d0fc6d5c8f' class='xr-var-data-in' type='checkbox'><label for='data-ba157987-b8f5-4565-8053-99d0fc6d5c8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>y-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>y_wind</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.81 GiB </td>\n",
+       "                        <td> 13.58 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 251, 250) </td>\n",
+       "                        <td> (1, 226, 126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W</span></div><div class='xr-var-dims'>(Time, z_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 227, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-00162281-1a03-4499-92a8-1696a2a93b7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00162281-1a03-4499-92a8-1696a2a93b7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37f6872d-2714-458a-803b-cf5cd1cad95e' class='xr-var-data-in' type='checkbox'><label for='data-37f6872d-2714-458a-803b-cf5cd1cad95e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>z-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd>Z</dd><dt><span>standard_name :</span></dt><dd>upward_air_velocity</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HFX_FORCE</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-6cc1ddf0-175e-4d9b-880a-2f88e657c0ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6cc1ddf0-175e-4d9b-880a-2f88e657c0ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5bbe398-dc96-4bbc-a875-1e04c2a7f928' class='xr-var-data-in' type='checkbox'><label for='data-c5bbe398-dc96-4bbc-a875-1e04c2a7f928' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>SCM ideal surface sensible heat flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
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+       "                        <th> Bytes </th>\n",
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+       "                        <td> 24 B </td>\n",
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+       "                    <tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LH_FORCE</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-d7260ede-befe-492b-8341-ed7ddf5e3258' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7260ede-befe-492b-8341-ed7ddf5e3258' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74890083-e91e-490d-a54c-6ed35de02d20' class='xr-var-data-in' type='checkbox'><label for='data-74890083-e91e-490d-a54c-6ed35de02d20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>SCM ideal surface latent heat flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                        <td> 24 B </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HFX_FORCE_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-4d7f6a29-c875-42ae-a322-5b10f89deb00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d7f6a29-c875-42ae-a322-5b10f89deb00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83124468-fe11-464c-bcf1-657c707a9002' class='xr-var-data-in' type='checkbox'><label for='data-83124468-fe11-464c-bcf1-657c707a9002' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>SCM ideal surface sensible heat flux tendency</dd><dt><span>units :</span></dt><dd>W m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LH_FORCE_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-a590f77a-b747-4187-898d-03d2a51f02d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a590f77a-b747-4187-898d-03d2a51f02d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bc6ceae-2439-4e88-86d5-84ee1e306419' class='xr-var-data-in' type='checkbox'><label for='data-5bc6ceae-2439-4e88-86d5-84ee1e306419' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>SCM ideal surface latent heat flux tendency</dd><dt><span>units :</span></dt><dd>W m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSK_FORCE_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-d68f9254-6e55-4c8f-83de-2b9b66788e9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d68f9254-6e55-4c8f-83de-2b9b66788e9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59f88b5e-2443-43ea-8483-3ac8a5ff6966' class='xr-var-data-in' type='checkbox'><label for='data-59f88b5e-2443-43ea-8483-3ac8a5ff6966' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>SCM ideal surface skin temperature tendency</dd><dt><span>units :</span></dt><dd>W m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MU</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-7932ffe4-b70b-443c-b622-55c8ffb70a48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7932ffe4-b70b-443c-b622-55c8ffb70a48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bd7a3b8-9322-4cb9-b344-51cbf1262156' class='xr-var-data-in' type='checkbox'><label for='data-4bd7a3b8-9322-4cb9-b344-51cbf1262156' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>perturbation dry air mass in column</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MUB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1b3a00e2-3f34-4eef-b81d-abd3111f1b1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b3a00e2-3f34-4eef-b81d-abd3111f1b1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50a3fc41-98d5-4767-8bd1-bd0cc6632b3e' class='xr-var-data-in' type='checkbox'><label for='data-50a3fc41-98d5-4767-8bd1-bd0cc6632b3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>base state dry air mass in column</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NEST_POS</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-43e2bda5-8303-4302-85d9-623403860556' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43e2bda5-8303-4302-85d9-623403860556' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e17db6b1-a419-43b3-9fa9-9054647891d1' class='xr-var-data-in' type='checkbox'><label for='data-e17db6b1-a419-43b3-9fa9-9054647891d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>-</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TKE</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-fdc313a4-7ecd-4c44-afc4-4de5aca6f6d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdc313a4-7ecd-4c44-afc4-4de5aca6f6d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afc5feb3-58ca-434f-b142-696b38288a20' class='xr-var-data-in' type='checkbox'><label for='data-afc5feb3-58ca-434f-b142-696b38288a20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>TURBULENCE KINETIC ENERGY</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALT</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e11824f2-8882-43d5-84a9-e7b42278eea1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e11824f2-8882-43d5-84a9-e7b42278eea1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74750c9b-31dd-46b1-8a47-2a1b6cca0aed' class='xr-var-data-in' type='checkbox'><label for='data-74750c9b-31dd-46b1-8a47-2a1b6cca0aed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>inverse density</dd><dt><span>units :</span></dt><dd>m3 kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "  <text x=\"317.811765\" y=\"123.811765\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,317.811765,123.811765)\">250</text>\n",
+       "  <text x=\"135.905882\" y=\"171.905882\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,135.905882,171.905882)\">226</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FNM</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-bf8ea829-aa31-47bb-a57b-fab52e8da661' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf8ea829-aa31-47bb-a57b-fab52e8da661' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7497fac-4129-4e91-a4d8-602bce4149ae' class='xr-var-data-in' type='checkbox'><label for='data-d7497fac-4129-4e91-a4d8-602bce4149ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>upper weight for vertical stretching</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
+       "  <text x=\"140.000000\" y=\"24.159292\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,24.159292)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FNP</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-4055db17-7d6d-410b-aa8c-20ca4c0a3b7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4055db17-7d6d-410b-aa8c-20ca4c0a3b7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd298732-23be-4449-8f02-394a175b7740' class='xr-var-data-in' type='checkbox'><label for='data-dd298732-23be-4449-8f02-394a175b7740' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>lower weight for vertical stretching</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
+       "  <text x=\"140.000000\" y=\"24.159292\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,24.159292)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RDNW</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-9b35ab49-944d-47e0-8e25-29bc74fd04d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b35ab49-944d-47e0-8e25-29bc74fd04d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97f4d450-1ea5-4acf-a287-e331ef004852' class='xr-var-data-in' type='checkbox'><label for='data-97f4d450-1ea5-4acf-a287-e331ef004852' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>inverse d(eta) values between full (w) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RDN</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-1d5eedcd-2d5f-48f1-9f2f-f8b9f866a16d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d5eedcd-2d5f-48f1-9f2f-f8b9f866a16d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3810a5a3-0a96-4b32-8ae0-c2e02060788c' class='xr-var-data-in' type='checkbox'><label for='data-3810a5a3-0a96-4b32-8ae0-c2e02060788c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>inverse d(eta) values between half (mass) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
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+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
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+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
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+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
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+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DNW</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-401399d3-f285-4af8-aacc-0c0dd117e487' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-401399d3-f285-4af8-aacc-0c0dd117e487' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26a4e82e-7e74-431d-9827-8da207fda4f9' class='xr-var-data-in' type='checkbox'><label for='data-26a4e82e-7e74-431d-9827-8da207fda4f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>d(eta) values between full (w) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DN</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-be62ceb6-0630-453c-9567-24ae3ef049d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be62ceb6-0630-453c-9567-24ae3ef049d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be033e92-de8a-4860-8f15-a0a947f56eab' class='xr-var-data-in' type='checkbox'><label for='data-be033e92-de8a-4860-8f15-a0a947f56eab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>d(eta) values between half (mass) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CFN</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-439b4dea-8d68-4ff0-9def-b31b8849ec36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-439b4dea-8d68-4ff0-9def-b31b8849ec36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f24c9bc-86f9-4afe-a527-7f3ef2705b0b' class='xr-var-data-in' type='checkbox'><label for='data-9f24c9bc-86f9-4afe-a527-7f3ef2705b0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>extrapolation constant</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CFN1</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-28ac4831-0214-46e6-99bf-f72bfe9f935b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28ac4831-0214-46e6-99bf-f72bfe9f935b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfea9923-47d0-4a44-9b55-d3cbd29c6e95' class='xr-var-data-in' type='checkbox'><label for='data-cfea9923-47d0-4a44-9b55-d3cbd29c6e95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>extrapolation constant</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THIS_IS_AN_IDEAL_RUN</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-db331e45-8f81-4fb9-bcef-5277b6028161' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db331e45-8f81-4fb9-bcef-5277b6028161' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5082b20-ad87-4da9-a58e-a58ddf073f5d' class='xr-var-data-in' type='checkbox'><label for='data-a5082b20-ad87-4da9-a58e-a58ddf073f5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>T/F flag: this is an ARW ideal simulation</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P_HYD</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b50c610e-a06a-460a-8b3a-1a51956d4e4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b50c610e-a06a-460a-8b3a-1a51956d4e4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5812f627-59d4-4dcb-9412-0bdee973ca23' class='xr-var-data-in' type='checkbox'><label for='data-5812f627-59d4-4dcb-9412-0bdee973ca23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>hydrostatic pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Q2</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-7c4876f8-3f4e-401f-8fa9-9a43c856a3ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c4876f8-3f4e-401f-8fa9-9a43c856a3ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7eb1e4b-1dd8-40a7-be2b-6dffb24bfb42' class='xr-var-data-in' type='checkbox'><label for='data-c7eb1e4b-1dd8-40a7-be2b-6dffb24bfb42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>QV at 2 M</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>humidity_mixing_ratio</dd><dt><span>long_name :</span></dt><dd>humidity_mixing_ratio_at_2m_agl</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T2</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-c2f728d1-05ad-415e-8abe-1f954d5c1ffb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2f728d1-05ad-415e-8abe-1f954d5c1ffb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbd71f11-3a9c-494b-a4ad-27b573fae888' class='xr-var-data-in' type='checkbox'><label for='data-bbd71f11-3a9c-494b-a4ad-27b573fae888' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>TEMP at 2 M</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>air_temperature_at_2m_agl</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TH2</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-926d8485-a43b-4d1f-b71d-9de41ff9cd1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-926d8485-a43b-4d1f-b71d-9de41ff9cd1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ceb6b42-4f96-4270-89c8-d7281f878c63' class='xr-var-data-in' type='checkbox'><label for='data-5ceb6b42-4f96-4270-89c8-d7281f878c63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>POT TEMP at 2 M</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>air_potential_temperature</dd><dt><span>long_name :</span></dt><dd>air_potential_temperature_at_2m_agl</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PSFC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-5b357c8b-f5b4-4422-854e-3c93cf8ab03b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b357c8b-f5b4-4422-854e-3c93cf8ab03b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af903051-6a6f-4fb6-8591-147709e2cb9d' class='xr-var-data-in' type='checkbox'><label for='data-af903051-6a6f-4fb6-8591-147709e2cb9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SFC PRESSURE</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>air_pressure_at_surface</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U10</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-af46f0a9-3b91-4c2c-b9e2-6f9ea5390bc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af46f0a9-3b91-4c2c-b9e2-6f9ea5390bc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bd264ad-3c9e-4efc-b412-f92aac181352' class='xr-var-data-in' type='checkbox'><label for='data-8bd264ad-3c9e-4efc-b412-f92aac181352' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>U at 10 M</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>x_wind</dd><dt><span>long_name :</span></dt><dd>x_wind_at_10m_agl</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V10</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-86cede65-f3c6-4f05-b29b-baf707fc5157' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86cede65-f3c6-4f05-b29b-baf707fc5157' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24fb82de-f02b-4c8a-bd5e-b9c356e41e6a' class='xr-var-data-in' type='checkbox'><label for='data-24fb82de-f02b-4c8a-bd5e-b9c356e41e6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>V at 10 M</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>y_wind</dd><dt><span>long_name :</span></dt><dd>y_wind_at_10m_agl</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RDX</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-e9a645ea-f53b-4a7a-8567-96349c75d01f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9a645ea-f53b-4a7a-8567-96349c75d01f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bb5aaa1-a3fc-4802-a328-2ad3ef2bf74d' class='xr-var-data-in' type='checkbox'><label for='data-5bb5aaa1-a3fc-4802-a328-2ad3ef2bf74d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>INVERSE X GRID LENGTH</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RDY</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-445f0903-2771-4183-be72-850c092b0b51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-445f0903-2771-4183-be72-850c092b0b51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0719906a-0486-4ddd-8436-75b6e71db6cb' class='xr-var-data-in' type='checkbox'><label for='data-0719906a-0486-4ddd-8436-75b6e71db6cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>INVERSE Y GRID LENGTH</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RESM</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-818ae424-ee97-491c-a00f-d39098384c43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-818ae424-ee97-491c-a00f-d39098384c43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0edda090-2c4d-4d8c-a651-f478194f0d4a' class='xr-var-data-in' type='checkbox'><label for='data-0edda090-2c4d-4d8c-a651-f478194f0d4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TIME WEIGHT CONSTANT FOR SMALL STEPS</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZETATOP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-d913a4df-640c-49dd-940e-aa45fef94ce7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d913a4df-640c-49dd-940e-aa45fef94ce7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3cced7a-453d-424c-a57b-f4daf0d58eb8' class='xr-var-data-in' type='checkbox'><label for='data-b3cced7a-453d-424c-a57b-f4daf0d58eb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>ZETA AT MODEL TOP</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CF1</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-e8677ed7-a056-4fcf-80ea-dc8e07213585' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8677ed7-a056-4fcf-80ea-dc8e07213585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa00b313-0a6a-4471-b703-32497a50ae15' class='xr-var-data-in' type='checkbox'><label for='data-fa00b313-0a6a-4471-b703-32497a50ae15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>2nd order extrapolation constant</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CF2</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-1cf2600a-4614-4360-bdab-b68de9910de5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cf2600a-4614-4360-bdab-b68de9910de5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eec9d95-b3a3-4bc5-8daf-e51e99c15957' class='xr-var-data-in' type='checkbox'><label for='data-9eec9d95-b3a3-4bc5-8daf-e51e99c15957' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>2nd order extrapolation constant</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CF3</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-76a67ce8-cf53-4b0a-8bf4-29a4b401332b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76a67ce8-cf53-4b0a-8bf4-29a4b401332b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ac8cfc4-2acb-446a-bbd8-5e5b32f00363' class='xr-var-data-in' type='checkbox'><label for='data-2ac8cfc4-2acb-446a-bbd8-5e5b32f00363' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>2nd order extrapolation constant</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ITIMESTEP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-e428218f-c7f3-4a96-9de4-3fef9af5ebf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e428218f-c7f3-4a96-9de4-3fef9af5ebf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecd221be-3b38-47bf-985e-82651971e82b' class='xr-var-data-in' type='checkbox'><label for='data-ecd221be-3b38-47bf-985e-82651971e82b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd></dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QVAPOR</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-c422625e-ffe7-49d5-8b24-4c1c79a07e18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c422625e-ffe7-49d5-8b24-4c1c79a07e18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af94a18e-28e9-46ed-9181-0b1598159c70' class='xr-var-data-in' type='checkbox'><label for='data-af94a18e-28e9-46ed-9181-0b1598159c70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Water vapor mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>humidity_mixing_ratio</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QCLOUD</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-32da1b98-8b51-4971-9fe5-f67d0be560fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32da1b98-8b51-4971-9fe5-f67d0be560fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad955295-4c75-481e-8cfe-2c3bdec9a801' class='xr-var-data-in' type='checkbox'><label for='data-ad955295-4c75-481e-8cfe-2c3bdec9a801' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Cloud water mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QRAIN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-07de5f02-67f9-4458-af68-8acb41ecb721' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07de5f02-67f9-4458-af68-8acb41ecb721' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27f63f16-974e-4e33-8e40-5edd8bca5a8f' class='xr-var-data-in' type='checkbox'><label for='data-27f63f16-974e-4e33-8e40-5edd8bca5a8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Rain water mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QICE</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e5ec0b12-b478-40e6-9d34-312bf46fd37c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5ec0b12-b478-40e6-9d34-312bf46fd37c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b2681ab-1c36-43a5-a05d-c5a2268bdab1' class='xr-var-data-in' type='checkbox'><label for='data-8b2681ab-1c36-43a5-a05d-c5a2268bdab1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Ice mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QSNOW</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-cb9022ef-4327-46c2-aed9-2ddab023f909' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb9022ef-4327-46c2-aed9-2ddab023f909' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cbe4ae1-e677-4425-bdd9-2199a5f59d1b' class='xr-var-data-in' type='checkbox'><label for='data-4cbe4ae1-e677-4425-bdd9-2199a5f59d1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Snow mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QGRAUP</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-39942236-10eb-4415-9249-a005166c26b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39942236-10eb-4415-9249-a005166c26b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cd514fa-d786-45a7-95cd-579cca3a7455' class='xr-var-data-in' type='checkbox'><label for='data-5cd514fa-d786-45a7-95cd-579cca3a7455' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Graupel mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QNICE</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-c0d6c9ba-af3e-4171-a218-bbe127bbc019' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0d6c9ba-af3e-4171-a218-bbe127bbc019' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b25dd638-1617-4456-9c4d-095b847f76b2' class='xr-var-data-in' type='checkbox'><label for='data-b25dd638-1617-4456-9c4d-095b847f76b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Ice Number concentration</dd><dt><span>units :</span></dt><dd>  kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QNRAIN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a5fef45f-7928-4006-87ff-ecc4217d743a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5fef45f-7928-4006-87ff-ecc4217d743a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a22d3cae-fd04-4c64-b9a2-c59863ce9e16' class='xr-var-data-in' type='checkbox'><label for='data-a22d3cae-fd04-4c64-b9a2-c59863ce9e16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Rain Number concentration</dd><dt><span>units :</span></dt><dd>kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHDMAX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-080229c7-bc17-482a-b154-ae00a8cdf9b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-080229c7-bc17-482a-b154-ae00a8cdf9b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db523b84-1543-43b0-833e-3d65afb6d79b' class='xr-var-data-in' type='checkbox'><label for='data-db523b84-1543-43b0-833e-3d65afb6d79b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ANNUAL MAX VEG FRACTION</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHDMIN</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b4a9967e-7067-464e-b686-50cf66256a12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4a9967e-7067-464e-b686-50cf66256a12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90247c7c-193a-492d-aa34-ac92ced1069c' class='xr-var-data-in' type='checkbox'><label for='data-90247c7c-193a-492d-aa34-ac92ced1069c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ANNUAL MIN VEG FRACTION</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOALB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f5ddf920-9712-40ae-b91d-63ac710a0f5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5ddf920-9712-40ae-b91d-63ac710a0f5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffb6b298-e4fa-4ab5-8fb5-57ffa015f5bb' class='xr-var-data-in' type='checkbox'><label for='data-ffb6b298-e4fa-4ab5-8fb5-57ffa015f5bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ANNUAL MAX SNOW ALBEDO IN FRACTION</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSLB</span></div><div class='xr-var-dims'>(Time, soil_layers_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-858f4a45-2a27-405b-8449-d413edede896' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-858f4a45-2a27-405b-8449-d413edede896' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dfe402f-e421-4da8-a6e4-a20307b2a2fe' class='xr-var-data-in' type='checkbox'><label for='data-4dfe402f-e421-4da8-a6e4-a20307b2a2fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>SOIL TEMPERATURE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 108.48 MiB </td>\n",
+       "                        <td> 305.18 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 5, 250, 250) </td>\n",
+       "                        <td> (1, 5, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SMOIS</span></div><div class='xr-var-dims'>(Time, soil_layers_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-27b1eafa-78a1-4f5c-bc41-ffc1954e36fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27b1eafa-78a1-4f5c-bc41-ffc1954e36fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22353887-80b2-4130-aa73-248cb3b76b47' class='xr-var-data-in' type='checkbox'><label for='data-22353887-80b2-4130-aa73-248cb3b76b47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>SOIL MOISTURE</dd><dt><span>units :</span></dt><dd>m3 m-3</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 108.48 MiB </td>\n",
+       "                        <td> 305.18 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 5, 250, 250) </td>\n",
+       "                        <td> (1, 5, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "            </table>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SH2O</span></div><div class='xr-var-dims'>(Time, soil_layers_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-05a6b484-82f4-4e10-a9a2-12c84b98408f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05a6b484-82f4-4e10-a9a2-12c84b98408f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d008fca-4144-48ae-a2eb-03940c3268e1' class='xr-var-data-in' type='checkbox'><label for='data-3d008fca-4144-48ae-a2eb-03940c3268e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>SOIL LIQUID WATER</dd><dt><span>units :</span></dt><dd>m3 m-3</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 108.48 MiB </td>\n",
+       "                        <td> 305.18 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 5, 250, 250) </td>\n",
+       "                        <td> (1, 5, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SEAICE</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4aaa15d2-f83b-4fb1-8e1b-d932d88f54af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4aaa15d2-f83b-4fb1-8e1b-d932d88f54af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-637716de-5f54-487d-8a1d-de865ced4cb2' class='xr-var-data-in' type='checkbox'><label for='data-637716de-5f54-487d-8a1d-de865ced4cb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SEA ICE FLAG</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XICEM</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a26e905b-680f-40e9-8699-9ba7da9860b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a26e905b-680f-40e9-8699-9ba7da9860b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-052f6e72-19d0-4537-b050-21586f506864' class='xr-var-data-in' type='checkbox'><label for='data-052f6e72-19d0-4537-b050-21586f506864' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SEA ICE FLAG (PREVIOUS STEP)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SFROFF</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-0769cf39-0768-4148-a72e-a7ed66f17b48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0769cf39-0768-4148-a72e-a7ed66f17b48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c351ac29-c391-4af0-bd2c-14ca69b4838a' class='xr-var-data-in' type='checkbox'><label for='data-c351ac29-c391-4af0-bd2c-14ca69b4838a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SURFACE RUNOFF</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UDROFF</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-664f29c6-678a-48d2-9a94-25a062616fef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-664f29c6-678a-48d2-9a94-25a062616fef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58b6aafe-ba20-4b27-be97-c2194007f8a3' class='xr-var-data-in' type='checkbox'><label for='data-58b6aafe-ba20-4b27-be97-c2194007f8a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>UNDERGROUND RUNOFF</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>IVGTYP</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-fdba1125-c396-4182-9619-feabf54f882f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdba1125-c396-4182-9619-feabf54f882f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da4c7afa-9a5c-4a53-8878-23c8d96f59bb' class='xr-var-data-in' type='checkbox'><label for='data-da4c7afa-9a5c-4a53-8878-23c8d96f59bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>DOMINANT VEGETATION CATEGORY</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ISLTYP</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-157ed892-1f8c-45be-8fc6-26735e8fb702' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-157ed892-1f8c-45be-8fc6-26735e8fb702' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-539ba6e9-3713-4f31-b7c2-69e9689d56fc' class='xr-var-data-in' type='checkbox'><label for='data-539ba6e9-3713-4f31-b7c2-69e9689d56fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>DOMINANT SOIL CATEGORY</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VEGFRA</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-d25e9625-b78d-46e3-8edc-d432303a3568' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d25e9625-b78d-46e3-8edc-d432303a3568' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c33df8f9-3dde-4728-a7f9-0e12fc261e46' class='xr-var-data-in' type='checkbox'><label for='data-c33df8f9-3dde-4728-a7f9-0e12fc261e46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>VEGETATION FRACTION</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>vegetation_area_fraction</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GRDFLX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-ee2553d5-c4ed-41fe-a899-248d8e982d50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee2553d5-c4ed-41fe-a899-248d8e982d50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-887b15c3-3c5b-4a6b-a402-930400e0b074' class='xr-var-data-in' type='checkbox'><label for='data-887b15c3-3c5b-4a6b-a402-930400e0b074' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>GROUND HEAT FLUX</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACGRDFLX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-240a6665-464b-482b-a279-a33905022c12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-240a6665-464b-482b-a279-a33905022c12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-396c5b05-1a83-4041-a5ae-80438f96e7da' class='xr-var-data-in' type='checkbox'><label for='data-396c5b05-1a83-4041-a5ae-80438f96e7da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED GROUND HEAT FLUX</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSNOM</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-8d178c4e-c370-4e67-a1a4-c469d90e7405' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d178c4e-c370-4e67-a1a4-c469d90e7405' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd076fb9-3f0d-4520-b594-8f5b33a22ebd' class='xr-var-data-in' type='checkbox'><label for='data-cd076fb9-3f0d-4520-b594-8f5b33a22ebd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED MELTED SNOW</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOW</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e4993e9d-732a-423c-97ac-7c2b3c6c63e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4993e9d-732a-423c-97ac-7c2b3c6c63e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18eb779f-9bf7-488d-b0ba-32b1548718fd' class='xr-var-data-in' type='checkbox'><label for='data-18eb779f-9bf7-488d-b0ba-32b1548718fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SNOW WATER EQUIVALENT</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOWH</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-63efcd45-b08a-44ca-9d75-3dee370cea58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63efcd45-b08a-44ca-9d75-3dee370cea58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d60ac08-c25e-4f65-ae37-2d2cdf28b004' class='xr-var-data-in' type='checkbox'><label for='data-9d60ac08-c25e-4f65-ae37-2d2cdf28b004' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>PHYSICAL SNOW DEPTH</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CANWAT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-45df360a-7657-4425-b694-ff72c239af04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45df360a-7657-4425-b694-ff72c239af04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82fdcbaa-e819-4f4f-b489-decc1cab7bee' class='xr-var-data-in' type='checkbox'><label for='data-82fdcbaa-e819-4f4f-b489-decc1cab7bee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>CANOPY WATER</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSTSK</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-65b8f570-65a7-4b71-8811-45bc1d8eba9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65b8f570-65a7-4b71-8811-45bc1d8eba9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f8e3422-546f-4b06-a281-5f53c9f3a96a' class='xr-var-data-in' type='checkbox'><label for='data-6f8e3422-546f-4b06-a281-5f53c9f3a96a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SKIN SEA SURFACE TEMPERATURE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>COSZEN</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a19d905f-ae88-419f-b10d-1a2d414e3e4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a19d905f-ae88-419f-b10d-1a2d414e3e4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d875547-874a-4c4c-bad6-8719d175c5db' class='xr-var-data-in' type='checkbox'><label for='data-1d875547-874a-4c4c-bad6-8719d175c5db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>COS of SOLAR ZENITH ANGLE</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LAI</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-3a0cfc18-0fab-4435-8478-47bb0ee41be7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a0cfc18-0fab-4435-8478-47bb0ee41be7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8a28bcc-af22-4860-94a8-2b64b7e91cad' class='xr-var-data-in' type='checkbox'><label for='data-c8a28bcc-af22-4860-94a8-2b64b7e91cad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LEAF AREA INDEX</dd><dt><span>units :</span></dt><dd>m-2/m-2</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>leaf_area_index</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VAR</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1d5c31ce-ec9b-428d-94d6-0d6f861501a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d5c31ce-ec9b-428d-94d6-0d6f861501a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e4754e8-1299-4001-bac7-0500b7bd38ba' class='xr-var-data-in' type='checkbox'><label for='data-6e4754e8-1299-4001-bac7-0500b7bd38ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>OROGRAPHIC VARIANCE</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_M</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-376d5611-b2d1-412c-8c88-36d0ec4f1be0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-376d5611-b2d1-412c-8c88-36d0ec4f1be0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90152c02-0658-4c5e-a2c9-ba53c09d09d3' class='xr-var-data-in' type='checkbox'><label for='data-90152c02-0658-4c5e-a2c9-ba53c09d09d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on mass grid</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_U</span></div><div class='xr-var-dims'>(Time, y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 126), meta=np.ndarray&gt;</div><input id='attrs-3667919c-25d0-4b80-ac3d-154896a01891' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3667919c-25d0-4b80-ac3d-154896a01891' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb39ef09-47a5-4d26-8cf5-044188aeb0e5' class='xr-var-data-in' type='checkbox'><label for='data-bb39ef09-47a5-4d26-8cf5-044188aeb0e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on u-grid</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 251) </td>\n",
+       "                        <td> (1, 125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_V</span></div><div class='xr-var-dims'>(Time, y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 126, 125), meta=np.ndarray&gt;</div><input id='attrs-5364ea74-351a-41b3-9ab7-9abd4ba6e038' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5364ea74-351a-41b3-9ab7-9abd4ba6e038' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f0d26fc-640b-4c24-b666-dbc845d29ae2' class='xr-var-data-in' type='checkbox'><label for='data-7f0d26fc-640b-4c24-b666-dbc845d29ae2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on v-grid</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 251, 250) </td>\n",
+       "                        <td> (1, 126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_MX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-ca4233e8-7494-4e98-8492-7db06ec8baec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca4233e8-7494-4e98-8492-7db06ec8baec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0518402d-4503-4918-9eef-8ca402fba482' class='xr-var-data-in' type='checkbox'><label for='data-0518402d-4503-4918-9eef-8ca402fba482' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on mass grid, x direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_MY</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-74322fcb-f884-4a38-890e-02b4ad31d2f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74322fcb-f884-4a38-890e-02b4ad31d2f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73327f81-3684-4eb5-aafe-98cde8d04f59' class='xr-var-data-in' type='checkbox'><label for='data-73327f81-3684-4eb5-aafe-98cde8d04f59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on mass grid, y direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_UX</span></div><div class='xr-var-dims'>(Time, y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 126), meta=np.ndarray&gt;</div><input id='attrs-6238e451-a6d7-40c8-bc19-f8ed368991f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6238e451-a6d7-40c8-bc19-f8ed368991f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c50f503-04e1-418d-b57c-6183cc362814' class='xr-var-data-in' type='checkbox'><label for='data-9c50f503-04e1-418d-b57c-6183cc362814' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on u-grid, x direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 251) </td>\n",
+       "                        <td> (1, 125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_UY</span></div><div class='xr-var-dims'>(Time, y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 126), meta=np.ndarray&gt;</div><input id='attrs-90ac5298-de66-49fb-8e6c-3cd3348b6535' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90ac5298-de66-49fb-8e6c-3cd3348b6535' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65cb52de-6b49-4889-8901-c1703d8ec796' class='xr-var-data-in' type='checkbox'><label for='data-65cb52de-6b49-4889-8901-c1703d8ec796' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on u-grid, y direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 251) </td>\n",
+       "                        <td> (1, 125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_VX</span></div><div class='xr-var-dims'>(Time, y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 126, 125), meta=np.ndarray&gt;</div><input id='attrs-3d6e7949-9a9b-4c25-8402-78ba05bd83a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d6e7949-9a9b-4c25-8402-78ba05bd83a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9a1c15b-f0ed-429f-9ffd-e4f1798635a9' class='xr-var-data-in' type='checkbox'><label for='data-d9a1c15b-f0ed-429f-9ffd-e4f1798635a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on v-grid, x direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 251, 250) </td>\n",
+       "                        <td> (1, 126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MF_VX_INV</span></div><div class='xr-var-dims'>(Time, y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 126, 125), meta=np.ndarray&gt;</div><input id='attrs-5e842f7e-544d-4640-bfbc-893e93cd146d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e842f7e-544d-4640-bfbc-893e93cd146d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06a73409-c11c-4e4a-b09a-fc9bdb9ceadc' class='xr-var-data-in' type='checkbox'><label for='data-06a73409-c11c-4e4a-b09a-fc9bdb9ceadc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Inverse map scale factor on v-grid, x direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 251, 250) </td>\n",
+       "                        <td> (1, 126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_VY</span></div><div class='xr-var-dims'>(Time, y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 126, 125), meta=np.ndarray&gt;</div><input id='attrs-5da41cf9-7dde-4ba6-abe0-40831678b971' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5da41cf9-7dde-4ba6-abe0-40831678b971' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0181b77-f5c3-4958-bbf2-21b60b0d4623' class='xr-var-data-in' type='checkbox'><label for='data-e0181b77-f5c3-4958-bbf2-21b60b0d4623' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on v-grid, y direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 251, 250) </td>\n",
+       "                        <td> (1, 126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>F</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-fae61a6e-9f18-487b-beff-ececa9a46781' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fae61a6e-9f18-487b-beff-ececa9a46781' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ded6b191-22af-4a8e-8ff5-9253746a78a0' class='xr-var-data-in' type='checkbox'><label for='data-ded6b191-22af-4a8e-8ff5-9253746a78a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Coriolis sine latitude term</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>E</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-881affef-fcea-4512-aa60-db829f7541c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-881affef-fcea-4512-aa60-db829f7541c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72686e01-4711-487b-a029-37b0b7d18384' class='xr-var-data-in' type='checkbox'><label for='data-72686e01-4711-487b-a029-37b0b7d18384' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Coriolis cosine latitude term</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SINALPHA</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-781731f5-9182-4be6-9dce-42c8b5c6e30e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-781731f5-9182-4be6-9dce-42c8b5c6e30e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d61319c8-ad09-423c-ba8c-c6d0344392b5' class='xr-var-data-in' type='checkbox'><label for='data-d61319c8-ad09-423c-ba8c-c6d0344392b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Local sine of map rotation</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>COSALPHA</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1cc70554-90cd-4b23-89ab-5297d9f04f65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cc70554-90cd-4b23-89ab-5297d9f04f65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5813ba3-ef0b-4cbd-93a3-ee887ec458f9' class='xr-var-data-in' type='checkbox'><label for='data-a5813ba3-ef0b-4cbd-93a3-ee887ec458f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Local cosine of map rotation</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HGT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-de513226-796a-4ac4-a798-c97deff6cb0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de513226-796a-4ac4-a798-c97deff6cb0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-428674c6-cecf-4515-ab79-84fdff6e43dc' class='xr-var-data-in' type='checkbox'><label for='data-428674c6-cecf-4515-ab79-84fdff6e43dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Terrain Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>surface_altitude</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSK</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-3ab014d5-39e7-4f37-8860-f3a1a27c9dc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ab014d5-39e7-4f37-8860-f3a1a27c9dc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbfef91b-e3aa-4451-b897-5b0700058c24' class='xr-var-data-in' type='checkbox'><label for='data-fbfef91b-e3aa-4451-b897-5b0700058c24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SURFACE SKIN TEMPERATURE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P_TOP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-c5834865-d00b-49e6-84e3-8512c3a07756' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5834865-d00b-49e6-84e3-8512c3a07756' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b787fd0f-9be1-47ad-9741-733c13370587' class='xr-var-data-in' type='checkbox'><label for='data-b787fd0f-9be1-47ad-9741-733c13370587' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>PRESSURE TOP OF THE MODEL</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "  <line x1=\"55\" y1=\"0\" x2=\"55\" y2=\"25\" />\n",
+       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
+       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
+       "  <line x1=\"79\" y1=\"0\" x2=\"79\" y2=\"25\" />\n",
+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T00</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-f05f56d6-67a3-4da3-a4d0-4b864572c0f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f05f56d6-67a3-4da3-a4d0-4b864572c0f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d17b66cc-ee00-423e-b3d2-0d84e1dc5bf5' class='xr-var-data-in' type='checkbox'><label for='data-d17b66cc-ee00-423e-b3d2-0d84e1dc5bf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>BASE STATE TEMPERATURE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"25\" />\n",
+       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
+       "  <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"25\" />\n",
+       "  <line x1=\"47\" y1=\"0\" x2=\"47\" y2=\"25\" />\n",
+       "  <line x1=\"55\" y1=\"0\" x2=\"55\" y2=\"25\" />\n",
+       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
+       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
+       "  <line x1=\"79\" y1=\"0\" x2=\"79\" y2=\"25\" />\n",
+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P00</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-f1c3c2cd-d0fc-4db9-8bc0-ebae11cb479d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1c3c2cd-d0fc-4db9-8bc0-ebae11cb479d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5d98913-ffd4-4b69-9497-e3f468802f7c' class='xr-var-data-in' type='checkbox'><label for='data-a5d98913-ffd4-4b69-9497-e3f468802f7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>BASE STATE PRESURE</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"25\" />\n",
+       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
+       "  <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"25\" />\n",
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+       "  <line x1=\"55\" y1=\"0\" x2=\"55\" y2=\"25\" />\n",
+       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
+       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
+       "  <line x1=\"79\" y1=\"0\" x2=\"79\" y2=\"25\" />\n",
+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-d228113a-ac47-4371-9bde-4dd74ed235d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d228113a-ac47-4371-9bde-4dd74ed235d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4ad1b1f-bc9a-4f49-8cf5-ce3113cad675' class='xr-var-data-in' type='checkbox'><label for='data-c4ad1b1f-bc9a-4f49-8cf5-ce3113cad675' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>BASE STATE LAPSE RATE</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
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+       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
+       "  <line x1=\"79\" y1=\"0\" x2=\"79\" y2=\"25\" />\n",
+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TISO</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-64e19704-b85b-4bf1-9a2e-3ba82517a66d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64e19704-b85b-4bf1-9a2e-3ba82517a66d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76b2560a-057d-41b8-a896-271e959bd6c3' class='xr-var-data-in' type='checkbox'><label for='data-76b2560a-057d-41b8-a896-271e959bd6c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TEMP AT WHICH THE BASE T TURNS CONST</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLP_STRAT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-fe9e19bd-8295-4f0c-8381-22bd74160968' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe9e19bd-8295-4f0c-8381-22bd74160968' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97af32cc-7297-48be-bedc-0285515ddc38' class='xr-var-data-in' type='checkbox'><label for='data-97af32cc-7297-48be-bedc-0285515ddc38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>BASE STATE LAPSE RATE (DT/D(LN(P)) IN STRATOSPHERE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P_STRAT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-99e82ddf-db2d-4ad8-baba-4ab19781377c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99e82ddf-db2d-4ad8-baba-4ab19781377c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bb801a0-36b9-4972-8523-4e98e311f8e2' class='xr-var-data-in' type='checkbox'><label for='data-4bb801a0-36b9-4972-8523-4e98e311f8e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>BASE STATE PRESSURE AT BOTTOM OF STRATOSPHERE</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAX_MSTFX</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-5ac26474-2dae-407f-be34-4a08b67580bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ac26474-2dae-407f-be34-4a08b67580bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c06d622-d46a-4f71-aef6-7f1da261992b' class='xr-var-data-in' type='checkbox'><label for='data-8c06d622-d46a-4f71-aef6-7f1da261992b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Max map factor in domain</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAX_MSTFY</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-12458a2a-a666-4bd0-86ad-8301092c98a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12458a2a-a666-4bd0-86ad-8301092c98a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f592910-8b75-42ee-a8fd-fa76b55b4cc4' class='xr-var-data-in' type='checkbox'><label for='data-9f592910-8b75-42ee-a8fd-fa76b55b4cc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Max map factor in domain</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAINC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-04a94b3f-bffa-4e90-9f00-c5ec3d3d4e63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04a94b3f-bffa-4e90-9f00-c5ec3d3d4e63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-308b004b-bdd7-4e13-9308-ea4f5b752029' class='xr-var-data-in' type='checkbox'><label for='data-308b004b-bdd7-4e13-9308-ea4f5b752029' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL CUMULUS PRECIPITATION</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAINSH</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-abbe3d3d-a85d-41f0-9851-68c623a01110' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abbe3d3d-a85d-41f0-9851-68c623a01110' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a28455ef-a494-454b-950d-d5e97fee01b1' class='xr-var-data-in' type='checkbox'><label for='data-a28455ef-a494-454b-950d-d5e97fee01b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED SHALLOW CUMULUS PRECIPITATION</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAINNC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-dac97ecc-477c-4e5a-83e5-02ebcf0330c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dac97ecc-477c-4e5a-83e5-02ebcf0330c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9e337d8-032a-427f-a477-d39fa9be5e93' class='xr-var-data-in' type='checkbox'><label for='data-b9e337d8-032a-427f-a477-d39fa9be5e93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE PRECIPITATION</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>integral_of_lwe_precipitation_rate_wrt_time</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
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+       "                        <td> 61.04 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOWNC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-cf021a0f-49ad-43c4-9496-1db55c8230c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf021a0f-49ad-43c4-9496-1db55c8230c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65d0d573-036c-4496-b395-ee112d7d3ddb' class='xr-var-data-in' type='checkbox'><label for='data-65d0d573-036c-4496-b395-ee112d7d3ddb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE SNOW AND ICE</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GRAUPELNC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b0e5a032-bd6a-4868-a72f-001f2c4b1b84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0e5a032-bd6a-4868-a72f-001f2c4b1b84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a751c10-1376-4deb-96af-83d87edfd958' class='xr-var-data-in' type='checkbox'><label for='data-6a751c10-1376-4deb-96af-83d87edfd958' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE GRAUPEL</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HAILNC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-c505b92d-0708-4e99-959b-6ac4fca67148' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c505b92d-0708-4e99-959b-6ac4fca67148' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22a91e75-1764-4c60-8efc-d30dcc8d928c' class='xr-var-data-in' type='checkbox'><label for='data-22a91e75-1764-4c60-8efc-d30dcc8d928c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE HAIL</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CLDFRA</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b8731e95-3e36-4c94-aa63-c9f986765bc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8731e95-3e36-4c94-aa63-c9f986765bc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-549e9aa2-da24-43b7-a1c2-d35acf8bb079' class='xr-var-data-in' type='checkbox'><label for='data-549e9aa2-da24-43b7-a1c2-d35acf8bb079' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>CLOUD FRACTION</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction_in_atmosphere_layer</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDOWN</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-40ddf804-ae92-4dbb-aa6f-2448d49072c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40ddf804-ae92-4dbb-aa6f-2448d49072c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67f38377-7b59-443b-a0a3-6bfa4f2bb07e' class='xr-var-data-in' type='checkbox'><label for='data-67f38377-7b59-443b-a0a3-6bfa4f2bb07e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>DOWNWARD SHORT WAVE FLUX AT GROUND SURFACE</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GLW</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-046cc2b5-5c7f-44ad-9288-bc50f4849345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-046cc2b5-5c7f-44ad-9288-bc50f4849345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7657561-e433-4030-8174-ed9c69af6bee' class='xr-var-data-in' type='checkbox'><label for='data-e7657561-e433-4030-8174-ed9c69af6bee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>DOWNWARD LONG WAVE FLUX AT GROUND SURFACE</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWNORM</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-8dede88a-62ea-49d6-9c00-fc8aefbe4ec8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8dede88a-62ea-49d6-9c00-fc8aefbe4ec8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87f82ce0-59d5-4cd7-a877-2a5c9243c412' class='xr-var-data-in' type='checkbox'><label for='data-87f82ce0-59d5-4cd7-a877-2a5c9243c412' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>NORMAL SHORT WAVE FLUX AT GROUND SURFACE (SLOPE-DEPENDENT)</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-69025cc0-ed80-4bee-91b6-021a9d3d81f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69025cc0-ed80-4bee-91b6-021a9d3d81f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a643e448-5f7f-4ff7-87ed-4d6f64d045e2' class='xr-var-data-in' type='checkbox'><label for='data-a643e448-5f7f-4ff7-87ed-4d6f64d045e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-04a9b7d3-0bb2-465e-80f6-c81798ac0059' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04a9b7d3-0bb2-465e-80f6-c81798ac0059' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ab1c4f8-136a-4416-9008-7a5e77b0b63b' class='xr-var-data-in' type='checkbox'><label for='data-5ab1c4f8-136a-4416-9008-7a5e77b0b63b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4777c28b-168b-4d1b-8740-e866feeaf709' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4777c28b-168b-4d1b-8740-e866feeaf709' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-267e8891-74d0-423b-b5fe-6c18e0b3c065' class='xr-var-data-in' type='checkbox'><label for='data-267e8891-74d0-423b-b5fe-6c18e0b3c065' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-8dbe9de5-b0fb-4da2-b509-bd4fcc6eeed5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8dbe9de5-b0fb-4da2-b509-bd4fcc6eeed5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27e1ec18-68b6-4e1b-87c6-78ed4e49f275' class='xr-var-data-in' type='checkbox'><label for='data-27e1ec18-68b6-4e1b-87c6-78ed4e49f275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-7552f162-072f-4fd1-8d03-dea3e413464a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7552f162-072f-4fd1-8d03-dea3e413464a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68bdf975-3055-4a5a-80e3-8446373d503f' class='xr-var-data-in' type='checkbox'><label for='data-68bdf975-3055-4a5a-80e3-8446373d503f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-7c416a23-6f6d-41d3-84c0-c6cf0108e2f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c416a23-6f6d-41d3-84c0-c6cf0108e2f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5e279f2-6a61-4099-a322-e725f1444c91' class='xr-var-data-in' type='checkbox'><label for='data-c5e279f2-6a61-4099-a322-e725f1444c91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-af7de5f4-a277-4af3-b408-c7f018f24358' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af7de5f4-a277-4af3-b408-c7f018f24358' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b1db5c4-0c38-4ea1-93b0-05baff8c78f6' class='xr-var-data-in' type='checkbox'><label for='data-4b1db5c4-0c38-4ea1-93b0-05baff8c78f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <th> Chunk </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
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+       "                        <td> 61.04 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-84026c17-ca88-46fc-b491-89ecd1c8978f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84026c17-ca88-46fc-b491-89ecd1c8978f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-276a6348-8426-475b-9f29-d5504f24a3dd' class='xr-var-data-in' type='checkbox'><label for='data-276a6348-8426-475b-9f29-d5504f24a3dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4da426b4-8257-427b-a383-f94a41827b3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4da426b4-8257-427b-a383-f94a41827b3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde0e150-1507-4f0e-a7a3-1945ea0be172' class='xr-var-data-in' type='checkbox'><label for='data-dde0e150-1507-4f0e-a7a3-1945ea0be172' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-081737d9-09c5-4edf-ae2d-25103be71a31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-081737d9-09c5-4edf-ae2d-25103be71a31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37e81f8c-461b-4db3-956f-fbe94604c16a' class='xr-var-data-in' type='checkbox'><label for='data-37e81f8c-461b-4db3-956f-fbe94604c16a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a663a676-9fdc-4013-a7bc-596d260f095e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a663a676-9fdc-4013-a7bc-596d260f095e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93121db3-5e8b-4276-a879-7df1f68401f5' class='xr-var-data-in' type='checkbox'><label for='data-93121db3-5e8b-4276-a879-7df1f68401f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-32be169b-6c08-48dd-847a-34bdab90b2d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32be169b-6c08-48dd-847a-34bdab90b2d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1aa326e-e382-49f2-99b7-7dc7499d59e3' class='xr-var-data-in' type='checkbox'><label for='data-f1aa326e-e382-49f2-99b7-7dc7499d59e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e1896b1c-b1e0-4bad-9649-30557477143f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1896b1c-b1e0-4bad-9649-30557477143f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-123c2e3e-1b8c-4f87-b631-e099c7fa2edc' class='xr-var-data-in' type='checkbox'><label for='data-123c2e3e-1b8c-4f87-b631-e099c7fa2edc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-44389a1d-5732-4b7b-9a20-12ddf0591e3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44389a1d-5732-4b7b-9a20-12ddf0591e3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-806a6aea-f2a8-4fac-8a55-6d63b9673ed2' class='xr-var-data-in' type='checkbox'><label for='data-806a6aea-f2a8-4fac-8a55-6d63b9673ed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-0fd76943-e81e-4680-808a-de9a78a45d2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fd76943-e81e-4680-808a-de9a78a45d2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3587fc8-4a09-4030-97eb-75ad6499e39d' class='xr-var-data-in' type='checkbox'><label for='data-a3587fc8-4a09-4030-97eb-75ad6499e39d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4b6c4daa-e3f5-4248-a7ae-5bf809539870' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b6c4daa-e3f5-4248-a7ae-5bf809539870' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16587b03-4391-4734-b9b2-ec09dcea009a' class='xr-var-data-in' type='checkbox'><label for='data-16587b03-4391-4734-b9b2-ec09dcea009a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-763d75fc-131e-464f-b8cb-9deddfebb4dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-763d75fc-131e-464f-b8cb-9deddfebb4dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdf71d31-9f07-4ff4-a66a-ec1b8fc07d4e' class='xr-var-data-in' type='checkbox'><label for='data-bdf71d31-9f07-4ff4-a66a-ec1b8fc07d4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-627e201b-89d5-4b0d-9220-b9e72821c69a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-627e201b-89d5-4b0d-9220-b9e72821c69a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff9c2d2f-efe1-4f11-aad6-692a5169dfca' class='xr-var-data-in' type='checkbox'><label for='data-ff9c2d2f-efe1-4f11-aad6-692a5169dfca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-d7da394d-3fbd-4340-80c1-624e50ea0394' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7da394d-3fbd-4340-80c1-624e50ea0394' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b36c6d70-f4c8-4201-a0c3-3fc7a1dd34f5' class='xr-var-data-in' type='checkbox'><label for='data-b36c6d70-f4c8-4201-a0c3-3fc7a1dd34f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-13074ccf-5441-4fdc-913f-33104db3d627' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13074ccf-5441-4fdc-913f-33104db3d627' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-772d377b-e874-430d-88d8-074a75124237' class='xr-var-data-in' type='checkbox'><label for='data-772d377b-e874-430d-88d8-074a75124237' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-43da0a0f-71bf-48ae-8605-05a32ffbf218' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43da0a0f-71bf-48ae-8605-05a32ffbf218' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23e70e7e-e662-45e2-bd3c-4cf0fc4db57d' class='xr-var-data-in' type='checkbox'><label for='data-23e70e7e-e662-45e2-bd3c-4cf0fc4db57d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-44e95fd7-ba89-4cd9-b0cb-996b887cb0eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44e95fd7-ba89-4cd9-b0cb-996b887cb0eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde885a3-e4cf-4647-97c9-63d87915e4f7' class='xr-var-data-in' type='checkbox'><label for='data-dde885a3-e4cf-4647-97c9-63d87915e4f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-2e43a739-6376-477a-86c6-b6b80a9d53a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e43a739-6376-477a-86c6-b6b80a9d53a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bbcf8a1-2af5-474e-830d-5fb76d08f25d' class='xr-var-data-in' type='checkbox'><label for='data-3bbcf8a1-2af5-474e-830d-5fb76d08f25d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-18c2cac9-1029-404b-9ae9-6ae7d02e8d69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18c2cac9-1029-404b-9ae9-6ae7d02e8d69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9fa0b5e-7ac5-4a85-9e06-86e7f11ca084' class='xr-var-data-in' type='checkbox'><label for='data-d9fa0b5e-7ac5-4a85-9e06-86e7f11ca084' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-084bef20-f3de-45e9-956c-e59ef0ada8f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-084bef20-f3de-45e9-956c-e59ef0ada8f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73f9434b-08cb-45ad-b3fa-055b129056af' class='xr-var-data-in' type='checkbox'><label for='data-73f9434b-08cb-45ad-b3fa-055b129056af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-7c68632c-e3a4-4bc5-b4e0-54ab50bbb625' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c68632c-e3a4-4bc5-b4e0-54ab50bbb625' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35f11c47-2442-48ec-a94a-41aa55f5c942' class='xr-var-data-in' type='checkbox'><label for='data-35f11c47-2442-48ec-a94a-41aa55f5c942' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-d72ad916-3b04-44b8-9312-092379fb9583' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d72ad916-3b04-44b8-9312-092379fb9583' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c80c8a53-df50-4bf7-a4d4-9d07a3112495' class='xr-var-data-in' type='checkbox'><label for='data-c80c8a53-df50-4bf7-a4d4-9d07a3112495' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-abab0edc-d97a-41b6-aba3-78c443e7be88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abab0edc-d97a-41b6-aba3-78c443e7be88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ff23bc6-907c-4d8d-8ded-579cf9c40738' class='xr-var-data-in' type='checkbox'><label for='data-5ff23bc6-907c-4d8d-8ded-579cf9c40738' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-6f6a6bf6-a1a0-4432-98eb-06035a7c1230' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f6a6bf6-a1a0-4432-98eb-06035a7c1230' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e430d878-25e3-4055-b855-ac646af5f687' class='xr-var-data-in' type='checkbox'><label for='data-e430d878-25e3-4055-b855-ac646af5f687' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-beb48313-2f55-4723-b1b1-465b6692d0ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-beb48313-2f55-4723-b1b1-465b6692d0ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-304adbe6-9d1e-4153-8695-748d2e038222' class='xr-var-data-in' type='checkbox'><label for='data-304adbe6-9d1e-4153-8695-748d2e038222' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-392cfeac-84d4-47c4-bd01-cea9908e3bc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-392cfeac-84d4-47c4-bd01-cea9908e3bc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2724072c-7e0c-4bce-b13c-444098f4444c' class='xr-var-data-in' type='checkbox'><label for='data-2724072c-7e0c-4bce-b13c-444098f4444c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-ac651d16-0653-48a0-8bad-80045cae3ffe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac651d16-0653-48a0-8bad-80045cae3ffe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60c1984d-efb5-449e-841d-6380ad53c298' class='xr-var-data-in' type='checkbox'><label for='data-60c1984d-efb5-449e-841d-6380ad53c298' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a57ec796-40e8-4e62-b961-00d28296160c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a57ec796-40e8-4e62-b961-00d28296160c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adf68f7b-c8c5-4d53-865b-035937849e95' class='xr-var-data-in' type='checkbox'><label for='data-adf68f7b-c8c5-4d53-865b-035937849e95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-3fe4f30e-de2f-41b0-9483-c859fe8b59fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fe4f30e-de2f-41b0-9483-c859fe8b59fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74d992c2-122c-4aed-9dd5-47fbe2ee5bc3' class='xr-var-data-in' type='checkbox'><label for='data-74d992c2-122c-4aed-9dd5-47fbe2ee5bc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-cb91912d-b545-4d7e-bd09-a4e6c297e409' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb91912d-b545-4d7e-bd09-a4e6c297e409' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e14c8ef-5789-4177-8ad4-0106c5388cc5' class='xr-var-data-in' type='checkbox'><label for='data-3e14c8ef-5789-4177-8ad4-0106c5388cc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-67f23f2e-330a-4e24-ba7d-17b8b8a7a34c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67f23f2e-330a-4e24-ba7d-17b8b8a7a34c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8535485-4ae1-48a3-88cf-a658eb8a46c9' class='xr-var-data-in' type='checkbox'><label for='data-c8535485-4ae1-48a3-88cf-a658eb8a46c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-bfc93e19-89ee-454f-8d86-98de528bc44e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfc93e19-89ee-454f-8d86-98de528bc44e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a28483b-7f02-4a6c-8644-0b137e7c8b25' class='xr-var-data-in' type='checkbox'><label for='data-3a28483b-7f02-4a6c-8644-0b137e7c8b25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-d649ebcf-ffd0-4b2f-b440-afea7767d23a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d649ebcf-ffd0-4b2f-b440-afea7767d23a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dba8916-84a2-4130-9c9d-500f7da57033' class='xr-var-data-in' type='checkbox'><label for='data-8dba8916-84a2-4130-9c9d-500f7da57033' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-92446726-8228-412e-b31f-bafa02de47e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92446726-8228-412e-b31f-bafa02de47e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-700d8d1b-0466-4d1f-9446-24683a9c185c' class='xr-var-data-in' type='checkbox'><label for='data-700d8d1b-0466-4d1f-9446-24683a9c185c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a6369b58-b31d-4824-bdad-66f62d419854' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6369b58-b31d-4824-bdad-66f62d419854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d250dde3-bae7-4119-94b6-a2446ad8debc' class='xr-var-data-in' type='checkbox'><label for='data-d250dde3-bae7-4119-94b6-a2446ad8debc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-8b05e129-f1c7-4298-87cd-f93beb008b15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b05e129-f1c7-4298-87cd-f93beb008b15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22d0fe76-1a5f-45b8-a049-95bb49348304' class='xr-var-data-in' type='checkbox'><label for='data-22d0fe76-1a5f-45b8-a049-95bb49348304' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-add98d0e-d2d2-4221-a8d7-82dd9fcdcc60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-add98d0e-d2d2-4221-a8d7-82dd9fcdcc60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3695e213-5657-455b-a7d9-24fb7505eac4' class='xr-var-data-in' type='checkbox'><label for='data-3695e213-5657-455b-a7d9-24fb7505eac4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-dbe35be0-97e0-49e5-b5bd-9d485ff636e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbe35be0-97e0-49e5-b5bd-9d485ff636e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae22e7e1-7402-4ad7-b8d0-26d22bcf9fbc' class='xr-var-data-in' type='checkbox'><label for='data-ae22e7e1-7402-4ad7-b8d0-26d22bcf9fbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a31c24bf-0641-43e9-96f4-be6d94f5de29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a31c24bf-0641-43e9-96f4-be6d94f5de29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ff8c891-b0a2-4833-97da-51a5d51b41a4' class='xr-var-data-in' type='checkbox'><label for='data-7ff8c891-b0a2-4833-97da-51a5d51b41a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b5527088-3157-4013-9a66-8b52e8a97f5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5527088-3157-4013-9a66-8b52e8a97f5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f6a2e09-24c4-4ef0-9a91-3f7c6658f9b0' class='xr-var-data-in' type='checkbox'><label for='data-5f6a2e09-24c4-4ef0-9a91-3f7c6658f9b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1a70ba9d-3e47-4ccc-9c5e-b1e36df1e5e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a70ba9d-3e47-4ccc-9c5e-b1e36df1e5e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7a49908-8c22-43dc-a450-05bc92857568' class='xr-var-data-in' type='checkbox'><label for='data-f7a49908-8c22-43dc-a450-05bc92857568' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-13e9c6d1-d272-45b3-bd87-242299664f2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13e9c6d1-d272-45b3-bd87-242299664f2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78900f47-8bd9-45b9-9d97-ba6553b1cae3' class='xr-var-data-in' type='checkbox'><label for='data-78900f47-8bd9-45b9-9d97-ba6553b1cae3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-d8c4c7a7-97dc-4886-8065-a6ab510301ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8c4c7a7-97dc-4886-8065-a6ab510301ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c347717b-27ea-43c7-8859-dcd45d052b2b' class='xr-var-data-in' type='checkbox'><label for='data-c347717b-27ea-43c7-8859-dcd45d052b2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OLR</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f55c9933-3710-45d6-b37c-53bd9a1817f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f55c9933-3710-45d6-b37c-53bd9a1817f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20ed75dc-022e-4518-bb47-23270d6e7f8c' class='xr-var-data-in' type='checkbox'><label for='data-20ed75dc-022e-4518-bb47-23270d6e7f8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>TOA OUTGOING LONG WAVE</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALBEDO</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-0f658b3b-d339-4fae-bdd5-8d5a8f97b00c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f658b3b-d339-4fae-bdd5-8d5a8f97b00c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43a32bba-9e6f-4dbd-bfad-2388172619b9' class='xr-var-data-in' type='checkbox'><label for='data-43a32bba-9e6f-4dbd-bfad-2388172619b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ALBEDO</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALBBCK</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-153f2560-1a7f-4c29-9a89-161c059ed7d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-153f2560-1a7f-4c29-9a89-161c059ed7d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e944878e-3e23-4893-bd66-48ef39e25e4e' class='xr-var-data-in' type='checkbox'><label for='data-e944878e-3e23-4893-bd66-48ef39e25e4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BACKGROUND ALBEDO</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EMISS</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-cc169fa2-4473-4fe9-a721-89a964e5ba19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc169fa2-4473-4fe9-a721-89a964e5ba19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3f848eb-2e5f-47b3-b1a2-f6289e87f9fe' class='xr-var-data-in' type='checkbox'><label for='data-a3f848eb-2e5f-47b3-b1a2-f6289e87f9fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SURFACE EMISSIVITY</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NOAHRES</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-46eb6d4b-cbbd-4351-9217-3677384d2c3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46eb6d4b-cbbd-4351-9217-3677384d2c3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b19e9c9e-f9f5-4cd8-954e-0400f130a89f' class='xr-var-data-in' type='checkbox'><label for='data-b19e9c9e-f9f5-4cd8-954e-0400f130a89f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>RESIDUAL OF THE NOAH SURFACE ENERGY BUDGET</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TMN</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1e71e98f-9f4e-4fcd-bd4b-dd9d2655b980' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e71e98f-9f4e-4fcd-bd4b-dd9d2655b980' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5077bf82-4992-4a18-867c-41b2996256d3' class='xr-var-data-in' type='checkbox'><label for='data-5077bf82-4992-4a18-867c-41b2996256d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SOIL TEMPERATURE AT LOWER BOUNDARY</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLAND</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-9fc0805b-8bb0-436d-8b9c-9255b18e89ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fc0805b-8bb0-436d-8b9c-9255b18e89ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6e70cdd-4b78-44b6-b46a-60b7bbd07993' class='xr-var-data-in' type='checkbox'><label for='data-e6e70cdd-4b78-44b6-b46a-60b7bbd07993' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LAND MASK (1 FOR LAND, 2 FOR WATER)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UST</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-126a9a71-b83b-44bc-a827-9d9dcd78853e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-126a9a71-b83b-44bc-a827-9d9dcd78853e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4964a80d-1b88-4bef-8867-93cd2d07f85d' class='xr-var-data-in' type='checkbox'><label for='data-4964a80d-1b88-4bef-8867-93cd2d07f85d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>U* IN SIMILARITY THEORY</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PBLH</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-6df92590-5f1e-43be-ac27-4414af8eb81e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6df92590-5f1e-43be-ac27-4414af8eb81e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9eb1430-5bd3-4049-a83f-600f76593384' class='xr-var-data-in' type='checkbox'><label for='data-e9eb1430-5bd3-4049-a83f-600f76593384' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>PBL HEIGHT</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>atmosphere_boundary_layer_thickness</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
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+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HFX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-47ad013c-a0f2-4fca-bb39-5d0bda138787' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47ad013c-a0f2-4fca-bb39-5d0bda138787' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0539335-7fcf-4b4b-a714-849ea8519a97' class='xr-var-data-in' type='checkbox'><label for='data-d0539335-7fcf-4b4b-a714-849ea8519a97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>UPWARD HEAT FLUX AT THE SURFACE</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>surface_upward_heat_flux_in_air</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QFX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-098e8a52-28c8-4e83-814a-210c9ef59353' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-098e8a52-28c8-4e83-814a-210c9ef59353' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d8360af-9ebe-41f3-88bf-a7d36c60b64a' class='xr-var-data-in' type='checkbox'><label for='data-1d8360af-9ebe-41f3-88bf-a7d36c60b64a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>UPWARD MOISTURE FLUX AT THE SURFACE</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LH</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-59023316-4efd-4717-8ccc-8d3c56bb9b55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59023316-4efd-4717-8ccc-8d3c56bb9b55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96383a38-5c92-4a7e-acac-86c373ce8c4c' class='xr-var-data-in' type='checkbox'><label for='data-96383a38-5c92-4a7e-acac-86c373ce8c4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LATENT HEAT FLUX AT THE SURFACE</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>surface_upward_latent_heat_flux_in_air</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACHFX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-beb4271e-febf-43e1-8ee5-61e777950770' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-beb4271e-febf-43e1-8ee5-61e777950770' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fa38d5a-e598-45db-87ab-cda5830554ef' class='xr-var-data-in' type='checkbox'><label for='data-0fa38d5a-e598-45db-87ab-cda5830554ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWARD HEAT FLUX AT THE SURFACE</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>integral_of_surface_upward_heat_flux_in_air_wrt_time</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLHF</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-0770ee72-0874-4aa5-9434-09523723ed4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0770ee72-0874-4aa5-9434-09523723ed4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6db29cb-dcc3-473a-b894-10deb1deecb9' class='xr-var-data-in' type='checkbox'><label for='data-e6db29cb-dcc3-473a-b894-10deb1deecb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWARD LATENT HEAT FLUX AT THE SURFACE</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>integral_of_surface_upward_latent_heat_flux_in_air_wrf_time</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOWC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4aae8bdd-9381-40cb-9d9d-72603d5dfc8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4aae8bdd-9381-40cb-9d9d-72603d5dfc8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73ab9699-8211-4fec-9336-5f1ae9669428' class='xr-var-data-in' type='checkbox'><label for='data-73ab9699-8211-4fec-9336-5f1ae9669428' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>FLAG INDICATING SNOW COVERAGE (1 FOR SNOW COVER)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
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+       "                    <tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SR</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-33d062b6-8515-412c-baab-0d981132b546' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33d062b6-8515-412c-baab-0d981132b546' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd6fe7c7-c67a-4af0-bb93-64c5725f66a3' class='xr-var-data-in' type='checkbox'><label for='data-fd6fe7c7-c67a-4af0-bb93-64c5725f66a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>fraction of frozen precipitation</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SAVE_TOPO_FROM_REAL</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-0803ebb3-0112-4602-bc28-27167e93a786' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0803ebb3-0112-4602-bc28-27167e93a786' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4042dda-3b6a-4bee-81c4-fc605c5d2217' class='xr-var-data-in' type='checkbox'><label for='data-d4042dda-3b6a-4bee-81c4-fc605c5d2217' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>1=original topo from real/0=topo modified by WRF</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ISEEDARR_RAND_PERTURB</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-69f33c15-8d86-41cb-b666-5892e732f10f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69f33c15-8d86-41cb-b666-5892e732f10f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26bf1744-fc43-44be-8f86-c860da5e4685' class='xr-var-data-in' type='checkbox'><label for='data-26bf1744-fc43-44be-8f86-c860da5e4685' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Array to hold seed for restart, RAND_PERT</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ISEEDARR_SPPT</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-9cbdcc99-99c1-4cfb-9448-6c5b2e702cfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9cbdcc99-99c1-4cfb-9448-6c5b2e702cfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f0cd0ea-74fe-487f-90e8-f7a5672c4241' class='xr-var-data-in' type='checkbox'><label for='data-9f0cd0ea-74fe-487f-90e8-f7a5672c4241' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Array to hold seed for restart, SPPT</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
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+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
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+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ISEEDARR_SKEBS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-4c2b34a4-72a4-4473-be71-dbea35764cea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c2b34a4-72a4-4473-be71-dbea35764cea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70487b8f-3be6-49d2-80b6-070506453e80' class='xr-var-data-in' type='checkbox'><label for='data-70487b8f-3be6-49d2-80b6-070506453e80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Array to hold seed for restart, SKEBS</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
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+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
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+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m11</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f547c030-ad77-42c0-8c72-a22ff4f1cd6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f547c030-ad77-42c0-8c72-a22ff4f1cd6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5a0071b-d19a-4914-8eba-7e880c725fb4' class='xr-var-data-in' type='checkbox'><label for='data-e5a0071b-d19a-4914-8eba-7e880c725fb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>11 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"461\" height=\"233\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m22</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b01145db-a81e-48f6-93d2-3f302f9edeb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b01145db-a81e-48f6-93d2-3f302f9edeb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53984c91-c02a-48ad-ab8e-489b71e369e8' class='xr-var-data-in' type='checkbox'><label for='data-53984c91-c02a-48ad-ab8e-489b71e369e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>22 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m33</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a1a24ddb-5121-4072-aeae-c77da76d082d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1a24ddb-5121-4072-aeae-c77da76d082d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11ab76f3-d417-4b78-8326-833b4302c42c' class='xr-var-data-in' type='checkbox'><label for='data-11ab76f3-d417-4b78-8326-833b4302c42c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>33 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m12</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-152bda02-69c8-4b2a-bf19-8e929d8aaec4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-152bda02-69c8-4b2a-bf19-8e929d8aaec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0c6d116-dcc1-426c-9b86-18f749b523b0' class='xr-var-data-in' type='checkbox'><label for='data-d0c6d116-dcc1-426c-9b86-18f749b523b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>12 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m13</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f3163afc-af9c-4171-b630-9d5cc69808ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3163afc-af9c-4171-b630-9d5cc69808ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-336c6f99-6b20-4c2b-9207-b1be05bdb35e' class='xr-var-data-in' type='checkbox'><label for='data-336c6f99-6b20-4c2b-9207-b1be05bdb35e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>13 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m23</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-6935e8be-eb6d-48b4-95ab-8c1d4fd5b770' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6935e8be-eb6d-48b4-95ab-8c1d4fd5b770' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab052d7c-abd1-48a7-8eeb-d536887d3aa3' class='xr-var-data-in' type='checkbox'><label for='data-ab052d7c-abd1-48a7-8eeb-d536887d3aa3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>23 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U_LS</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-f333295c-7b25-44d9-aaf1-c2871592daaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f333295c-7b25-44d9-aaf1-c2871592daaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96739f8d-51b4-4f9c-96ed-b0a81898030d' class='xr-var-data-in' type='checkbox'><label for='data-96739f8d-51b4-4f9c-96ed-b0a81898030d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>large-scale zonal wind velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                </thead>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V_LS</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-ddc07a6b-61f5-4969-91fa-abf376072d0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddc07a6b-61f5-4969-91fa-abf376072d0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ddd5383-25fe-4046-ba55-5e8ed666d896' class='xr-var-data-in' type='checkbox'><label for='data-6ddd5383-25fe-4046-ba55-5e8ed666d896' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>large-scale meridional wind velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
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+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
+       "  <text x=\"140.000000\" y=\"19.816052\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,19.816052)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V_LS_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-95ff8dc8-8eb0-4a10-852c-c58866edcf95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95ff8dc8-8eb0-4a10-852c-c58866edcf95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43bcfdc4-fb9c-4dd7-a505-3130b144607e' class='xr-var-data-in' type='checkbox'><label for='data-43bcfdc4-fb9c-4dd7-a505-3130b144607e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency large-scale meridional wind velocity</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
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+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
+       "  <text x=\"140.000000\" y=\"19.816052\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,19.816052)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_LS</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-88306110-3fe2-4a80-adc5-a7ebd996f34f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88306110-3fe2-4a80-adc5-a7ebd996f34f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fe35761-555d-4d86-9109-d93a9904625c' class='xr-var-data-in' type='checkbox'><label for='data-9fe35761-555d-4d86-9109-d93a9904625c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>large-scale vertical velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
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+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_LS_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-a9df7185-1fbc-4949-b8c0-93400524911b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9df7185-1fbc-4949-b8c0-93400524911b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-702778d5-933c-464a-b8d9-1950acb20ca9' class='xr-var-data-in' type='checkbox'><label for='data-702778d5-933c-464a-b8d9-1950acb20ca9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency large-scale vertical velocity</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>INV_TAU_S</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-71ec9623-bded-4ce9-a738-398a9be59eaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71ec9623-bded-4ce9-a738-398a9be59eaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe5eae80-8eb2-4374-a716-abc5a005cbe1' class='xr-var-data-in' type='checkbox'><label for='data-fe5eae80-8eb2-4374-a716-abc5a005cbe1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>inverse relaxation time for scalars</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
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+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>INV_TAU_M</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-d835e400-a788-4349-9a7b-5584e39d6d5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d835e400-a788-4349-9a7b-5584e39d6d5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5e37b9d-b17a-4b0b-9297-5346afaf7c16' class='xr-var-data-in' type='checkbox'><label for='data-b5e37b9d-b17a-4b0b-9297-5346afaf7c16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>inverse relaxation time for scalars</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
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+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
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+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
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+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
+       "  <text x=\"140.000000\" y=\"19.816052\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,19.816052)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TH_ADV</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-0605d000-5829-4669-86c3-d371a11f1cdb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0605d000-5829-4669-86c3-d371a11f1cdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2356874-cb40-4928-900e-4a93689e4b6f' class='xr-var-data-in' type='checkbox'><label for='data-e2356874-cb40-4928-900e-4a93689e4b6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency theta advection</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
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+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
+       "  <text x=\"140.000000\" y=\"19.816052\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,19.816052)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TH_ADV_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-c1232bf2-e88e-4678-b984-91bdffff5a65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1232bf2-e88e-4678-b984-91bdffff5a65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b07ee18a-b4b1-47e6-b4b3-489425718fcf' class='xr-var-data-in' type='checkbox'><label for='data-b07ee18a-b4b1-47e6-b4b3-489425718fcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency of tendency theta advection</dd><dt><span>units :</span></dt><dd>K s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TH_RLX</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-10931ce1-4b69-4b7b-b1dd-0f680667d078' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10931ce1-4b69-4b7b-b1dd-0f680667d078' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-267f5011-2e69-4f8e-8c8c-c3be4f236069' class='xr-var-data-in' type='checkbox'><label for='data-267f5011-2e69-4f8e-8c8c-c3be4f236069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>theta relaxation</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TH_RLX_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-b7148495-9da3-407e-aade-784c67f54723' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7148495-9da3-407e-aade-784c67f54723' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a470acd-dbef-4f3c-beba-77ba0d2d0800' class='xr-var-data-in' type='checkbox'><label for='data-4a470acd-dbef-4f3c-beba-77ba0d2d0800' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency theta relaxation</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
+       "  <text x=\"140.000000\" y=\"19.816052\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,19.816052)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV_ADV</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-3fb24aa9-f014-4095-98a9-58fcc233b696' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fb24aa9-f014-4095-98a9-58fcc233b696' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1e010eb-9bc4-4c7f-aa14-caeffb635284' class='xr-var-data-in' type='checkbox'><label for='data-f1e010eb-9bc4-4c7f-aa14-caeffb635284' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency qv advection</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
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+       "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
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+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
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+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
+       "  <text x=\"140.000000\" y=\"19.816052\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,19.816052)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV_ADV_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-5c630bbc-4135-44b2-9bbf-1a6e179053f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c630bbc-4135-44b2-9bbf-1a6e179053f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-349f1d4c-83ff-4a25-880e-07709c06f4e2' class='xr-var-data-in' type='checkbox'><label for='data-349f1d4c-83ff-4a25-880e-07709c06f4e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency of tendency qv advection</dd><dt><span>units :</span></dt><dd>kg kg-1 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV_RLX</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-b0d8cbd5-2443-4f7f-9cb3-b1b72a7f4a47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0d8cbd5-2443-4f7f-9cb3-b1b72a7f4a47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3541115d-fcac-4b9f-8e9e-171c211142c0' class='xr-var-data-in' type='checkbox'><label for='data-3541115d-fcac-4b9f-8e9e-171c211142c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv relaxation</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
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+       "  <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
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+       "  <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV_RLX_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-0dfb3c0d-78c9-4c5e-80ef-0dd9d3b8e17e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dfb3c0d-78c9-4c5e-80ef-0dd9d3b8e17e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df3ca801-377d-4080-985b-63e1a40df028' class='xr-var-data-in' type='checkbox'><label for='data-df3ca801-377d-4080-985b-63e1a40df028' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency qv relaxation</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
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+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
+       "  <text x=\"140.000000\" y=\"19.816052\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,19.816052)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z_LS</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-34c9a6e0-4189-4a5a-ab09-514bfde2b4e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34c9a6e0-4189-4a5a-ab09-514bfde2b4e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f3cd457-a670-4cb0-a9c7-34e248805f5a' class='xr-var-data-in' type='checkbox'><label for='data-8f3cd457-a670-4cb0-a9c7-34e248805f5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>z of large-scale forcings</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
+       "  <text x=\"140.000000\" y=\"19.816052\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,19.816052)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z_LS_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-a00121a9-ee30-424a-9ae3-0d6c731b35db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a00121a9-ee30-424a-9ae3-0d6c731b35db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5522df39-3f17-4c17-a346-fedefa61ca14' class='xr-var-data-in' type='checkbox'><label for='data-5522df39-3f17-4c17-a346-fedefa61ca14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency of z of large-scale forcings</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_SH_FLX</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-c1be21f0-2490-4815-8f16-78cdaa72fe12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1be21f0-2490-4815-8f16-78cdaa72fe12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a8c40e6-5962-4e7f-8ece-da1217e0ebd8' class='xr-var-data-in' type='checkbox'><label for='data-2a8c40e6-5962-4e7f-8ece-da1217e0ebd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>prescribed sensible heat flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_SH_FLX_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-ea8fbea3-9aa2-49b9-bd0d-8c95ad745453' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea8fbea3-9aa2-49b9-bd0d-8c95ad745453' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83cbbf08-8188-4c7f-9365-6c6f94c44119' class='xr-var-data-in' type='checkbox'><label for='data-83cbbf08-8188-4c7f-9365-6c6f94c44119' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>tendency prescribed sensible heat flux</dd><dt><span>units :</span></dt><dd>W m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_LH_FLX</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-1aa65f47-12ef-4ea0-ad47-a9bce00aeea4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1aa65f47-12ef-4ea0-ad47-a9bce00aeea4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7267dbdf-d185-4681-a2d8-402c63adc798' class='xr-var-data-in' type='checkbox'><label for='data-7267dbdf-d185-4681-a2d8-402c63adc798' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>prescribed latent heat flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_LH_FLX_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-55153d95-a7af-4681-af4d-9212002716bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55153d95-a7af-4681-af4d-9212002716bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9f11efb-f085-49f0-ab3b-40907c630dbc' class='xr-var-data-in' type='checkbox'><label for='data-f9f11efb-f085-49f0-ab3b-40907c630dbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>tendency prescribed latent heat flux</dd><dt><span>units :</span></dt><dd>W m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_ALBEDO</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-f91e82e5-d392-4d28-8f5c-a69e8014f767' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f91e82e5-d392-4d28-8f5c-a69e8014f767' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c132439-65e5-4918-830e-c0574b0a4f2c' class='xr-var-data-in' type='checkbox'><label for='data-1c132439-65e5-4918-830e-c0574b0a4f2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>prescribed albedo</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_ALBEDO_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-790ecbae-a96d-4d10-8d45-b084a88853e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-790ecbae-a96d-4d10-8d45-b084a88853e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a0db59b-5d5c-452e-99db-7e70b4355cf3' class='xr-var-data-in' type='checkbox'><label for='data-2a0db59b-5d5c-452e-99db-7e70b4355cf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>tendency prescribed albedo</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
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+       "  <line x1=\"79\" y1=\"0\" x2=\"79\" y2=\"25\" />\n",
+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_TSK</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-a0da0760-9f6d-474e-9bdb-ce953ea25b58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0da0760-9f6d-474e-9bdb-ce953ea25b58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e051d50c-f694-4e55-a577-ac375a25b674' class='xr-var-data-in' type='checkbox'><label for='data-e051d50c-f694-4e55-a577-ac375a25b674' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>prescribed skin temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"79\" y1=\"0\" x2=\"79\" y2=\"25\" />\n",
+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_TSK_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-449ac85f-b028-49f5-a2b7-1a9646081e9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-449ac85f-b028-49f5-a2b7-1a9646081e9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54c760d8-5c68-4665-b4c7-241e768130df' class='xr-var-data-in' type='checkbox'><label for='data-54c760d8-5c68-4665-b4c7-241e768130df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>tendency prescribed skin temperature</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NUM_MODES_AER</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-ec1fc12c-23de-4563-b270-0cffafa0b0a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec1fc12c-23de-4563-b270-0cffafa0b0a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dd4944c-ad9e-4342-9aef-612ef9763115' class='xr-var-data-in' type='checkbox'><label for='data-3dd4944c-ad9e-4342-9aef-612ef9763115' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Number of aerosol distribution modes</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RULSTEN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-7051a7a5-a5ce-40c1-9c5f-2efbbdd41800' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7051a7a5-a5ce-40c1-9c5f-2efbbdd41800' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3ca0862-54be-4dc4-8ec1-c5e6de74becc' class='xr-var-data-in' type='checkbox'><label for='data-a3ca0862-54be-4dc4-8ec1-c5e6de74becc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>coupled u tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>Pa m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RVLSTEN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-c2769435-0aa9-45c9-9bda-ad0707f5aa40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2769435-0aa9-45c9-9bda-ad0707f5aa40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e6abafc-9b40-4c57-a041-6d396be5f5eb' class='xr-var-data-in' type='checkbox'><label for='data-4e6abafc-9b40-4c57-a041-6d396be5f5eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>coupled v tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>Pa m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RTHLSTEN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-51f2f8e9-743b-4668-b78e-00bfe3603755' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51f2f8e9-743b-4668-b78e-00bfe3603755' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb614d21-9ba7-4f9a-bbf2-2acdfe9898ef' class='xr-var-data-in' type='checkbox'><label for='data-bb614d21-9ba7-4f9a-bbf2-2acdfe9898ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>coupled theta tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>Pa K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RQVLSTEN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4e6b5675-76ba-4fe1-a790-fdd6059b5602' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e6b5675-76ba-4fe1-a790-fdd6059b5602' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c3da141-cb59-48ae-90d8-2e188a77e100' class='xr-var-data-in' type='checkbox'><label for='data-9c3da141-cb59-48ae-90d8-2e188a77e100' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>coupled Q_V tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>Pa kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_DTHDZ</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-fce0b5ce-b6a3-4dc2-8668-d428908bba07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fce0b5ce-b6a3-4dc2-8668-d428908bba07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34021a32-6ba7-4368-b702-845049715b28' class='xr-var-data-in' type='checkbox'><label for='data-34021a32-6ba7-4368-b702-845049715b28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>th tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_DQVDZ</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-0cc4ddd4-c85e-43ca-960d-9c6a9005c117' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cc4ddd4-c85e-43ca-960d-9c6a9005c117' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7287d4cc-aead-4ec7-9d28-691f55550b9c' class='xr-var-data-in' type='checkbox'><label for='data-7287d4cc-aead-4ec7-9d28-691f55550b9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>qv tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_DUDZ</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-81d795f8-6939-42dd-9770-aae88cd9ed90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81d795f8-6939-42dd-9770-aae88cd9ed90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0ab46e9-2742-4d6c-b3a4-4949f491fe65' class='xr-var-data-in' type='checkbox'><label for='data-b0ab46e9-2742-4d6c-b3a4-4949f491fe65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>u tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_DVDZ</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4fa9820f-b621-4847-afb7-e81159801e94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fa9820f-b621-4847-afb7-e81159801e94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90310690-4468-46b3-aacc-0a3be0a537bf' class='xr-var-data-in' type='checkbox'><label for='data-90310690-4468-46b3-aacc-0a3be0a537bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>v tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THDT_LSHOR</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-fdde66e0-33e2-4c7e-b126-e69ae8920fde' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdde66e0-33e2-4c7e-b126-e69ae8920fde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cc3ee5e-d3a9-4f24-9f7a-7fa584397f7a' class='xr-var-data-in' type='checkbox'><label for='data-6cc3ee5e-d3a9-4f24-9f7a-7fa584397f7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>th tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
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+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
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+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
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+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
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+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
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+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QVDT_LSHOR</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-060666c3-4719-4d86-9e5c-8ab40c20de69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-060666c3-4719-4d86-9e5c-8ab40c20de69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-852099f1-a242-496b-a0a0-a0e78196eb51' class='xr-var-data-in' type='checkbox'><label for='data-852099f1-a242-496b-a0a0-a0e78196eb51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
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+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
+       "  <text x=\"140.000000\" y=\"24.159292\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,24.159292)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-4cf1ff5a-d749-4e19-94fd-68a76537b433' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cf1ff5a-d749-4e19-94fd-68a76537b433' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e65fa282-0dfe-4e83-8021-6d72121b5377' class='xr-var-data-in' type='checkbox'><label for='data-e65fa282-0dfe-4e83-8021-6d72121b5377' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>th tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
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+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QVDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-c471abd9-878c-4f16-9f1e-51e5d7d5f9e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c471abd9-878c-4f16-9f1e-51e5d7d5f9e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4f9c567-6bf9-469f-accd-5c2151d89569' class='xr-var-data-in' type='checkbox'><label for='data-c4f9c567-6bf9-469f-accd-5c2151d89569' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-f8541080-775d-4c0e-a4fe-14f52ae29982' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8541080-775d-4c0e-a4fe-14f52ae29982' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73f61fdc-eb72-4530-a4c7-af237acdcb98' class='xr-var-data-in' type='checkbox'><label for='data-73f61fdc-eb72-4530-a4c7-af237acdcb98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-4f1ad363-1bf9-40c1-af2f-732f0bc40bef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f1ad363-1bf9-40c1-af2f-732f0bc40bef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b93b0fc7-8abd-4d2a-810a-1205eea86b56' class='xr-var-data-in' type='checkbox'><label for='data-b93b0fc7-8abd-4d2a-810a-1205eea86b56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>v tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EFFCS</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4494647a-e4a1-4035-8719-4dac8244ec7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4494647a-e4a1-4035-8719-4dac8244ec7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75a9e28d-f78b-4672-aef7-a46aa03bd867' class='xr-var-data-in' type='checkbox'><label for='data-75a9e28d-f78b-4672-aef7-a46aa03bd867' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>CLOUD DROPLET EFFECTIVE RADIUS</dd><dt><span>units :</span></dt><dd>micron</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWF0</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1c3bf721-4206-4425-8ca1-42c91c22d46e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c3bf721-4206-4425-8ca1-42c91c22d46e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-947a1292-8730-4fad-ab55-39b8ca8f1d72' class='xr-var-data-in' type='checkbox'><label for='data-947a1292-8730-4fad-ab55-39b8ca8f1d72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Net LW radiative flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWF1</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-bd2411d0-6687-471d-b49b-3ce1fcbce10f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd2411d0-6687-471d-b49b-3ce1fcbce10f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bb57bcd-135d-41ee-933b-b8d522aa6217' class='xr-var-data-in' type='checkbox'><label for='data-7bb57bcd-135d-41ee-933b-b8d522aa6217' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Net LW radiative flux, term1</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWF2</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-d4769020-dcf1-4fbc-8320-011ed84dbf89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4769020-dcf1-4fbc-8320-011ed84dbf89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29f014c0-0eda-4202-8573-710a84710cc0' class='xr-var-data-in' type='checkbox'><label for='data-29f014c0-0eda-4202-8573-710a84710cc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Net LW radiative flux, term2</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
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+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWF3</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-93692c08-7060-4879-b800-be231121afe2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93692c08-7060-4879-b800-be231121afe2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b02afc8f-7d6c-47b4-b0d8-3a740e695131' class='xr-var-data-in' type='checkbox'><label for='data-b02afc8f-7d6c-47b4-b0d8-3a740e695131' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Net LW radiative flux, term3</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SEDFQC</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-11b8ab09-01d6-4f3b-ae3e-1e51e19e4a96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11b8ab09-01d6-4f3b-ae3e-1e51e19e4a96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc4b9d3a-24fa-43bd-8440-3f6a74834459' class='xr-var-data-in' type='checkbox'><label for='data-dc4b9d3a-24fa-43bd-8440-3f6a74834459' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>sedimentation flux of cloud water</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SEDFQR</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1b88ec90-bbb1-466c-a846-4502ed8f21cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b88ec90-bbb1-466c-a846-4502ed8f21cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-104cefd2-4cdf-4f82-afdb-f2f170b00032' class='xr-var-data-in' type='checkbox'><label for='data-104cefd2-4cdf-4f82-afdb-f2f170b00032' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>sedimentation flux of rain water</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QVDT_PR</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-648f3aa6-3ded-4689-9715-35447badd9d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-648f3aa6-3ded-4689-9715-35447badd9d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14314ecd-dc21-474a-8745-f99cb7025ea1' class='xr-var-data-in' type='checkbox'><label for='data-14314ecd-dc21-474a-8745-f99cb7025ea1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Production rate of vapor by conversion to rain</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QVDT_COND</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-347edf9a-5791-4ce6-b2f0-0b4814a46e52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-347edf9a-5791-4ce6-b2f0-0b4814a46e52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdb9f944-4f25-49df-a573-65612594183c' class='xr-var-data-in' type='checkbox'><label for='data-cdb9f944-4f25-49df-a573-65612594183c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Production rate of vapor by conversion to rain</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QCDT_PR</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-36c16d01-4de1-48b2-a823-ff7f72849545' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36c16d01-4de1-48b2-a823-ff7f72849545' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7bf9799-524c-439c-b126-2033a39e0ac8' class='xr-var-data-in' type='checkbox'><label for='data-b7bf9799-524c-439c-b126-2033a39e0ac8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Production rate of vapor by conversion to rain</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QCDT_SED</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-deb9cd13-0288-436a-b89c-803db4b365b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-deb9cd13-0288-436a-b89c-803db4b365b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e2a065e-4fd6-45e7-b191-1367cc3502a8' class='xr-var-data-in' type='checkbox'><label for='data-4e2a065e-4fd6-45e7-b191-1367cc3502a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Tendency of cloud water due to sedimentation</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                    <tr>\n",
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+       "                    <tr>\n",
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+       "                    <tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RMINACT</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-3de53b8a-c666-4773-a1c9-54f7600bef2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3de53b8a-c666-4773-a1c9-54f7600bef2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41665037-1b42-4ea8-9f9f-54c03d1c2d91' class='xr-var-data-in' type='checkbox'><label for='data-41665037-1b42-4ea8-9f9f-54c03d1c2d91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Minimum activated radius in Morrison microphysics</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LANDMASK</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-5872a95d-9d56-4dbc-a95c-04355a07755e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5872a95d-9d56-4dbc-a95c-04355a07755e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0458a52c-c9bd-44c7-9026-8e353fcde85a' class='xr-var-data-in' type='checkbox'><label for='data-0458a52c-c9bd-44c7-9026-8e353fcde85a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LAND MASK (1 FOR LAND, 0 FOR WATER)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LAKEMASK</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-38faf961-568c-4958-a323-17b6228fcd40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38faf961-568c-4958-a323-17b6228fcd40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25203da1-50df-4d04-8bca-6f75584338d4' class='xr-var-data-in' type='checkbox'><label for='data-25203da1-50df-4d04-8bca-6f75584338d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LAKE MASK (1 FOR LAKE, 0 FOR NON-LAKE)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SST</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1a0f0b76-5dcc-45b6-96ab-0f0d00a57565' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a0f0b76-5dcc-45b6-96ab-0f0d00a57565' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc696900-0238-404b-94e9-8fc894c854d6' class='xr-var-data-in' type='checkbox'><label for='data-fc696900-0238-404b-94e9-8fc894c854d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SEA SURFACE TEMPERATURE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SST_INPUT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-69d278bb-989a-4209-93af-2f35ea94d11e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69d278bb-989a-4209-93af-2f35ea94d11e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88024fee-e622-4197-9467-8c5ed0771f66' class='xr-var-data-in' type='checkbox'><label for='data-88024fee-e622-4197-9467-8c5ed0771f66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SEA SURFACE TEMPERATURE FROM WRFLOWINPUT FILE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_potential_temperature</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-bc7e100e-3154-40f9-9002-312c8660ff26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc7e100e-3154-40f9-9002-312c8660ff26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd233b4e-48fc-4304-86d5-6b577c7bd5cd' class='xr-var-data-in' type='checkbox'><label for='data-fd233b4e-48fc-4304-86d5-6b577c7bd5cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>air_potential_temperature</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                        <th> Array </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_pressure</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f153cdd2-178b-4f71-a40b-af880522d474' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f153cdd2-178b-4f71-a40b-af880522d474' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20054e0a-d16e-4e1e-84a1-f8b88cb68c3e' class='xr-var-data-in' type='checkbox'><label for='data-20054e0a-d16e-4e1e-84a1-f8b88cb68c3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>Pa</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 67 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geopotential</span></div><div class='xr-var-dims'>(Time, z_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 227, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-23feba29-af8c-4447-b465-323fa285664a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23feba29-af8c-4447-b465-323fa285664a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac208a7a-2ef9-4612-b2b6-b041eec05be6' class='xr-var-data-in' type='checkbox'><label for='data-ac208a7a-2ef9-4612-b2b6-b041eec05be6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.81 GiB </td>\n",
+       "                        <td> 13.53 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 227, 250, 250) </td>\n",
+       "                        <td> (1, 227, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 67 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geopotential_height</span></div><div class='xr-var-dims'>(Time, z_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 227, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-ce21cae8-4798-46f7-987d-143aa08c64e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce21cae8-4798-46f7-987d-143aa08c64e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68bb5062-4df3-4c24-80c6-2dd91b776164' class='xr-var-data-in' type='checkbox'><label for='data-68bb5062-4df3-4c24-80c6-2dd91b776164' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>geopotential_height</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.81 GiB </td>\n",
+       "                        <td> 13.53 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 227, 250, 250) </td>\n",
+       "                        <td> (1, 227, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 68 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_east</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-043cc387-afee-4c8c-abfd-9dffe1879378' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-043cc387-afee-4c8c-abfd-9dffe1879378' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-039660a4-f9df-428d-ba9b-fe46f96dc0b2' class='xr-var-data-in' type='checkbox'><label for='data-039660a4-f9df-428d-ba9b-fe46f96dc0b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative x-wind component</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wrf_projection</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 819 chunks in 155 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_north</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-7a89b89b-6af8-4d44-974f-de35febb95c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a89b89b-6af8-4d44-974f-de35febb95c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-390d6351-fe6f-47ed-8858-6f5ff4ef2430' class='xr-var-data-in' type='checkbox'><label for='data-390d6351-fe6f-47ed-8858-6f5ff4ef2430' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative y-wind component</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wrf_projection</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_east_10</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-3849b081-8bac-4c9d-99e2-c02578c8b5bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3849b081-8bac-4c9d-99e2-c02578c8b5bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcc82fae-5b54-46d2-a65f-c1396e4a8fd3' class='xr-var-data-in' type='checkbox'><label for='data-bcc82fae-5b54-46d2-a65f-c1396e4a8fd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative 10m x-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wrf_projection</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 135 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_north_10</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-3cc94a5f-2e27-4361-be96-3655a0390928' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cc94a5f-2e27-4361-be96-3655a0390928' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af96911d-6e88-4e44-a620-ecd1b0d1baf2' class='xr-var-data-in' type='checkbox'><label for='data-af96911d-6e88-4e44-a620-ecd1b0d1baf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative 10m y-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wrf_projection</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 135 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c1b3dab-0dfa-4b9e-913f-0ef04bb9742a' class='xr-section-summary-in' type='checkbox'  ><label for='section-1c1b3dab-0dfa-4b9e-913f-0ef04bb9742a' class='xr-section-summary' >Indexes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6493d320-bcbf-4b5c-ad3f-6ccbbbf6bf89' class='xr-index-data-in' type='checkbox'/><label for='index-6493d320-bcbf-4b5c-ad3f-6ccbbbf6bf89' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.9979435801506042,    0.9938474893569946,    0.9897772073745728,\n",
+       "          0.9857232570648193,    0.9816849231719971,    0.9776613116264343,\n",
+       "          0.9736500978469849,    0.9696520566940308,    0.9656686782836914,\n",
+       "          0.9616985321044922,\n",
+       "       ...\n",
+       "         0.07436569780111313,   0.06433471292257309,  0.054764386266469955,\n",
+       "         0.04565010964870453,   0.03694861754775047,   0.02862708829343319,\n",
+       "        0.020665660500526428,  0.013039967976510525,  0.005742852110415697,\n",
+       "       0.0010888208635151386],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;z&#x27;, length=226))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z_stag</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8474f8d1-3036-42e6-8202-ad29cb861279' class='xr-index-data-in' type='checkbox'/><label for='index-8474f8d1-3036-42e6-8202-ad29cb861279' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                  1.0,    0.9958871603012085,    0.9918078780174255,\n",
+       "            0.98774653673172,    0.9837000370025635,    0.9796698093414307,\n",
+       "           0.975652813911438,    0.9716474413871765,    0.9676567316055298,\n",
+       "          0.9636806845664978,\n",
+       "       ...\n",
+       "         0.06923836469650269,  0.059431057423353195,  0.050097715109586716,\n",
+       "         0.04120250791311264,  0.032694727182388306,  0.024559449404478073,\n",
+       "        0.016771873459219933,  0.009308062493801117, 0.0021776417270302773,\n",
+       "                         0.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;z_stag&#x27;, length=227))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bb4ee067-0b66-4b1c-88ce-d84a23245f5c' class='xr-index-data-in' type='checkbox'/><label for='index-bb4ee067-0b66-4b1c-88ce-d84a23245f5c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-04-04 12:00:00&#x27;, &#x27;2019-04-04 12:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 12:20:00&#x27;, &#x27;2019-04-04 12:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 12:40:00&#x27;, &#x27;2019-04-04 12:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 13:00:00&#x27;, &#x27;2019-04-04 13:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 13:20:00&#x27;, &#x27;2019-04-04 13:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 13:40:00&#x27;, &#x27;2019-04-04 13:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 14:00:00&#x27;, &#x27;2019-04-04 14:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 14:20:00&#x27;, &#x27;2019-04-04 14:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 14:40:00&#x27;, &#x27;2019-04-04 14:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 15:00:00&#x27;, &#x27;2019-04-04 15:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 15:20:00&#x27;, &#x27;2019-04-04 15:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 15:40:00&#x27;, &#x27;2019-04-04 15:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 16:00:00&#x27;, &#x27;2019-04-04 16:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 16:20:00&#x27;, &#x27;2019-04-04 16:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 16:40:00&#x27;, &#x27;2019-04-04 16:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 17:00:00&#x27;, &#x27;2019-04-04 17:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 17:20:00&#x27;, &#x27;2019-04-04 17:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 17:40:00&#x27;, &#x27;2019-04-04 17:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 18:00:00&#x27;, &#x27;2019-04-04 18:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 18:20:00&#x27;, &#x27;2019-04-04 18:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 18:40:00&#x27;, &#x27;2019-04-04 18:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 19:00:00&#x27;, &#x27;2019-04-04 19:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 19:20:00&#x27;, &#x27;2019-04-04 19:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 19:40:00&#x27;, &#x27;2019-04-04 19:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 20:00:00&#x27;, &#x27;2019-04-04 20:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 20:20:00&#x27;, &#x27;2019-04-04 20:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 20:40:00&#x27;, &#x27;2019-04-04 20:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 21:00:00&#x27;, &#x27;2019-04-04 21:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 21:20:00&#x27;, &#x27;2019-04-04 21:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 21:40:00&#x27;, &#x27;2019-04-04 21:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 22:00:00&#x27;, &#x27;2019-04-04 22:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 22:20:00&#x27;, &#x27;2019-04-04 22:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 22:40:00&#x27;, &#x27;2019-04-04 22:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 23:00:00&#x27;, &#x27;2019-04-04 23:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 23:20:00&#x27;, &#x27;2019-04-04 23:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 23:40:00&#x27;, &#x27;2019-04-04 23:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 00:00:00&#x27;, &#x27;2019-04-05 00:10:00&#x27;,\n",
+       "               &#x27;2019-04-05 00:20:00&#x27;, &#x27;2019-04-05 00:30:00&#x27;,\n",
+       "               &#x27;2019-04-05 00:40:00&#x27;, &#x27;2019-04-05 00:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 01:00:00&#x27;, &#x27;2019-04-05 01:10:00&#x27;,\n",
+       "               &#x27;2019-04-05 01:20:00&#x27;, &#x27;2019-04-05 01:30:00&#x27;,\n",
+       "               &#x27;2019-04-05 01:40:00&#x27;, &#x27;2019-04-05 01:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 02:00:00&#x27;, &#x27;2019-04-05 02:10:00&#x27;,\n",
+       "               &#x27;2019-04-05 02:20:00&#x27;, &#x27;2019-04-05 02:30:00&#x27;,\n",
+       "               &#x27;2019-04-05 02:40:00&#x27;, &#x27;2019-04-05 02:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 03:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_stag</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cd676b8f-dee9-4415-b8f0-1c128d1a3544' class='xr-index-data-in' type='checkbox'/><label for='index-cd676b8f-dee9-4415-b8f0-1c128d1a3544' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12500.0, -12400.0, -12300.0, -12200.0, -12100.0, -12000.0, -11900.0,\n",
+       "       -11800.0, -11700.0, -11600.0,\n",
+       "       ...\n",
+       "        11600.0,  11700.0,  11800.0,  11900.0,  12000.0,  12100.0,  12200.0,\n",
+       "        12300.0,  12400.0,  12500.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y_stag&#x27;, length=251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-16c83b25-dab9-4066-a205-7e23f0edee2c' class='xr-index-data-in' type='checkbox'/><label for='index-16c83b25-dab9-4066-a205-7e23f0edee2c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12450.0, -12350.0, -12250.0, -12150.0, -12050.0, -11950.0, -11850.0,\n",
+       "       -11750.0, -11650.0, -11550.0,\n",
+       "       ...\n",
+       "        11550.0,  11650.0,  11750.0,  11850.0,  11950.0,  12050.0,  12150.0,\n",
+       "        12250.0,  12350.0,  12450.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=250))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-62591188-d2f6-4c89-b652-0341f6a62150' class='xr-index-data-in' type='checkbox'/><label for='index-62591188-d2f6-4c89-b652-0341f6a62150' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12450.0, -12350.0, -12250.0, -12150.0, -12050.0, -11950.0, -11850.0,\n",
+       "       -11750.0, -11650.0, -11550.0,\n",
+       "       ...\n",
+       "        11550.0,  11650.0,  11750.0,  11850.0,  11950.0,  12050.0,  12150.0,\n",
+       "        12250.0,  12350.0,  12450.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=250))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x_stag</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6bd21250-7eb7-4938-9bf5-cbf6fbef93b0' class='xr-index-data-in' type='checkbox'/><label for='index-6bd21250-7eb7-4938-9bf5-cbf6fbef93b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12500.0, -12400.0, -12300.0, -12200.0, -12100.0, -12000.0, -11900.0,\n",
+       "       -11800.0, -11700.0, -11600.0,\n",
+       "       ...\n",
+       "        11600.0,  11700.0,  11800.0,  11900.0,  12000.0,  12100.0,  12200.0,\n",
+       "        12300.0,  12400.0,  12500.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x_stag&#x27;, length=251))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41277c88-c58a-4586-9360-3d976bc5f632' class='xr-section-summary-in' type='checkbox'  ><label for='section-41277c88-c58a-4586-9360-3d976bc5f632' class='xr-section-summary' >Attributes: <span>(142)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TITLE :</span></dt><dd> OUTPUT FROM WRF V3.8.1 MODEL</dd><dt><span>START_DATE :</span></dt><dd>2019-04-04_12:00:00</dd><dt><span>SIMULATION_START_DATE :</span></dt><dd>2019-04-04_12:00:00</dd><dt><span>WEST-EAST_GRID_DIMENSION :</span></dt><dd>251</dd><dt><span>SOUTH-NORTH_GRID_DIMENSION :</span></dt><dd>251</dd><dt><span>BOTTOM-TOP_GRID_DIMENSION :</span></dt><dd>227</dd><dt><span>DX :</span></dt><dd>100.0</dd><dt><span>DY :</span></dt><dd>100.0</dd><dt><span>SKEBS_ON :</span></dt><dd>0</dd><dt><span>SPEC_BDY_FINAL_MU :</span></dt><dd>1</dd><dt><span>USE_Q_DIABATIC :</span></dt><dd>0</dd><dt><span>GRIDTYPE :</span></dt><dd>C</dd><dt><span>DIFF_OPT :</span></dt><dd>2</dd><dt><span>KM_OPT :</span></dt><dd>2</dd><dt><span>DAMP_OPT :</span></dt><dd>3</dd><dt><span>DAMPCOEF :</span></dt><dd>0.2</dd><dt><span>KHDIF :</span></dt><dd>1.0</dd><dt><span>KVDIF :</span></dt><dd>1.0</dd><dt><span>MP_PHYSICS :</span></dt><dd>8</dd><dt><span>RA_LW_PHYSICS :</span></dt><dd>4</dd><dt><span>RA_SW_PHYSICS :</span></dt><dd>4</dd><dt><span>SF_SFCLAY_PHYSICS :</span></dt><dd>1</dd><dt><span>SF_SURFACE_PHYSICS :</span></dt><dd>1</dd><dt><span>BL_PBL_PHYSICS :</span></dt><dd>0</dd><dt><span>CU_PHYSICS :</span></dt><dd>0</dd><dt><span>SF_LAKE_PHYSICS :</span></dt><dd>0</dd><dt><span>SURFACE_INPUT_SOURCE :</span></dt><dd>3</dd><dt><span>SST_UPDATE :</span></dt><dd>0</dd><dt><span>GRID_FDDA :</span></dt><dd>0</dd><dt><span>GFDDA_INTERVAL_M :</span></dt><dd>0</dd><dt><span>GFDDA_END_H :</span></dt><dd>0</dd><dt><span>GRID_SFDDA :</span></dt><dd>0</dd><dt><span>SGFDDA_INTERVAL_M :</span></dt><dd>0</dd><dt><span>SGFDDA_END_H :</span></dt><dd>0</dd><dt><span>HYPSOMETRIC_OPT :</span></dt><dd>1</dd><dt><span>USE_THETA_M :</span></dt><dd>1</dd><dt><span>SF_URBAN_PHYSICS :</span></dt><dd>0</dd><dt><span>SHCU_PHYSICS :</span></dt><dd>0</dd><dt><span>MFSHCONV :</span></dt><dd>0</dd><dt><span>FEEDBACK :</span></dt><dd>0</dd><dt><span>SMOOTH_OPTION :</span></dt><dd>0</dd><dt><span>SWRAD_SCAT :</span></dt><dd>1.0</dd><dt><span>W_DAMPING :</span></dt><dd>0</dd><dt><span>RADT :</span></dt><dd>1.0</dd><dt><span>BLDT :</span></dt><dd>0.0</dd><dt><span>CUDT :</span></dt><dd>0.0</dd><dt><span>AER_OPT :</span></dt><dd>0</dd><dt><span>SWINT_OPT :</span></dt><dd>0</dd><dt><span>AER_TYPE :</span></dt><dd>1</dd><dt><span>AER_AOD550_OPT :</span></dt><dd>1</dd><dt><span>AER_ANGEXP_OPT :</span></dt><dd>1</dd><dt><span>AER_SSA_OPT :</span></dt><dd>1</dd><dt><span>AER_ASY_OPT :</span></dt><dd>1</dd><dt><span>AER_AOD550_VAL :</span></dt><dd>0.12</dd><dt><span>AER_ANGEXP_VAL :</span></dt><dd>1.3</dd><dt><span>AER_SSA_VAL :</span></dt><dd>1e-45</dd><dt><span>AER_ASY_VAL :</span></dt><dd>1e-45</dd><dt><span>MOIST_ADV_OPT :</span></dt><dd>2</dd><dt><span>SCALAR_ADV_OPT :</span></dt><dd>2</dd><dt><span>TKE_ADV_OPT :</span></dt><dd>2</dd><dt><span>DIFF_6TH_OPT :</span></dt><dd>0</dd><dt><span>DIFF_6TH_FACTOR :</span></dt><dd>0.12</dd><dt><span>OBS_NUDGE_OPT :</span></dt><dd>0</dd><dt><span>BUCKET_MM :</span></dt><dd>-1.0</dd><dt><span>BUCKET_J :</span></dt><dd>-1.0</dd><dt><span>PREC_ACC_DT :</span></dt><dd>0.0</dd><dt><span>SF_OCEAN_PHYSICS :</span></dt><dd>0</dd><dt><span>ISFTCFLX :</span></dt><dd>0</dd><dt><span>ISHALLOW :</span></dt><dd>0</dd><dt><span>ISFFLX :</span></dt><dd>11</dd><dt><span>ICLOUD :</span></dt><dd>1</dd><dt><span>ICLOUD_CU :</span></dt><dd>0</dd><dt><span>TRACER_PBLMIX :</span></dt><dd>1</dd><dt><span>SCALAR_PBLMIX :</span></dt><dd>0</dd><dt><span>YSU_TOPDOWN_PBLMIX :</span></dt><dd>0</dd><dt><span>GRAV_SETTLING :</span></dt><dd>0</dd><dt><span>DFI_OPT :</span></dt><dd>0</dd><dt><span>SIMULATION_INITIALIZATION_TYPE :</span></dt><dd>IDEALIZED DATA</dd><dt><span>WEST-EAST_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>WEST-EAST_PATCH_END_UNSTAG :</span></dt><dd>250</dd><dt><span>WEST-EAST_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>WEST-EAST_PATCH_END_STAG :</span></dt><dd>251</dd><dt><span>SOUTH-NORTH_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>SOUTH-NORTH_PATCH_END_UNSTAG :</span></dt><dd>250</dd><dt><span>SOUTH-NORTH_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>SOUTH-NORTH_PATCH_END_STAG :</span></dt><dd>251</dd><dt><span>BOTTOM-TOP_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>BOTTOM-TOP_PATCH_END_UNSTAG :</span></dt><dd>226</dd><dt><span>BOTTOM-TOP_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>BOTTOM-TOP_PATCH_END_STAG :</span></dt><dd>227</dd><dt><span>GRID_ID :</span></dt><dd>1</dd><dt><span>PARENT_ID :</span></dt><dd>0</dd><dt><span>I_PARENT_START :</span></dt><dd>0</dd><dt><span>J_PARENT_START :</span></dt><dd>0</dd><dt><span>PARENT_GRID_RATIO :</span></dt><dd>1</dd><dt><span>DT :</span></dt><dd>0.5</dd><dt><span>CEN_LAT :</span></dt><dd>0.0</dd><dt><span>CEN_LON :</span></dt><dd>0.0</dd><dt><span>TRUELAT1 :</span></dt><dd>0.0</dd><dt><span>TRUELAT2 :</span></dt><dd>0.0</dd><dt><span>MOAD_CEN_LAT :</span></dt><dd>0.0</dd><dt><span>STAND_LON :</span></dt><dd>0.0</dd><dt><span>POLE_LAT :</span></dt><dd>0.0</dd><dt><span>POLE_LON :</span></dt><dd>0.0</dd><dt><span>GMT :</span></dt><dd>0.0</dd><dt><span>JULYR :</span></dt><dd>0</dd><dt><span>JULDAY :</span></dt><dd>1</dd><dt><span>MAP_PROJ :</span></dt><dd>0</dd><dt><span>MAP_PROJ_CHAR :</span></dt><dd>Cartesian</dd><dt><span>MMINLU :</span></dt><dd></dd><dt><span>NUM_LAND_CAT :</span></dt><dd>21</dd><dt><span>ISWATER :</span></dt><dd>16</dd><dt><span>ISLAKE :</span></dt><dd>0</dd><dt><span>ISICE :</span></dt><dd>0</dd><dt><span>ISURBAN :</span></dt><dd>0</dd><dt><span>ISOILWATER :</span></dt><dd>0</dd><dt><span>doi :</span></dt><dd>10.5439/1342961</dd><dt><span>contacts :</span></dt><dd>lasso@arm.gov, LASSO PI: William Gustafson (William.Gustafson@pnnl.gov), LASSO Co-PI: Andrew Vogelmann (vogelmann@bnl.gov)</dd><dt><span>site_id :</span></dt><dd>sgp</dd><dt><span>facility_id :</span></dt><dd>C1</dd><dt><span>location_description :</span></dt><dd>Southern Great Plains (SGP), Lamont, Oklahoma</dd><dt><span>date :</span></dt><dd>20190404</dd><dt><span>simulation_id_number :</span></dt><dd>4</dd><dt><span>model_type :</span></dt><dd>WRF</dd><dt><span>model_version :</span></dt><dd>3.8.1</dd><dt><span>model_github_hash :</span></dt><dd>b6b6a5cc4229eec1ea9b005746b5ebef2205fb07</dd><dt><span>output_domain_size :</span></dt><dd>25.0 km</dd><dt><span>output_number_of_levels :</span></dt><dd>226</dd><dt><span>output_horizontal_grid_spacing :</span></dt><dd>100 m</dd><dt><span>config_large_scale_forcing :</span></dt><dd>ECMWF</dd><dt><span>config_large_scale_forcing_scale :</span></dt><dd>114 km</dd><dt><span>config_large_scale_forcing_specifics :</span></dt><dd>sgpecmwfvarX1.c1,sgpecmwftenX1.c1,sgpecmwfsfc1lX1.c1,sgpecmwfsfceX1.c1 (v20191206)</dd><dt><span>config_surface_treatment :</span></dt><dd>VARANAL</dd><dt><span>config_surface_treatment_specifics :</span></dt><dd>sgp60varanarapC1.c1 (v20191126)</dd><dt><span>config_initial_condition :</span></dt><dd>Sounding</dd><dt><span>config_initial_condition_specifics :</span></dt><dd>sgpsondewnpnC1</dd><dt><span>config_aerosol :</span></dt><dd>NA</dd><dt><span>config_forecast_time :</span></dt><dd>15.0 h</dd><dt><span>config_boundary_method :</span></dt><dd>Periodic</dd><dt><span>config_microphysics :</span></dt><dd>Thompson (mp_physics=8)</dd><dt><span>config_nickname :</span></dt><dd>runlas20190404v1addhm</dd><dt><span>simulation_origin_host :</span></dt><dd>cumulus-login2.ccs.ornl.gov</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 255GB\n",
+       "Dimensions:                    (Time: 91, y: 250, x: 250, soil_layers_stag: 5,\n",
+       "                                z: 226, x_stag: 251, y_stag: 251, z_stag: 227,\n",
+       "                                force_layers: 751)\n",
+       "Coordinates: (12/15)\n",
+       "    CLAT                       (y, x) float32 250kB dask.array<chunksize=(125, 125), meta=np.ndarray>\n",
+       "    XLAT                       (y, x) float32 250kB dask.array<chunksize=(125, 125), meta=np.ndarray>\n",
+       "    XLONG                      (y, x) float32 250kB dask.array<chunksize=(125, 125), meta=np.ndarray>\n",
+       "    XTIME                      (Time) datetime64[ns] 728B dask.array<chunksize=(6,), meta=np.ndarray>\n",
+       "    XLAT_U                     (y, x_stag) float32 251kB dask.array<chunksize=(125, 126), meta=np.ndarray>\n",
+       "    XLONG_U                    (y, x_stag) float32 251kB dask.array<chunksize=(125, 126), meta=np.ndarray>\n",
+       "    ...                         ...\n",
+       "  * z_stag                     (z_stag) float32 908B 1.0 0.9959 ... 0.002178 0.0\n",
+       "  * Time                       (Time) datetime64[ns] 728B 2019-04-04T12:00:00...\n",
+       "  * y_stag                     (y_stag) float64 2kB -1.25e+04 ... 1.25e+04\n",
+       "  * y                          (y) float64 2kB -1.245e+04 ... 1.245e+04\n",
+       "  * x                          (x) float64 2kB -1.245e+04 ... 1.245e+04\n",
+       "  * x_stag                     (x_stag) float64 2kB -1.25e+04 ... 1.25e+04\n",
+       "Dimensions without coordinates: soil_layers_stag, force_layers\n",
+       "Data variables: (12/251)\n",
+       "    Times                      (Time) |S19 2kB dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    LU_INDEX                   (Time, y, x) float32 23MB dask.array<chunksize=(1, 125, 125), meta=np.ndarray>\n",
+       "    ZS                         (Time, soil_layers_stag) float32 2kB dask.array<chunksize=(1, 5), meta=np.ndarray>\n",
+       "    DZS                        (Time, soil_layers_stag) float32 2kB dask.array<chunksize=(1, 5), meta=np.ndarray>\n",
+       "    VAR_SSO                    (Time, y, x) float32 23MB dask.array<chunksize=(1, 125, 125), meta=np.ndarray>\n",
+       "    U                          (Time, z, y, x_stag) float32 5GB dask.array<chunksize=(1, 226, 125, 126), meta=np.ndarray>\n",
+       "    ...                         ...\n",
+       "    geopotential               (Time, z_stag, y, x) float32 5GB dask.array<chunksize=(1, 227, 125, 125), meta=np.ndarray>\n",
+       "    geopotential_height        (Time, z_stag, y, x) float32 5GB dask.array<chunksize=(1, 227, 125, 125), meta=np.ndarray>\n",
+       "    wind_east                  (Time, z, y, x) float32 5GB dask.array<chunksize=(1, 226, 125, 125), meta=np.ndarray>\n",
+       "    wind_north                 (Time, z, y, x) float32 5GB dask.array<chunksize=(1, 226, 125, 125), meta=np.ndarray>\n",
+       "    wind_east_10               (Time, y, x) float32 23MB dask.array<chunksize=(1, 125, 125), meta=np.ndarray>\n",
+       "    wind_north_10              (Time, y, x) float32 23MB dask.array<chunksize=(1, 125, 125), meta=np.ndarray>\n",
+       "Attributes: (12/142)\n",
+       "    TITLE:                                  OUTPUT FROM WRF V3.8.1 MODEL\n",
+       "    START_DATE:                            2019-04-04_12:00:00\n",
+       "    SIMULATION_START_DATE:                 2019-04-04_12:00:00\n",
+       "    WEST-EAST_GRID_DIMENSION:              251\n",
+       "    SOUTH-NORTH_GRID_DIMENSION:            251\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:             227\n",
+       "    ...                                    ...\n",
+       "    config_aerosol:                        NA\n",
+       "    config_forecast_time:                  15.0 h\n",
+       "    config_boundary_method:                Periodic\n",
+       "    config_microphysics:                   Thompson (mp_physics=8)\n",
+       "    config_nickname:                       runlas20190404v1addhm\n",
+       "    simulation_origin_host:                cumulus-login2.ccs.ornl.gov"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define path to the lasso simulation data\n",
+    "path_shcu_root = \"/data/project/ARM_Summer_School_2024_Data/lasso_tutorial/ShCu/untar\"  # on Jupyter\n",
+    "\n",
+    "#Define LASSO SGP case date and simulation of interest\n",
+    "case_date = datetime(2019, 4, 4) #Options[April 4, 2019; May 6, 2019]\n",
+    "sim_id = 4\n",
+    "\n",
+    "#Load in LASSO wrfstat files. These provide 10-minute averages for various metorology variables and diagnostics\n",
+    "ds_stat = xr.open_dataset(f\"{path_shcu_root}/{case_date:%Y%m%d}/sim{sim_id:04d}/raw_model/wrfstat_d01_{case_date:%Y-%m-%d_12:00:00}.nc\")\n",
+    "#ds_stat\n",
+    "\n",
+    "#Load in LASSO-ShCu wrfout data, which is raw simulation output from the Weather Research and Forecasting (WRF) model run in an idealized LES mode.\n",
+    "#Post process using xwrf package\n",
+    "ds = xr.open_mfdataset(f\"{path_shcu_root}/{case_date:%Y%m%d}/sim{sim_id:04d}/raw_model/wrfout_d01_*.nc\", combine=\"nested\", concat_dim=\"Time\").xwrf.postprocess()\n",
+    "\n",
+    "# By default, xarray does not interpret the wrfout/wrfstat time information in a way that attaches \n",
+    "# it to each variable. Here is a trick to map the time held in XTIME with the Time coordinate \n",
+    "# associated with each variable.\n",
+    "ds_stat[\"Time\"] = ds_stat[\"XTIME\"]\n",
+    "ds[\"Time\"] = ds[\"XTIME\"]\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f29b1d67-af48-4f31-bd80-f1e95275e4c8",
+   "metadata": {},
+   "source": [
+    "### Moisture convergence requires U, V, and moisture Q. We load these in below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "276b1ab9-d405-4b9b-9fc8-e8a3245399fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(250, 250)"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Load in u, v, and q data\n",
+    "U10 = ds[\"U10\"]\n",
+    "V10 = ds[\"V10\"]\n",
+    "QVAPOR = ds[\"QVAPOR\"].sel(z=10, method='nearest').sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\")\n",
+    "\n",
+    "#U and V have staggered x and y dimensions. The following unstaggers them to align with QVAPOR \n",
+    "U = ds.U.interp(x_stag=ds.x)\n",
+    "V = ds.V.interp(y_stag=ds.y)\n",
+    "\n",
+    "QVAPOR.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "af82c337-bb8a-464e-9948-4c65a071f524",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fe8be7e3050>"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can use xarray's plotting features to get time-labeled plots.\n",
+    "hour_to_plot = 17 #UTC (6hrs after simulation start)\n",
+    "\n",
+    "#This line shows the U winds at 10m from the surface at 18UTC\n",
+    "ds[\"U10\"].sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\").plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "fcdda6d1-622c-48b5-bc8e-3ffb034030f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1554/326214781.py:4: UserWarning: More than one latitude coordinate present for variable .\n",
+      "  div = mpcalc.divergence(QVAPOR*ds[\"U10\"].sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\"), QVAPOR*ds[\"V10\"].sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\"))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Horizontal Moisture Divergence: 10m')"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate the divergence of the flow\n",
+    "# Multiply by the water vapor (QVAPOR) to get the moisture divergenc\n",
+    "\n",
+    "div = mpcalc.divergence(QVAPOR*ds[\"U10\"].sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\"), QVAPOR*ds[\"V10\"].sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\"))\n",
+    "\n",
+    "# start figure and set axis\n",
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "\n",
+    "# plot divergence and scale by 1e5\n",
+    "cf = ax.contourf(ds.y, ds.x, div*1e5 , range(-15, 16, 1), cmap=plt.cm.bwr_r) #* 1e5\n",
+    "plt.colorbar(cf, pad=0, aspect=50)\n",
+    "#ax.barbs(ds.y.values, ds.x.values, ds.U10.sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\"), ds.V10.sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\"), color='black', length=0.5, alpha=0.5)\n",
+    "#ax.set(xlim=(260, 270), ylim=(30, 40))\n",
+    "ax.set_title('Horizontal Moisture Divergence: 10m')\n",
+    "#ax.set_\n",
+    "\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "880254a3-5401-4224-88e7-9b141fc3a09f",
+   "metadata": {},
+   "source": [
+    "### Moisture convergence at 1km"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "4e57262a-0bc7-4ad2-8805-4a016086379f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(250, 250)"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Load in u, v, and q data\n",
+    "#U and V have staggered x and y dimensions. The following unstaggers them to align with QVAPOR \n",
+    "U = ds.U.interp(x_stag=ds.x)\n",
+    "V = ds.V.interp(y_stag=ds.y)\n",
+    "\n",
+    "z = 1000 \n",
+    "\n",
+    "QVAPOR = ds[\"QVAPOR\"].sel(z=z,method='nearest').sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\")\n",
+    "QVAPOR.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "1b08a427-7b76-4c0d-a111-1b7eb33c8071",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(250, 250)\n",
+      "(250, 250)\n"
+     ]
+    }
+   ],
+   "source": [
+    "U_at_z = U.sel(z=z,method='nearest').sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\")\n",
+    "V_at_z = V.sel(z=z,method='nearest').sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\")\n",
+    "\n",
+    "print(U_at_z.shape)\n",
+    "print(V_at_z.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "5ceb190c-caae-4023-801e-c0c141ea0694",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1554/3030828318.py:4: UserWarning: More than one longitude coordinate present for variable .\n",
+      "  div2 = mpcalc.divergence(QVAPOR*U_at_z, QVAPOR*V_at_z)\n",
+      "/tmp/ipykernel_1554/3030828318.py:4: UserWarning: More than one latitude coordinate present for variable .\n",
+      "  div2 = mpcalc.divergence(QVAPOR*U_at_z, QVAPOR*V_at_z)\n",
+      "/tmp/ipykernel_1554/3030828318.py:4: UserWarning: More than one time coordinate present for variable .\n",
+      "  div2 = mpcalc.divergence(QVAPOR*U_at_z, QVAPOR*V_at_z)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate the divergence of the flow\n",
+    "# Multiply by the water vapor (QVAPOR) to get the moisture divergenc\n",
+    "\n",
+    "div2 = mpcalc.divergence(QVAPOR*U_at_z, QVAPOR*V_at_z)\n",
+    "\n",
+    "# start figure and set axis\n",
+    "fig2, ax = plt.subplots(figsize=(5, 5))\n",
+    "\n",
+    "# plot divergence and scale by 1e5\n",
+    "cf = ax.contourf(ds.y, ds.x, div2*1e5 , range(-15, 16, 1), cmap=plt.cm.bwr_r) #* 1e5\n",
+    "plt.colorbar(cf, pad=0, aspect=50)\n",
+    "#ax.barbs(ds.y.values, ds.x.values, ds.U10.sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\"), ds.V10.sel(Time=f\"{case_date:%Y-%m-%d} {hour_to_plot}:00\"), color='black', length=0.5, alpha=0.5)\n",
+    "#ax.set(xlim=(260, 270), ylim=(30, 40))\n",
+    "ax.set_title('Horizontal Moisture Divergence: 1000m')\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "410e42e9-ed44-490e-93bf-1b0a8435634d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 255GB\n",
+       "Dimensions:                    (Time: 91, y: 250, x: 250, soil_layers_stag: 5,\n",
+       "                                z: 226, x_stag: 251, y_stag: 251, z_stag: 227,\n",
+       "                                force_layers: 751)\n",
+       "Coordinates: (12/15)\n",
+       "    CLAT                       (y, x) float32 250kB dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;\n",
+       "    XLAT                       (y, x) float32 250kB dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;\n",
+       "    XLONG                      (y, x) float32 250kB dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;\n",
+       "    XTIME                      (Time) datetime64[ns] 728B dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;\n",
+       "    XLAT_U                     (y, x_stag) float32 251kB dask.array&lt;chunksize=(125, 126), meta=np.ndarray&gt;\n",
+       "    XLONG_U                    (y, x_stag) float32 251kB dask.array&lt;chunksize=(125, 126), meta=np.ndarray&gt;\n",
+       "    ...                         ...\n",
+       "  * z_stag                     (z_stag) float32 908B 1.0 0.9959 ... 0.002178 0.0\n",
+       "  * Time                       (Time) datetime64[ns] 728B 2019-04-04T12:00:00...\n",
+       "  * y_stag                     (y_stag) float64 2kB -1.25e+04 ... 1.25e+04\n",
+       "  * y                          (y) float64 2kB -1.245e+04 ... 1.245e+04\n",
+       "  * x                          (x) float64 2kB -1.245e+04 ... 1.245e+04\n",
+       "  * x_stag                     (x_stag) float64 2kB -1.25e+04 ... 1.25e+04\n",
+       "Dimensions without coordinates: soil_layers_stag, force_layers\n",
+       "Data variables: (12/251)\n",
+       "    Times                      (Time) |S19 2kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    LU_INDEX                   (Time, y, x) float32 23MB dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;\n",
+       "    ZS                         (Time, soil_layers_stag) float32 2kB dask.array&lt;chunksize=(1, 5), meta=np.ndarray&gt;\n",
+       "    DZS                        (Time, soil_layers_stag) float32 2kB dask.array&lt;chunksize=(1, 5), meta=np.ndarray&gt;\n",
+       "    VAR_SSO                    (Time, y, x) float32 23MB dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;\n",
+       "    U                          (Time, z, y, x_stag) float32 5GB dask.array&lt;chunksize=(1, 226, 125, 126), meta=np.ndarray&gt;\n",
+       "    ...                         ...\n",
+       "    geopotential               (Time, z_stag, y, x) float32 5GB dask.array&lt;chunksize=(1, 227, 125, 125), meta=np.ndarray&gt;\n",
+       "    geopotential_height        (Time, z_stag, y, x) float32 5GB dask.array&lt;chunksize=(1, 227, 125, 125), meta=np.ndarray&gt;\n",
+       "    wind_east                  (Time, z, y, x) float32 5GB dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;\n",
+       "    wind_north                 (Time, z, y, x) float32 5GB dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;\n",
+       "    wind_east_10               (Time, y, x) float32 23MB dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;\n",
+       "    wind_north_10              (Time, y, x) float32 23MB dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;\n",
+       "Attributes: (12/142)\n",
+       "    TITLE:                                  OUTPUT FROM WRF V3.8.1 MODEL\n",
+       "    START_DATE:                            2019-04-04_12:00:00\n",
+       "    SIMULATION_START_DATE:                 2019-04-04_12:00:00\n",
+       "    WEST-EAST_GRID_DIMENSION:              251\n",
+       "    SOUTH-NORTH_GRID_DIMENSION:            251\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:             227\n",
+       "    ...                                    ...\n",
+       "    config_aerosol:                        NA\n",
+       "    config_forecast_time:                  15.0 h\n",
+       "    config_boundary_method:                Periodic\n",
+       "    config_microphysics:                   Thompson (mp_physics=8)\n",
+       "    config_nickname:                       runlas20190404v1addhm\n",
+       "    simulation_origin_host:                cumulus-login2.ccs.ornl.gov</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b1235d9f-747b-41fe-873d-9fa361bb9c6b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b1235d9f-747b-41fe-873d-9fa361bb9c6b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Time</span>: 91</li><li><span class='xr-has-index'>y</span>: 250</li><li><span class='xr-has-index'>x</span>: 250</li><li><span>soil_layers_stag</span>: 5</li><li><span class='xr-has-index'>z</span>: 226</li><li><span class='xr-has-index'>x_stag</span>: 251</li><li><span class='xr-has-index'>y_stag</span>: 251</li><li><span class='xr-has-index'>z_stag</span>: 227</li><li><span>force_layers</span>: 751</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-61031b7c-67eb-4cbf-bcb4-838a01424098' class='xr-section-summary-in' type='checkbox'  checked><label for='section-61031b7c-67eb-4cbf-bcb4-838a01424098' class='xr-section-summary' >Coordinates: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>CLAT</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;</div><input id='attrs-fa6c5d8d-b89d-4c81-8b14-eab6671607a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa6c5d8d-b89d-4c81-8b14-eab6671607a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-707694f3-d750-4391-93b1-42615dfae780' class='xr-var-data-in' type='checkbox'><label for='data-707694f3-d750-4391-93b1-42615dfae780' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>COMPUTATIONAL GRID LATITUDE, SOUTH IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 244.14 kiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (250, 250) </td>\n",
+       "                        <td> (125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">250</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLAT</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;</div><input id='attrs-e8ccce13-56e4-44e7-9df8-42619ce9ec6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8ccce13-56e4-44e7-9df8-42619ce9ec6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6894f62d-a96e-44e1-856f-95948ceae84b' class='xr-var-data-in' type='checkbox'><label for='data-6894f62d-a96e-44e1-856f-95948ceae84b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LATITUDE, SOUTH IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 244.14 kiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (250, 250) </td>\n",
+       "                        <td> (125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">250</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLONG</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(125, 125), meta=np.ndarray&gt;</div><input id='attrs-9b6b2141-02ec-48df-86c4-d8cd3d82a2a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b6b2141-02ec-48df-86c4-d8cd3d82a2a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e23c1222-c02c-4231-b00e-48175bf096cd' class='xr-var-data-in' type='checkbox'><label for='data-e23c1222-c02c-4231-b00e-48175bf096cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LONGITUDE, WEST IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 244.14 kiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (250, 250) </td>\n",
+       "                        <td> (125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
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+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">250</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XTIME</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-ad1c9ed6-43f1-4337-94ab-c3b3d4c33f80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad1c9ed6-43f1-4337-94ab-c3b3d4c33f80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10bc7d6c-9bd8-419a-b347-425754585ada' class='xr-var-data-in' type='checkbox'><label for='data-10bc7d6c-9bd8-419a-b347-425754585ada' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>minutes since 2019-04-04 12:00:00</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 728 B </td>\n",
+       "                        <td> 48 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"79\" y1=\"0\" x2=\"79\" y2=\"25\" />\n",
+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLAT_U</span></div><div class='xr-var-dims'>(y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(125, 126), meta=np.ndarray&gt;</div><input id='attrs-cf118287-3688-4efc-8029-124676230164' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf118287-3688-4efc-8029-124676230164' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3de5dd9-93e4-4054-a161-78da0611e209' class='xr-var-data-in' type='checkbox'><label for='data-e3de5dd9-93e4-4054-a161-78da0611e209' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LATITUDE, SOUTH IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 245.12 kiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (250, 251) </td>\n",
+       "                        <td> (125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"169\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"119\" x2=\"120\" y2=\"119\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"119\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"119\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,119.5219123505976 0.0,119.5219123505976\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"139.521912\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >251</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLONG_U</span></div><div class='xr-var-dims'>(y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(125, 126), meta=np.ndarray&gt;</div><input id='attrs-58167aaf-7381-4cb8-a614-a41b4803de8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58167aaf-7381-4cb8-a614-a41b4803de8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4412032e-f22d-4da4-83fe-c4546c12ecfb' class='xr-var-data-in' type='checkbox'><label for='data-4412032e-f22d-4da4-83fe-c4546c12ecfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LONGITUDE, WEST IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 245.12 kiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (250, 251) </td>\n",
+       "                        <td> (125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"169\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"119\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLAT_V</span></div><div class='xr-var-dims'>(y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(126, 125), meta=np.ndarray&gt;</div><input id='attrs-392ba09a-d414-44ae-bea2-5bcc5cef0820' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-392ba09a-d414-44ae-bea2-5bcc5cef0820' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e5c16d1-502b-411f-9382-f8136d97bf43' class='xr-var-data-in' type='checkbox'><label for='data-1e5c16d1-502b-411f-9382-f8136d97bf43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LATITUDE, SOUTH IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 245.12 kiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (251, 250) </td>\n",
+       "                        <td> (126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"169\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"119\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"119\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"119\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"120\" />\n",
+       "  <line x1=\"119\" y1=\"0\" x2=\"119\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 119.5219123505976,0.0 119.5219123505976,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"59.760956\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
+       "  <text x=\"139.521912\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,139.521912,60.000000)\">251</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLONG_V</span></div><div class='xr-var-dims'>(y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(126, 125), meta=np.ndarray&gt;</div><input id='attrs-ff24c16e-2f5c-444d-906f-13aadd4802c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff24c16e-2f5c-444d-906f-13aadd4802c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1431b77c-64af-4e49-ba83-4fe1806503b7' class='xr-var-data-in' type='checkbox'><label for='data-1431b77c-64af-4e49-ba83-4fe1806503b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LONGITUDE, WEST IS NEGATIVE</dd><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 245.12 kiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (251, 250) </td>\n",
+       "                        <td> (126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"169\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"119\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"119\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"119\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"120\" />\n",
+       "  <line x1=\"119\" y1=\"0\" x2=\"119\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 119.5219123505976,0.0 119.5219123505976,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"59.760956\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
+       "  <text x=\"139.521912\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,139.521912,60.000000)\">251</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.9979 0.9938 ... 0.005743 0.001089</div><input id='attrs-d336e1b0-7857-42ed-92e2-3ed46dda2593' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d336e1b0-7857-42ed-92e2-3ed46dda2593' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-794a9949-603e-46dd-b432-7909a729c3ac' class='xr-var-data-in' type='checkbox'><label for='data-794a9949-603e-46dd-b432-7909a729c3ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>eta values on half (mass) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>standard_name :</span></dt><dd>atmosphere_sigma_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([0.997944, 0.993847, 0.989777, ..., 0.01304 , 0.005743, 0.001089],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_stag</span></div><div class='xr-var-dims'>(z_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 0.9959 0.9918 ... 0.002178 0.0</div><input id='attrs-e47dc4b3-a4a4-48d0-8487-9c5419140c6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e47dc4b3-a4a4-48d0-8487-9c5419140c6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-257fc58a-f780-427a-8882-a004d189f07d' class='xr-var-data-in' type='checkbox'><label for='data-257fc58a-f780-427a-8882-a004d189f07d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>eta values on full (w) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Z</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd><dt><span>standard_name :</span></dt><dd>atmosphere_sigma_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1.      , 0.995887, 0.991808, ..., 0.009308, 0.002178, 0.      ],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Time</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-04-04T12:00:00 ... 2019-04-...</div><input id='attrs-778a2f09-9f41-477d-9a9b-ba7659e98294' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-778a2f09-9f41-477d-9a9b-ba7659e98294' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3f26b02-e1a7-41f0-b9fd-5ff7caee8f04' class='xr-var-data-in' type='checkbox'><label for='data-b3f26b02-e1a7-41f0-b9fd-5ff7caee8f04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>minutes since 2019-04-04 12:00:00</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-04-04T12:00:00.000000000&#x27;, &#x27;2019-04-04T12:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T12:20:00.000000000&#x27;, &#x27;2019-04-04T12:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T12:40:00.000000000&#x27;, &#x27;2019-04-04T12:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T13:00:00.000000000&#x27;, &#x27;2019-04-04T13:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T13:20:00.000000000&#x27;, &#x27;2019-04-04T13:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T13:40:00.000000000&#x27;, &#x27;2019-04-04T13:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T14:00:00.000000000&#x27;, &#x27;2019-04-04T14:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T14:20:00.000000000&#x27;, &#x27;2019-04-04T14:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T14:40:00.000000000&#x27;, &#x27;2019-04-04T14:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T15:00:00.000000000&#x27;, &#x27;2019-04-04T15:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T15:20:00.000000000&#x27;, &#x27;2019-04-04T15:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T15:40:00.000000000&#x27;, &#x27;2019-04-04T15:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T16:00:00.000000000&#x27;, &#x27;2019-04-04T16:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T16:20:00.000000000&#x27;, &#x27;2019-04-04T16:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T16:40:00.000000000&#x27;, &#x27;2019-04-04T16:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T17:00:00.000000000&#x27;, &#x27;2019-04-04T17:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T17:20:00.000000000&#x27;, &#x27;2019-04-04T17:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T17:40:00.000000000&#x27;, &#x27;2019-04-04T17:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T18:00:00.000000000&#x27;, &#x27;2019-04-04T18:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T18:20:00.000000000&#x27;, &#x27;2019-04-04T18:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T18:40:00.000000000&#x27;, &#x27;2019-04-04T18:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T19:00:00.000000000&#x27;, &#x27;2019-04-04T19:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T19:20:00.000000000&#x27;, &#x27;2019-04-04T19:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T19:40:00.000000000&#x27;, &#x27;2019-04-04T19:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T20:00:00.000000000&#x27;, &#x27;2019-04-04T20:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T20:20:00.000000000&#x27;, &#x27;2019-04-04T20:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T20:40:00.000000000&#x27;, &#x27;2019-04-04T20:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T21:00:00.000000000&#x27;, &#x27;2019-04-04T21:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T21:20:00.000000000&#x27;, &#x27;2019-04-04T21:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T21:40:00.000000000&#x27;, &#x27;2019-04-04T21:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T22:00:00.000000000&#x27;, &#x27;2019-04-04T22:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T22:20:00.000000000&#x27;, &#x27;2019-04-04T22:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T22:40:00.000000000&#x27;, &#x27;2019-04-04T22:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T23:00:00.000000000&#x27;, &#x27;2019-04-04T23:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T23:20:00.000000000&#x27;, &#x27;2019-04-04T23:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-04T23:40:00.000000000&#x27;, &#x27;2019-04-04T23:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T00:00:00.000000000&#x27;, &#x27;2019-04-05T00:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T00:20:00.000000000&#x27;, &#x27;2019-04-05T00:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T00:40:00.000000000&#x27;, &#x27;2019-04-05T00:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T01:00:00.000000000&#x27;, &#x27;2019-04-05T01:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T01:20:00.000000000&#x27;, &#x27;2019-04-05T01:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T01:40:00.000000000&#x27;, &#x27;2019-04-05T01:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T02:00:00.000000000&#x27;, &#x27;2019-04-05T02:10:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T02:20:00.000000000&#x27;, &#x27;2019-04-05T02:30:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T02:40:00.000000000&#x27;, &#x27;2019-04-05T02:50:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-05T03:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_stag</span></div><div class='xr-var-dims'>(y_stag)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.25e+04 -1.24e+04 ... 1.25e+04</div><input id='attrs-b744b6e1-1b03-4169-bdfe-441d27b04271' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b744b6e1-1b03-4169-bdfe-441d27b04271' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d998051b-27fd-4850-9cac-39e7846ddd8a' class='xr-var-data-in' type='checkbox'><label for='data-d998051b-27fd-4850-9cac-39e7846ddd8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([-12500., -12400., -12300., ...,  12300.,  12400.,  12500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.245e+04 -1.235e+04 ... 1.245e+04</div><input id='attrs-dce45720-5c59-4b71-b46f-18b75f3dcdb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dce45720-5c59-4b71-b46f-18b75f3dcdb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a558a188-6db9-4b2e-9763-8a253258cc98' class='xr-var-data-in' type='checkbox'><label for='data-a558a188-6db9-4b2e-9763-8a253258cc98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-12450., -12350., -12250., ...,  12250.,  12350.,  12450.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.245e+04 -1.235e+04 ... 1.245e+04</div><input id='attrs-83316ca5-7c90-4773-b5c9-0a63d9b77199' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83316ca5-7c90-4773-b5c9-0a63d9b77199' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f639921-ee98-41b8-bd10-06b323103e08' class='xr-var-data-in' type='checkbox'><label for='data-1f639921-ee98-41b8-bd10-06b323103e08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-12450., -12350., -12250., ...,  12250.,  12350.,  12450.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x_stag</span></div><div class='xr-var-dims'>(x_stag)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.25e+04 -1.24e+04 ... 1.25e+04</div><input id='attrs-318335e8-e1a2-4e61-94e0-6fad4d33cc03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-318335e8-e1a2-4e61-94e0-6fad4d33cc03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b736a2f-7c1f-4b52-a077-3868f6c19067' class='xr-var-data-in' type='checkbox'><label for='data-2b736a2f-7c1f-4b52-a077-3868f6c19067' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([-12500., -12400., -12300., ...,  12300.,  12400.,  12500.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2ec97db3-6eb7-49d8-8bd4-21f364a99cd9' class='xr-section-summary-in' type='checkbox'  ><label for='section-2ec97db3-6eb7-49d8-8bd4-21f364a99cd9' class='xr-section-summary' >Data variables: <span>(251)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Times</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>|S19</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-227ba399-94aa-44be-bb64-f0fadf5dea46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-227ba399-94aa-44be-bb64-f0fadf5dea46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a94ac3f4-18a2-4295-a70d-9890d7be2250' class='xr-var-data-in' type='checkbox'><label for='data-a94ac3f4-18a2-4295-a70d-9890d7be2250' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.69 kiB </td>\n",
+       "                        <td> 19 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> |S19 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n",
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+       "  <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"68\" y1=\"0\" x2=\"68\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n",
+       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
+       "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
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+       "  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n",
+       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LU_INDEX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-5824a1ae-ae20-4671-87ce-3dd98469004e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5824a1ae-ae20-4671-87ce-3dd98469004e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9d4e744-689a-41ff-b7a8-6d147558f206' class='xr-var-data-in' type='checkbox'><label for='data-a9d4e744-689a-41ff-b7a8-6d147558f206' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LAND USE CATEGORY</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"205\" height=\"195\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"145\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZS</span></div><div class='xr-var-dims'>(Time, soil_layers_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5), meta=np.ndarray&gt;</div><input id='attrs-f7e2dda3-7e5c-4b45-a3d3-b0b8aef590a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7e2dda3-7e5c-4b45-a3d3-b0b8aef590a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20a7f12f-3d5e-43e0-aa91-0f5d60269692' class='xr-var-data-in' type='checkbox'><label for='data-20a7f12f-3d5e-43e0-aa91-0f5d60269692' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>DEPTHS OF CENTERS OF SOIL LAYERS</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.78 kiB </td>\n",
+       "                        <td> 20 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 5) </td>\n",
+       "                        <td> (1, 5) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"85\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DZS</span></div><div class='xr-var-dims'>(Time, soil_layers_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5), meta=np.ndarray&gt;</div><input id='attrs-ed4bdc7c-b69d-4508-a635-63474dfbabe6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed4bdc7c-b69d-4508-a635-63474dfbabe6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa0d78ad-b254-42c2-ad6a-8b4dccab799b' class='xr-var-data-in' type='checkbox'><label for='data-fa0d78ad-b254-42c2-ad6a-8b4dccab799b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>THICKNESSES OF SOIL LAYERS</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.78 kiB </td>\n",
+       "                        <td> 20 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 5) </td>\n",
+       "                        <td> (1, 5) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VAR_SSO</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e5f502a5-a337-4017-98fc-4dde16fb0869' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5f502a5-a337-4017-98fc-4dde16fb0869' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07526440-7ca2-4f88-b40b-959d229f1c4b' class='xr-var-data-in' type='checkbox'><label for='data-07526440-7ca2-4f88-b40b-959d229f1c4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Variance of Subgrid Scale Orography MSL</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(Time, z, y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 126), meta=np.ndarray&gt;</div><input id='attrs-67a191b6-825b-473d-8793-c30c1e9764f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67a191b6-825b-473d-8793-c30c1e9764f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a68d174-2835-4f76-a0d5-580b87ffd612' class='xr-var-data-in' type='checkbox'><label for='data-3a68d174-2835-4f76-a0d5-580b87ffd612' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>x-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>x_wind</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.81 GiB </td>\n",
+       "                        <td> 13.58 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 251) </td>\n",
+       "                        <td> (1, 226, 125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V</span></div><div class='xr-var-dims'>(Time, z, y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 126, 125), meta=np.ndarray&gt;</div><input id='attrs-8174d52c-267f-4d57-a17d-92acdc13ca61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8174d52c-267f-4d57-a17d-92acdc13ca61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80c6285b-3ec3-4e25-8304-b84bd146a6e0' class='xr-var-data-in' type='checkbox'><label for='data-80c6285b-3ec3-4e25-8304-b84bd146a6e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>y-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>y_wind</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.81 GiB </td>\n",
+       "                        <td> 13.58 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 251, 250) </td>\n",
+       "                        <td> (1, 226, 126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W</span></div><div class='xr-var-dims'>(Time, z_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 227, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-61eaefae-9cc8-48ac-8985-d1c5712b0e2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61eaefae-9cc8-48ac-8985-d1c5712b0e2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66511e74-ff64-45a4-adb6-1f1e51fc27a4' class='xr-var-data-in' type='checkbox'><label for='data-66511e74-ff64-45a4-adb6-1f1e51fc27a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>z-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd>Z</dd><dt><span>standard_name :</span></dt><dd>upward_air_velocity</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.81 GiB </td>\n",
+       "                        <td> 13.53 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 227, 250, 250) </td>\n",
+       "                        <td> (1, 227, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HFX_FORCE</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-81c03b3e-92f4-4b1e-865a-47f0eeb669c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81c03b3e-92f4-4b1e-865a-47f0eeb669c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42fecedc-0c14-4e16-89c5-2f8a292cfa0f' class='xr-var-data-in' type='checkbox'><label for='data-42fecedc-0c14-4e16-89c5-2f8a292cfa0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>SCM ideal surface sensible heat flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LH_FORCE</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-030acb6c-b610-427c-8770-62463f6b1344' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-030acb6c-b610-427c-8770-62463f6b1344' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00d92932-891c-40b4-8760-0b649806bb34' class='xr-var-data-in' type='checkbox'><label for='data-00d92932-891c-40b4-8760-0b649806bb34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>SCM ideal surface latent heat flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSK_FORCE</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-93c9e094-7212-4f91-a9f8-3d9a310d2988' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93c9e094-7212-4f91-a9f8-3d9a310d2988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91936542-4a70-4e68-aafc-784a337fc6f9' class='xr-var-data-in' type='checkbox'><label for='data-91936542-4a70-4e68-aafc-784a337fc6f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>SCM ideal surface skin temperature</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HFX_FORCE_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-8905de28-928e-43c0-b442-30535caf84ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8905de28-928e-43c0-b442-30535caf84ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad1ea624-0c00-4277-ab81-8d657a1f5ee2' class='xr-var-data-in' type='checkbox'><label for='data-ad1ea624-0c00-4277-ab81-8d657a1f5ee2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>SCM ideal surface sensible heat flux tendency</dd><dt><span>units :</span></dt><dd>W m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LH_FORCE_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-1edf344b-fa61-4068-8ed8-7577769cc9cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1edf344b-fa61-4068-8ed8-7577769cc9cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2518eed-a93b-4ed1-a073-0dec3ed8e8a9' class='xr-var-data-in' type='checkbox'><label for='data-d2518eed-a93b-4ed1-a073-0dec3ed8e8a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>SCM ideal surface latent heat flux tendency</dd><dt><span>units :</span></dt><dd>W m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSK_FORCE_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-b7c67367-2220-4d2f-8756-9164eaba695c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7c67367-2220-4d2f-8756-9164eaba695c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-233ab5c1-491f-4980-a6e8-23a7b970365e' class='xr-var-data-in' type='checkbox'><label for='data-233ab5c1-491f-4980-a6e8-23a7b970365e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>SCM ideal surface skin temperature tendency</dd><dt><span>units :</span></dt><dd>W m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "                    <tr>\n",
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+       "                    <tr>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MUB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-bf673c96-4232-4031-9e1a-6165f9865bcb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf673c96-4232-4031-9e1a-6165f9865bcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0e8ba25-1a5b-4bce-9bb4-5ea2f9e426e9' class='xr-var-data-in' type='checkbox'><label for='data-e0e8ba25-1a5b-4bce-9bb4-5ea2f9e426e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>base state dry air mass in column</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NEST_POS</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-37fdeb4a-cc81-43f8-b95a-81f468a91836' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37fdeb4a-cc81-43f8-b95a-81f468a91836' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-797050e2-b39b-4f87-bc4f-218bd319492a' class='xr-var-data-in' type='checkbox'><label for='data-797050e2-b39b-4f87-bc4f-218bd319492a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>-</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TKE</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-ae0b5c59-0d70-47a1-b598-9178756a3adf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae0b5c59-0d70-47a1-b598-9178756a3adf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ab47569-928c-4d71-a1af-df9343aa5c58' class='xr-var-data-in' type='checkbox'><label for='data-7ab47569-928c-4d71-a1af-df9343aa5c58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>TURBULENCE KINETIC ENERGY</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALT</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-faa03d51-c28c-4b6d-9678-3bde8dd0ea78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-faa03d51-c28c-4b6d-9678-3bde8dd0ea78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2b371f6-b9c4-4c0c-b0f2-9585e4dbb67e' class='xr-var-data-in' type='checkbox'><label for='data-d2b371f6-b9c4-4c0c-b0f2-9585e4dbb67e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>inverse density</dd><dt><span>units :</span></dt><dd>m3 kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FNM</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-d61c2f47-cc7b-45ec-8392-9abbb0f7a4ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d61c2f47-cc7b-45ec-8392-9abbb0f7a4ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d86c4de-b6cd-4ad0-a902-ba947496e763' class='xr-var-data-in' type='checkbox'><label for='data-2d86c4de-b6cd-4ad0-a902-ba947496e763' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>upper weight for vertical stretching</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
+       "  <text x=\"140.000000\" y=\"24.159292\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,24.159292)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FNP</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-4edaa2bd-f48e-4b2b-9b99-39096be41e81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4edaa2bd-f48e-4b2b-9b99-39096be41e81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-682ecc8f-ab92-4233-8219-053065291c71' class='xr-var-data-in' type='checkbox'><label for='data-682ecc8f-ab92-4233-8219-053065291c71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>lower weight for vertical stretching</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RDNW</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-dd4a9055-0c68-4227-a042-44711dff026d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd4a9055-0c68-4227-a042-44711dff026d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0de0fc98-2658-44b6-86b6-b2b07c6cbbbc' class='xr-var-data-in' type='checkbox'><label for='data-0de0fc98-2658-44b6-86b6-b2b07c6cbbbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>inverse d(eta) values between full (w) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
+       "  <text x=\"140.000000\" y=\"24.159292\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,24.159292)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RDN</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-8b4749ce-bec5-4916-a19b-330ada589653' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b4749ce-bec5-4916-a19b-330ada589653' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-936ed1d8-595b-477b-b69a-0bf8021bc158' class='xr-var-data-in' type='checkbox'><label for='data-936ed1d8-595b-477b-b69a-0bf8021bc158' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>inverse d(eta) values between half (mass) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
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+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DNW</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-9e73a5f6-b051-4155-a499-c7244b3679be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e73a5f6-b051-4155-a499-c7244b3679be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f69469c5-7eb5-4a3b-a44a-5fed6d04a79e' class='xr-var-data-in' type='checkbox'><label for='data-f69469c5-7eb5-4a3b-a44a-5fed6d04a79e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>d(eta) values between full (w) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
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+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
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+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
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+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DN</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-797f1c75-c2da-48bb-a949-f41c9f84a6a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-797f1c75-c2da-48bb-a949-f41c9f84a6a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e15bb267-dc38-4777-9a63-1864f951b14b' class='xr-var-data-in' type='checkbox'><label for='data-e15bb267-dc38-4777-9a63-1864f951b14b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>d(eta) values between half (mass) levels</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CFN</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-9dc56eef-d38b-46a5-8f08-13ecf7495356' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9dc56eef-d38b-46a5-8f08-13ecf7495356' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70b94919-8a7d-45ae-8c5f-38945c5604d8' class='xr-var-data-in' type='checkbox'><label for='data-70b94919-8a7d-45ae-8c5f-38945c5604d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>extrapolation constant</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CFN1</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-5ff6835d-6658-4b0c-b6d1-3fa5ac200746' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ff6835d-6658-4b0c-b6d1-3fa5ac200746' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31baa890-ce40-482b-baf5-77e4cce7c674' class='xr-var-data-in' type='checkbox'><label for='data-31baa890-ce40-482b-baf5-77e4cce7c674' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>extrapolation constant</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THIS_IS_AN_IDEAL_RUN</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-f1b0f086-8dc2-4bfe-ad08-1770e8344054' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1b0f086-8dc2-4bfe-ad08-1770e8344054' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e22d6f28-2d11-48f9-ad3d-1fe302f14648' class='xr-var-data-in' type='checkbox'><label for='data-e22d6f28-2d11-48f9-ad3d-1fe302f14648' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>T/F flag: this is an ARW ideal simulation</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P_HYD</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-7315591a-8b7a-415a-8570-09c441964fb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7315591a-8b7a-415a-8570-09c441964fb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b995e359-d1f2-4b3d-afd0-cd4de0953b6c' class='xr-var-data-in' type='checkbox'><label for='data-b995e359-d1f2-4b3d-afd0-cd4de0953b6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>hydrostatic pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Q2</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-13def6b2-721b-411c-aa4d-cd3bea49ab0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13def6b2-721b-411c-aa4d-cd3bea49ab0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69babf27-8c2a-4c51-b01c-168f69dd5210' class='xr-var-data-in' type='checkbox'><label for='data-69babf27-8c2a-4c51-b01c-168f69dd5210' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>QV at 2 M</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>humidity_mixing_ratio</dd><dt><span>long_name :</span></dt><dd>humidity_mixing_ratio_at_2m_agl</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T2</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e1882f14-d9cf-4103-83c9-0bda14d86e97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1882f14-d9cf-4103-83c9-0bda14d86e97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25c50a02-dde3-42cd-a15a-22f793684f60' class='xr-var-data-in' type='checkbox'><label for='data-25c50a02-dde3-42cd-a15a-22f793684f60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>TEMP at 2 M</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>air_temperature_at_2m_agl</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TH2</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-18f69bae-ab56-4e08-84b3-c4fafa75461b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18f69bae-ab56-4e08-84b3-c4fafa75461b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52af06da-2de9-4783-8f14-0c39b44ce170' class='xr-var-data-in' type='checkbox'><label for='data-52af06da-2de9-4783-8f14-0c39b44ce170' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>POT TEMP at 2 M</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>air_potential_temperature</dd><dt><span>long_name :</span></dt><dd>air_potential_temperature_at_2m_agl</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PSFC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-6dad5c7f-196d-41e8-8d30-9b52c1779992' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6dad5c7f-196d-41e8-8d30-9b52c1779992' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e442e5bb-53ef-4495-97d3-813a2ea34543' class='xr-var-data-in' type='checkbox'><label for='data-e442e5bb-53ef-4495-97d3-813a2ea34543' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SFC PRESSURE</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>air_pressure_at_surface</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U10</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-856bb84f-c429-4b91-94f5-1cb7de2a5b65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-856bb84f-c429-4b91-94f5-1cb7de2a5b65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38443efa-c5a3-4c7f-98b4-db8aaab84802' class='xr-var-data-in' type='checkbox'><label for='data-38443efa-c5a3-4c7f-98b4-db8aaab84802' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>U at 10 M</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>x_wind</dd><dt><span>long_name :</span></dt><dd>x_wind_at_10m_agl</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V10</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-3670f8fd-6ffb-4263-87af-9ac57164560a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3670f8fd-6ffb-4263-87af-9ac57164560a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdd9208f-3f8c-4bdb-ada2-d4f69c717f4c' class='xr-var-data-in' type='checkbox'><label for='data-fdd9208f-3f8c-4bdb-ada2-d4f69c717f4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>V at 10 M</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>y_wind</dd><dt><span>long_name :</span></dt><dd>y_wind_at_10m_agl</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "  <line x1=\"155\" y1=\"25\" x2=\"155\" y2=\"145\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RDX</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-d0288867-28bc-4ef7-951b-5036b4b1869b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0288867-28bc-4ef7-951b-5036b4b1869b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-700291d2-04ba-4d72-876c-1819ee39d014' class='xr-var-data-in' type='checkbox'><label for='data-700291d2-04ba-4d72-876c-1819ee39d014' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>INVERSE X GRID LENGTH</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RDY</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-01644709-e6a4-4ba9-a535-29b1772152f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01644709-e6a4-4ba9-a535-29b1772152f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f79f4284-4260-43ae-9c0d-c531d9e38bc9' class='xr-var-data-in' type='checkbox'><label for='data-f79f4284-4260-43ae-9c0d-c531d9e38bc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>INVERSE Y GRID LENGTH</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RESM</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-8ce1e5ec-f483-4371-bb88-916df7110f19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ce1e5ec-f483-4371-bb88-916df7110f19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e6f17d1-34b3-4f24-9be8-b2fc9759b20f' class='xr-var-data-in' type='checkbox'><label for='data-0e6f17d1-34b3-4f24-9be8-b2fc9759b20f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TIME WEIGHT CONSTANT FOR SMALL STEPS</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZETATOP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-153d78c7-40d3-4bac-aec5-31017e838266' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-153d78c7-40d3-4bac-aec5-31017e838266' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a97e713c-906b-447a-8fbf-b057c46b37e1' class='xr-var-data-in' type='checkbox'><label for='data-a97e713c-906b-447a-8fbf-b057c46b37e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>ZETA AT MODEL TOP</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CF1</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-f010c648-0abc-4104-8450-385ca4d1317a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f010c648-0abc-4104-8450-385ca4d1317a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9f7268a-a185-4caa-b78b-f842ee508053' class='xr-var-data-in' type='checkbox'><label for='data-b9f7268a-a185-4caa-b78b-f842ee508053' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>2nd order extrapolation constant</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
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+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CF2</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-c34ec0a6-e6ac-4efd-a083-1a316bb5883b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c34ec0a6-e6ac-4efd-a083-1a316bb5883b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5385eaa5-4c47-4f44-bcdc-3da4a044f670' class='xr-var-data-in' type='checkbox'><label for='data-5385eaa5-4c47-4f44-bcdc-3da4a044f670' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>2nd order extrapolation constant</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
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+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CF3</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-45439e26-dfa8-4106-9f03-6e8828e1a401' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45439e26-dfa8-4106-9f03-6e8828e1a401' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f57e81e7-859c-40c7-91da-a709cda87e3b' class='xr-var-data-in' type='checkbox'><label for='data-f57e81e7-859c-40c7-91da-a709cda87e3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>2nd order extrapolation constant</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ITIMESTEP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-b41f1abf-8eb8-4f6b-b04b-1f0f1d7dfb2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b41f1abf-8eb8-4f6b-b04b-1f0f1d7dfb2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-591d52a4-79b9-4a9e-b0de-16022c239a49' class='xr-var-data-in' type='checkbox'><label for='data-591d52a4-79b9-4a9e-b0de-16022c239a49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd></dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QVAPOR</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e19f896e-5939-41b6-89a6-00b6e1ce8f5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e19f896e-5939-41b6-89a6-00b6e1ce8f5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fbff558-0b91-442a-871d-4b467bfa10db' class='xr-var-data-in' type='checkbox'><label for='data-2fbff558-0b91-442a-871d-4b467bfa10db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Water vapor mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>humidity_mixing_ratio</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QCLOUD</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a0ff78f8-5235-4b19-a453-101249e0cff4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0ff78f8-5235-4b19-a453-101249e0cff4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-455e7fa6-71f7-4b44-9d95-3a65ea934211' class='xr-var-data-in' type='checkbox'><label for='data-455e7fa6-71f7-4b44-9d95-3a65ea934211' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Cloud water mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QRAIN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b46bb4fb-d7ba-4685-bbb4-0106c8e7c6ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b46bb4fb-d7ba-4685-bbb4-0106c8e7c6ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-341046db-6a80-4f11-9101-00bfb05a7cf7' class='xr-var-data-in' type='checkbox'><label for='data-341046db-6a80-4f11-9101-00bfb05a7cf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Rain water mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QICE</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-919f20e8-1d83-43e7-b5b0-661d111fb490' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-919f20e8-1d83-43e7-b5b0-661d111fb490' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10298004-013b-4936-874e-e04d984608d1' class='xr-var-data-in' type='checkbox'><label for='data-10298004-013b-4936-874e-e04d984608d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Ice mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QSNOW</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f78b5d11-cdaf-4455-ad2f-b3aad4112b19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f78b5d11-cdaf-4455-ad2f-b3aad4112b19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-770816f7-7af8-4b94-8eb1-422fd76d9fbb' class='xr-var-data-in' type='checkbox'><label for='data-770816f7-7af8-4b94-8eb1-422fd76d9fbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Snow mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QGRAUP</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4491a8de-cc77-40f6-a52b-f40cb12e895e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4491a8de-cc77-40f6-a52b-f40cb12e895e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ae77f6d-270e-4ec3-9c35-e0eece8c4239' class='xr-var-data-in' type='checkbox'><label for='data-4ae77f6d-270e-4ec3-9c35-e0eece8c4239' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Graupel mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QNICE</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-854235e9-d113-4dae-83c9-04ab453ab122' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-854235e9-d113-4dae-83c9-04ab453ab122' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9044d98-8849-42ab-b1ef-890660250e63' class='xr-var-data-in' type='checkbox'><label for='data-a9044d98-8849-42ab-b1ef-890660250e63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Ice Number concentration</dd><dt><span>units :</span></dt><dd>  kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QNRAIN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-75cbccb2-3e15-482f-8b9b-19b230cbcd31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75cbccb2-3e15-482f-8b9b-19b230cbcd31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-849ace07-8b0b-4c84-9925-e0279552780c' class='xr-var-data-in' type='checkbox'><label for='data-849ace07-8b0b-4c84-9925-e0279552780c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Rain Number concentration</dd><dt><span>units :</span></dt><dd>kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHDMAX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-716cb1b0-eb7b-4a87-956b-c7121decb43d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-716cb1b0-eb7b-4a87-956b-c7121decb43d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42713c14-d571-411f-a310-c5a5264dc0df' class='xr-var-data-in' type='checkbox'><label for='data-42713c14-d571-411f-a310-c5a5264dc0df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ANNUAL MAX VEG FRACTION</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHDMIN</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4143ebc2-0cb3-405c-a99f-5f00279da2da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4143ebc2-0cb3-405c-a99f-5f00279da2da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62bccecc-7f20-4963-ac99-71507cab5f75' class='xr-var-data-in' type='checkbox'><label for='data-62bccecc-7f20-4963-ac99-71507cab5f75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ANNUAL MIN VEG FRACTION</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOALB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e4877b0b-7f60-421a-a839-14ccd621d6f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4877b0b-7f60-421a-a839-14ccd621d6f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9de0d641-bddd-47ab-900f-2891bfe724e2' class='xr-var-data-in' type='checkbox'><label for='data-9de0d641-bddd-47ab-900f-2891bfe724e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ANNUAL MAX SNOW ALBEDO IN FRACTION</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSLB</span></div><div class='xr-var-dims'>(Time, soil_layers_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a7e1ce2a-18fc-4779-be98-98b72b2694ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7e1ce2a-18fc-4779-be98-98b72b2694ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44327ae5-8b71-4c36-906e-3b6b8b9656df' class='xr-var-data-in' type='checkbox'><label for='data-44327ae5-8b71-4c36-906e-3b6b8b9656df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>SOIL TEMPERATURE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 108.48 MiB </td>\n",
+       "                        <td> 305.18 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 5, 250, 250) </td>\n",
+       "                        <td> (1, 5, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SMOIS</span></div><div class='xr-var-dims'>(Time, soil_layers_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-dc39b04e-1a03-44e8-ab20-7387ffb8bc24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc39b04e-1a03-44e8-ab20-7387ffb8bc24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb6c77e3-2c97-40ea-9157-7a46100aef2a' class='xr-var-data-in' type='checkbox'><label for='data-fb6c77e3-2c97-40ea-9157-7a46100aef2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>SOIL MOISTURE</dd><dt><span>units :</span></dt><dd>m3 m-3</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 108.48 MiB </td>\n",
+       "                        <td> 305.18 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 5, 250, 250) </td>\n",
+       "                        <td> (1, 5, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SH2O</span></div><div class='xr-var-dims'>(Time, soil_layers_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-855efe11-5c4e-4081-99b8-55876c6d204a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-855efe11-5c4e-4081-99b8-55876c6d204a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-906c60c3-2c2e-415d-a152-f8e75666abd0' class='xr-var-data-in' type='checkbox'><label for='data-906c60c3-2c2e-415d-a152-f8e75666abd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>SOIL LIQUID WATER</dd><dt><span>units :</span></dt><dd>m3 m-3</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 108.48 MiB </td>\n",
+       "                        <td> 305.18 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 5, 250, 250) </td>\n",
+       "                        <td> (1, 5, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SEAICE</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-42f30968-0884-42c6-9c35-8555f8495a74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42f30968-0884-42c6-9c35-8555f8495a74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bba6c20-d583-49ad-a413-5b12c0e98455' class='xr-var-data-in' type='checkbox'><label for='data-8bba6c20-d583-49ad-a413-5b12c0e98455' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SEA ICE FLAG</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XICEM</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-5993588f-449d-46e3-976c-35c3e8c17ceb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5993588f-449d-46e3-976c-35c3e8c17ceb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d55f60c-e68b-4691-bd68-87e24403c1fe' class='xr-var-data-in' type='checkbox'><label for='data-0d55f60c-e68b-4691-bd68-87e24403c1fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SEA ICE FLAG (PREVIOUS STEP)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SFROFF</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-36a6bd1c-f2f7-45a2-8b0d-f4eea1cca6e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36a6bd1c-f2f7-45a2-8b0d-f4eea1cca6e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4220e151-7322-4042-8a7a-b595269ac927' class='xr-var-data-in' type='checkbox'><label for='data-4220e151-7322-4042-8a7a-b595269ac927' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SURFACE RUNOFF</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UDROFF</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-fb99c9d9-aaa2-4502-b5c4-13a85fdbed02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb99c9d9-aaa2-4502-b5c4-13a85fdbed02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-180ec320-5d16-4992-91a0-677d739ca632' class='xr-var-data-in' type='checkbox'><label for='data-180ec320-5d16-4992-91a0-677d739ca632' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>UNDERGROUND RUNOFF</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>IVGTYP</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-5737f442-c2a7-4deb-be24-3da1e9cfdc5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5737f442-c2a7-4deb-be24-3da1e9cfdc5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da2eac22-fbc6-4e10-8e40-b61760f17284' class='xr-var-data-in' type='checkbox'><label for='data-da2eac22-fbc6-4e10-8e40-b61760f17284' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>DOMINANT VEGETATION CATEGORY</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ISLTYP</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1cad957d-afed-4d34-9f82-5355ba804108' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cad957d-afed-4d34-9f82-5355ba804108' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-121ebf46-b42a-4d83-ab4e-6d573288a203' class='xr-var-data-in' type='checkbox'><label for='data-121ebf46-b42a-4d83-ab4e-6d573288a203' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>DOMINANT SOIL CATEGORY</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VEGFRA</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f3c4a8a0-6430-4b97-81a9-e0b7ab22b2f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3c4a8a0-6430-4b97-81a9-e0b7ab22b2f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ce196d8-70ad-45e7-adf1-ade0730a1f90' class='xr-var-data-in' type='checkbox'><label for='data-7ce196d8-70ad-45e7-adf1-ade0730a1f90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>VEGETATION FRACTION</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>vegetation_area_fraction</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GRDFLX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-675251df-a8b4-4034-9a88-ec0a284da461' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-675251df-a8b4-4034-9a88-ec0a284da461' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb8eba8e-fcfd-4f33-88b8-174ca798c5c2' class='xr-var-data-in' type='checkbox'><label for='data-fb8eba8e-fcfd-4f33-88b8-174ca798c5c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>GROUND HEAT FLUX</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACGRDFLX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e624829b-ed45-407b-b6c7-bf4af77f22a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e624829b-ed45-407b-b6c7-bf4af77f22a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52aa8926-1d52-42db-90ec-f45cdc3c6ad2' class='xr-var-data-in' type='checkbox'><label for='data-52aa8926-1d52-42db-90ec-f45cdc3c6ad2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED GROUND HEAT FLUX</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSNOM</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1ce3d65b-cc02-48d2-887d-a5dda999a821' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ce3d65b-cc02-48d2-887d-a5dda999a821' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05be856d-af29-4671-8b45-c2fe75e1d24d' class='xr-var-data-in' type='checkbox'><label for='data-05be856d-af29-4671-8b45-c2fe75e1d24d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED MELTED SNOW</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOW</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f6be3e33-e219-408f-90e8-e89dc1c018bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6be3e33-e219-408f-90e8-e89dc1c018bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f97893a3-a423-43de-90be-c57f7747365d' class='xr-var-data-in' type='checkbox'><label for='data-f97893a3-a423-43de-90be-c57f7747365d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SNOW WATER EQUIVALENT</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOWH</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-73b01b44-d725-42f5-b860-694c7014d97b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73b01b44-d725-42f5-b860-694c7014d97b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0fb7c1b-0a71-41e5-b819-c319eb556961' class='xr-var-data-in' type='checkbox'><label for='data-b0fb7c1b-0a71-41e5-b819-c319eb556961' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>PHYSICAL SNOW DEPTH</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CANWAT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-44d7c9f8-5bea-41e5-8d0f-b182739bdc9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44d7c9f8-5bea-41e5-8d0f-b182739bdc9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d96eb6a-dde9-42c5-8298-999effd5c258' class='xr-var-data-in' type='checkbox'><label for='data-7d96eb6a-dde9-42c5-8298-999effd5c258' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>CANOPY WATER</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSTSK</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-5fc7cb7e-0fdf-4e97-be53-1a9e9636ff97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5fc7cb7e-0fdf-4e97-be53-1a9e9636ff97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0198c9f3-6d2a-4584-9e72-7718ebcbf26a' class='xr-var-data-in' type='checkbox'><label for='data-0198c9f3-6d2a-4584-9e72-7718ebcbf26a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SKIN SEA SURFACE TEMPERATURE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>COSZEN</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-731b91f4-0f25-43b6-8fd3-9d05f7fd1f11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-731b91f4-0f25-43b6-8fd3-9d05f7fd1f11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac56440f-7612-4a79-8982-df57e3bdffbe' class='xr-var-data-in' type='checkbox'><label for='data-ac56440f-7612-4a79-8982-df57e3bdffbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>COS of SOLAR ZENITH ANGLE</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LAI</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a7cbe39b-31c6-45c6-b62a-c67f97452bd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7cbe39b-31c6-45c6-b62a-c67f97452bd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc5c488b-b050-42e3-9480-4e2b055b449a' class='xr-var-data-in' type='checkbox'><label for='data-dc5c488b-b050-42e3-9480-4e2b055b449a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LEAF AREA INDEX</dd><dt><span>units :</span></dt><dd>m-2/m-2</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>leaf_area_index</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "            </table>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VAR</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-bf651424-4516-406b-9c52-d84c5fba30d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf651424-4516-406b-9c52-d84c5fba30d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88c5f873-9647-40ab-b54a-04b45bcb6334' class='xr-var-data-in' type='checkbox'><label for='data-88c5f873-9647-40ab-b54a-04b45bcb6334' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>OROGRAPHIC VARIANCE</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_M</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e3d6f214-0eb5-415c-9de7-9d2f71dd280a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3d6f214-0eb5-415c-9de7-9d2f71dd280a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e2f5a47-af72-4029-ad1b-c3b56d58d0ca' class='xr-var-data-in' type='checkbox'><label for='data-2e2f5a47-af72-4029-ad1b-c3b56d58d0ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on mass grid</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_U</span></div><div class='xr-var-dims'>(Time, y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 126), meta=np.ndarray&gt;</div><input id='attrs-e27b2a11-d871-4224-bda2-b3c73e1f7dc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e27b2a11-d871-4224-bda2-b3c73e1f7dc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91c5f9df-d809-4aee-9e93-3a0ff97a8350' class='xr-var-data-in' type='checkbox'><label for='data-91c5f9df-d809-4aee-9e93-3a0ff97a8350' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on u-grid</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 251) </td>\n",
+       "                        <td> (1, 125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_V</span></div><div class='xr-var-dims'>(Time, y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 126, 125), meta=np.ndarray&gt;</div><input id='attrs-cd7d51d2-82b8-494c-81a1-690e4b5dd8c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd7d51d2-82b8-494c-81a1-690e4b5dd8c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-456e4f46-73ce-4e71-9cc3-9488e5b2c2d3' class='xr-var-data-in' type='checkbox'><label for='data-456e4f46-73ce-4e71-9cc3-9488e5b2c2d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on v-grid</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 251, 250) </td>\n",
+       "                        <td> (1, 126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_MX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-ad6f9daf-587f-41af-bfee-ce296e10b38c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad6f9daf-587f-41af-bfee-ce296e10b38c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19de5704-9eaf-4e98-9bc9-0adc47b0c71d' class='xr-var-data-in' type='checkbox'><label for='data-19de5704-9eaf-4e98-9bc9-0adc47b0c71d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on mass grid, x direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_MY</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e4d28e9c-a7a6-4afc-88da-f10b6ac4d074' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4d28e9c-a7a6-4afc-88da-f10b6ac4d074' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fa9bab1-981f-439e-951f-105c4fcde4da' class='xr-var-data-in' type='checkbox'><label for='data-8fa9bab1-981f-439e-951f-105c4fcde4da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on mass grid, y direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_UX</span></div><div class='xr-var-dims'>(Time, y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 126), meta=np.ndarray&gt;</div><input id='attrs-6a9572e1-13c1-434e-b2ed-1edd4e75c588' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a9572e1-13c1-434e-b2ed-1edd4e75c588' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-669ba1c0-4b3d-428c-b81c-1755aa167830' class='xr-var-data-in' type='checkbox'><label for='data-669ba1c0-4b3d-428c-b81c-1755aa167830' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on u-grid, x direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_UY</span></div><div class='xr-var-dims'>(Time, y, x_stag)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 126), meta=np.ndarray&gt;</div><input id='attrs-4018f6b1-c2a3-4430-918c-b2c8668998ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4018f6b1-c2a3-4430-918c-b2c8668998ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-261608bb-8b86-4c3a-bb50-754a871ade8c' class='xr-var-data-in' type='checkbox'><label for='data-261608bb-8b86-4c3a-bb50-754a871ade8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on u-grid, y direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 251) </td>\n",
+       "                        <td> (1, 125, 126) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_VX</span></div><div class='xr-var-dims'>(Time, y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 126, 125), meta=np.ndarray&gt;</div><input id='attrs-aa0e2d7e-1802-4a61-a063-62b797db670f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa0e2d7e-1802-4a61-a063-62b797db670f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13f11698-218b-47fa-adf7-0677b4ab18d0' class='xr-var-data-in' type='checkbox'><label for='data-13f11698-218b-47fa-adf7-0677b4ab18d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on v-grid, x direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 251, 250) </td>\n",
+       "                        <td> (1, 126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MF_VX_INV</span></div><div class='xr-var-dims'>(Time, y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 126, 125), meta=np.ndarray&gt;</div><input id='attrs-d91e93cd-0e1c-45a6-8854-b84c87ec170d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d91e93cd-0e1c-45a6-8854-b84c87ec170d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a361207e-c9b2-4419-90c9-a421529f0947' class='xr-var-data-in' type='checkbox'><label for='data-a361207e-c9b2-4419-90c9-a421529f0947' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Inverse map scale factor on v-grid, x direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 251, 250) </td>\n",
+       "                        <td> (1, 126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPFAC_VY</span></div><div class='xr-var-dims'>(Time, y_stag, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 126, 125), meta=np.ndarray&gt;</div><input id='attrs-10e34994-252e-4cfe-9946-cefbd3d5f2b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10e34994-252e-4cfe-9946-cefbd3d5f2b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fe8e002-745b-4c83-b5b7-6ea0542068f4' class='xr-var-data-in' type='checkbox'><label for='data-2fe8e002-745b-4c83-b5b7-6ea0542068f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Map scale factor on v-grid, y direction</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.78 MiB </td>\n",
+       "                        <td> 61.52 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 251, 250) </td>\n",
+       "                        <td> (1, 126, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>F</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-401b56b7-52f9-43be-8ee9-55e95b5c3709' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-401b56b7-52f9-43be-8ee9-55e95b5c3709' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4538577-7dbd-4058-b446-e9fff6b8b46f' class='xr-var-data-in' type='checkbox'><label for='data-b4538577-7dbd-4058-b446-e9fff6b8b46f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Coriolis sine latitude term</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>E</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f097f332-1f2e-47e1-ab50-895c2e815570' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f097f332-1f2e-47e1-ab50-895c2e815570' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-572b0840-d567-4f62-bb82-e39bf204536c' class='xr-var-data-in' type='checkbox'><label for='data-572b0840-d567-4f62-bb82-e39bf204536c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Coriolis cosine latitude term</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SINALPHA</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b5358768-cd13-4f4f-a654-9d1bd2ce5a78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5358768-cd13-4f4f-a654-9d1bd2ce5a78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95090ee6-077b-477c-9b07-2c00c5b52503' class='xr-var-data-in' type='checkbox'><label for='data-95090ee6-077b-477c-9b07-2c00c5b52503' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Local sine of map rotation</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>COSALPHA</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f1efaf26-9e21-4778-a264-63b4ed589e12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1efaf26-9e21-4778-a264-63b4ed589e12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc78ed95-6860-4b9a-8751-f36d17215022' class='xr-var-data-in' type='checkbox'><label for='data-dc78ed95-6860-4b9a-8751-f36d17215022' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Local cosine of map rotation</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HGT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-db88770c-ab27-4b14-b75d-ef55cdaace1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db88770c-ab27-4b14-b75d-ef55cdaace1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bae5593b-493a-49ec-bdf2-3457eaa8fd18' class='xr-var-data-in' type='checkbox'><label for='data-bae5593b-493a-49ec-bdf2-3457eaa8fd18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Terrain Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>surface_altitude</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TSK</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-50c05875-29e4-48fc-85ab-8b4e7fe1654f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50c05875-29e4-48fc-85ab-8b4e7fe1654f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff3768f6-f4dc-45ff-abc5-03c81d355188' class='xr-var-data-in' type='checkbox'><label for='data-ff3768f6-f4dc-45ff-abc5-03c81d355188' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SURFACE SKIN TEMPERATURE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P_TOP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-dda10b64-350b-4c05-82c0-71c0b3aba429' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dda10b64-350b-4c05-82c0-71c0b3aba429' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c6229f0-88c0-4404-94d0-63e4cedb61e7' class='xr-var-data-in' type='checkbox'><label for='data-4c6229f0-88c0-4404-94d0-63e4cedb61e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>PRESSURE TOP OF THE MODEL</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T00</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-0e8564ee-07c2-435e-9a2b-6b8a2b2fe044' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e8564ee-07c2-435e-9a2b-6b8a2b2fe044' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63d3d975-8035-4418-ac52-7943a00e6297' class='xr-var-data-in' type='checkbox'><label for='data-63d3d975-8035-4418-ac52-7943a00e6297' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>BASE STATE TEMPERATURE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P00</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-659dd7f4-266f-42e3-8177-937b7d02021d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-659dd7f4-266f-42e3-8177-937b7d02021d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ed9dadf-87df-477c-9db3-bfbe63a6a11d' class='xr-var-data-in' type='checkbox'><label for='data-0ed9dadf-87df-477c-9db3-bfbe63a6a11d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>BASE STATE PRESURE</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-4bf55059-8e6e-4f27-8300-a3f32b965dc5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bf55059-8e6e-4f27-8300-a3f32b965dc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0de138be-0bc9-41f2-8354-7a9dfc4cb8e5' class='xr-var-data-in' type='checkbox'><label for='data-0de138be-0bc9-41f2-8354-7a9dfc4cb8e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>BASE STATE LAPSE RATE</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TISO</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-a280d378-39f4-4263-a744-de65b5048cb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a280d378-39f4-4263-a744-de65b5048cb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59ce734a-928e-498f-8410-28a0e568fa68' class='xr-var-data-in' type='checkbox'><label for='data-59ce734a-928e-498f-8410-28a0e568fa68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>TEMP AT WHICH THE BASE T TURNS CONST</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLP_STRAT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-b2dad09e-8179-4bf9-bfb1-bdeccec7b28a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2dad09e-8179-4bf9-bfb1-bdeccec7b28a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cfb00ec-c954-4bb2-b2a1-ada8e385ad07' class='xr-var-data-in' type='checkbox'><label for='data-6cfb00ec-c954-4bb2-b2a1-ada8e385ad07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>BASE STATE LAPSE RATE (DT/D(LN(P)) IN STRATOSPHERE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P_STRAT</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-e6687105-4ca2-4c13-962c-276b5b246538' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6687105-4ca2-4c13-962c-276b5b246538' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-593564d6-5b19-4193-89da-cd41d5c0dd95' class='xr-var-data-in' type='checkbox'><label for='data-593564d6-5b19-4193-89da-cd41d5c0dd95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>BASE STATE PRESSURE AT BOTTOM OF STRATOSPHERE</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAX_MSTFX</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-9b87678f-3e01-499b-b417-53bb42ef5e30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b87678f-3e01-499b-b417-53bb42ef5e30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba389d2a-ec51-46ac-85b6-9a3d84170983' class='xr-var-data-in' type='checkbox'><label for='data-ba389d2a-ec51-46ac-85b6-9a3d84170983' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Max map factor in domain</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAX_MSTFY</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-4133c817-9e02-4bcd-b666-28a6783a4bcb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4133c817-9e02-4bcd-b666-28a6783a4bcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac781e8b-9235-4ce7-8685-ef2b272aef20' class='xr-var-data-in' type='checkbox'><label for='data-ac781e8b-9235-4ce7-8685-ef2b272aef20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Max map factor in domain</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAINC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-dde07103-25b6-44c9-ba59-91866526e006' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dde07103-25b6-44c9-ba59-91866526e006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34c264fe-7ecc-4411-b3df-ebfaf907f9ec' class='xr-var-data-in' type='checkbox'><label for='data-34c264fe-7ecc-4411-b3df-ebfaf907f9ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL CUMULUS PRECIPITATION</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAINSH</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-6e23bedf-3b56-4d2f-a9fe-753046f1a8c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e23bedf-3b56-4d2f-a9fe-753046f1a8c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77c45127-5ab9-47fd-a471-ee7e2ee3ff7b' class='xr-var-data-in' type='checkbox'><label for='data-77c45127-5ab9-47fd-a471-ee7e2ee3ff7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED SHALLOW CUMULUS PRECIPITATION</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAINNC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-ea485c04-c278-42d6-82a0-c7751f2a3646' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea485c04-c278-42d6-82a0-c7751f2a3646' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ace475e6-088e-4be1-a2c0-d24f83184f0d' class='xr-var-data-in' type='checkbox'><label for='data-ace475e6-088e-4be1-a2c0-d24f83184f0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE PRECIPITATION</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>integral_of_lwe_precipitation_rate_wrt_time</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOWNC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-10c28b32-8f6e-4ba6-81f5-87581a91b797' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10c28b32-8f6e-4ba6-81f5-87581a91b797' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c3ba4a0-0f7f-43e5-abc3-c38ae2c5344d' class='xr-var-data-in' type='checkbox'><label for='data-0c3ba4a0-0f7f-43e5-abc3-c38ae2c5344d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE SNOW AND ICE</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GRAUPELNC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-5719bcf1-3643-4bcd-b202-e1cd11f7612a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5719bcf1-3643-4bcd-b202-e1cd11f7612a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d86bb96a-1367-4381-a1b9-ff31fcfe7a7a' class='xr-var-data-in' type='checkbox'><label for='data-d86bb96a-1367-4381-a1b9-ff31fcfe7a7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE GRAUPEL</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HAILNC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b6ea48c9-dff6-4087-b64e-0a0861de832f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6ea48c9-dff6-4087-b64e-0a0861de832f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a3e03b4-90da-4ed9-b6cb-1cb9c2d62403' class='xr-var-data-in' type='checkbox'><label for='data-5a3e03b4-90da-4ed9-b6cb-1cb9c2d62403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE HAIL</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CLDFRA</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-bad72cc4-0179-40f5-b1ea-8a9e2fcc5215' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bad72cc4-0179-40f5-b1ea-8a9e2fcc5215' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb5526be-2127-4e3a-bf9a-15fdb03987f3' class='xr-var-data-in' type='checkbox'><label for='data-bb5526be-2127-4e3a-bf9a-15fdb03987f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>CLOUD FRACTION</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction_in_atmosphere_layer</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                <thead>\n",
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+       "                        <th> Array </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDOWN</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-2446318d-a0e7-4a1e-87e5-389888186a2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2446318d-a0e7-4a1e-87e5-389888186a2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a3140a6-683a-43c8-ba14-b308fc9a2836' class='xr-var-data-in' type='checkbox'><label for='data-6a3140a6-683a-43c8-ba14-b308fc9a2836' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>DOWNWARD SHORT WAVE FLUX AT GROUND SURFACE</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GLW</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-3c1a29a1-9ece-4b20-8602-bba6bea327e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c1a29a1-9ece-4b20-8602-bba6bea327e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40175703-695b-4d2e-bce0-9a5e06c59675' class='xr-var-data-in' type='checkbox'><label for='data-40175703-695b-4d2e-bce0-9a5e06c59675' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>DOWNWARD LONG WAVE FLUX AT GROUND SURFACE</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWNORM</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-00115ad8-0277-44a8-af4f-b4dc2bbd4cf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00115ad8-0277-44a8-af4f-b4dc2bbd4cf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e11834f-5790-49aa-8a26-4a65365255b4' class='xr-var-data-in' type='checkbox'><label for='data-5e11834f-5790-49aa-8a26-4a65365255b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>NORMAL SHORT WAVE FLUX AT GROUND SURFACE (SLOPE-DEPENDENT)</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b6c11c6a-874c-4d90-9ae7-9fee4ce3559a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6c11c6a-874c-4d90-9ae7-9fee4ce3559a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb22539d-7ab4-4e5b-84c6-a0b8ba2b741b' class='xr-var-data-in' type='checkbox'><label for='data-bb22539d-7ab4-4e5b-84c6-a0b8ba2b741b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-c5145864-dd2a-48d7-97e0-93a293a0bf7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5145864-dd2a-48d7-97e0-93a293a0bf7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c1a115f-c6dd-4156-a480-5117efed414c' class='xr-var-data-in' type='checkbox'><label for='data-4c1a115f-c6dd-4156-a480-5117efed414c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-6fd0b0dd-88b1-4bfb-94a2-75dd1c9ecf63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fd0b0dd-88b1-4bfb-94a2-75dd1c9ecf63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f240570-557e-4e09-b9e7-8167a978872b' class='xr-var-data-in' type='checkbox'><label for='data-0f240570-557e-4e09-b9e7-8167a978872b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-9ff9f1eb-631b-49bb-9bc9-bed23c3f9298' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ff9f1eb-631b-49bb-9bc9-bed23c3f9298' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2616510e-418e-448f-892a-d55d1766869a' class='xr-var-data-in' type='checkbox'><label for='data-2616510e-418e-448f-892a-d55d1766869a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-764e195b-bdda-4cdf-9d13-bd142c9f2deb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-764e195b-bdda-4cdf-9d13-bd142c9f2deb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5f63b32-ad9e-4151-adb5-f15807983fc4' class='xr-var-data-in' type='checkbox'><label for='data-d5f63b32-ad9e-4151-adb5-f15807983fc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-c6b90c4a-aac3-47ce-adfc-573ae8164fc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6b90c4a-aac3-47ce-adfc-573ae8164fc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-393965c8-49eb-47c8-99c2-7f6ce86b60c5' class='xr-var-data-in' type='checkbox'><label for='data-393965c8-49eb-47c8-99c2-7f6ce86b60c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a716315b-b4c7-4d66-adee-47b73232b821' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a716315b-b4c7-4d66-adee-47b73232b821' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-632579c0-20d4-4524-9646-dfb285c01cec' class='xr-var-data-in' type='checkbox'><label for='data-632579c0-20d4-4524-9646-dfb285c01cec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACSWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-fdc05fed-d1e8-4ed1-8946-6183b139d857' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdc05fed-d1e8-4ed1-8946-6183b139d857' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-762d44cc-1d93-43b7-8cf4-858a328fa269' class='xr-var-data-in' type='checkbox'><label for='data-762d44cc-1d93-43b7-8cf4-858a328fa269' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e31f466e-8ce4-415d-af67-d69b26f9fcd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e31f466e-8ce4-415d-af67-d69b26f9fcd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4052d4c4-511a-40a5-8562-b9e13102e5ba' class='xr-var-data-in' type='checkbox'><label for='data-4052d4c4-511a-40a5-8562-b9e13102e5ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-277269d8-2526-4eca-9ca1-43268788d88f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-277269d8-2526-4eca-9ca1-43268788d88f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1aa77618-e319-4e9a-8406-69d379cd8b29' class='xr-var-data-in' type='checkbox'><label for='data-1aa77618-e319-4e9a-8406-69d379cd8b29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4a9303b6-e678-492e-a0e8-bb8640e0ec8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a9303b6-e678-492e-a0e8-bb8640e0ec8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e968db45-e016-4f62-97e4-232022f3c785' class='xr-var-data-in' type='checkbox'><label for='data-e968db45-e016-4f62-97e4-232022f3c785' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1b8ab84d-9fbc-4973-a3e8-1a4eb8764a80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b8ab84d-9fbc-4973-a3e8-1a4eb8764a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75450c17-ae5a-45c0-868c-adecdd336405' class='xr-var-data-in' type='checkbox'><label for='data-75450c17-ae5a-45c0-868c-adecdd336405' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b56018ac-56ca-481e-8f8b-3e4e7464ed4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b56018ac-56ca-481e-8f8b-3e4e7464ed4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08eaa16f-995b-4f9d-83b4-bc2c1a7be3e6' class='xr-var-data-in' type='checkbox'><label for='data-08eaa16f-995b-4f9d-83b4-bc2c1a7be3e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-698f67e0-ab3c-4b0c-8708-6a94ca973039' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-698f67e0-ab3c-4b0c-8708-6a94ca973039' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a148a0db-d175-4d6c-a64f-66c7ea98ab6b' class='xr-var-data-in' type='checkbox'><label for='data-a148a0db-d175-4d6c-a64f-66c7ea98ab6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-deb6883f-85d0-4495-bcd4-efb961c7a7cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-deb6883f-85d0-4495-bcd4-efb961c7a7cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef63f339-9eb5-4715-b1c4-c3ea5024a33a' class='xr-var-data-in' type='checkbox'><label for='data-ef63f339-9eb5-4715-b1c4-c3ea5024a33a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-621fd1bd-5594-4eea-917e-e27e25c682d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-621fd1bd-5594-4eea-917e-e27e25c682d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b0b3e25-6176-435d-829b-90a77a774034' class='xr-var-data-in' type='checkbox'><label for='data-4b0b3e25-6176-435d-829b-90a77a774034' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED DOWNWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-6ce15955-61f8-40fe-a2fc-ce9b2d501a51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ce15955-61f8-40fe-a2fc-ce9b2d501a51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e46fcf29-d401-4ee4-b186-f75967f21c49' class='xr-var-data-in' type='checkbox'><label for='data-e46fcf29-d401-4ee4-b186-f75967f21c49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-113a0dab-e849-47ef-a61b-8ae5fb193298' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-113a0dab-e849-47ef-a61b-8ae5fb193298' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abdfba3c-3fea-4b02-8139-4c552d20b92f' class='xr-var-data-in' type='checkbox'><label for='data-abdfba3c-3fea-4b02-8139-4c552d20b92f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-328d4165-905b-4368-a23f-2dbc70fab98d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-328d4165-905b-4368-a23f-2dbc70fab98d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55f40e1c-5285-4d74-aed8-3b64b1d511b0' class='xr-var-data-in' type='checkbox'><label for='data-55f40e1c-5285-4d74-aed8-3b64b1d511b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-01f7f3cc-1ea9-4f9b-95cf-983388a3e54e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01f7f3cc-1ea9-4f9b-95cf-983388a3e54e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02a013c7-5c8f-4f7c-916b-73bc2a98dc28' class='xr-var-data-in' type='checkbox'><label for='data-02a013c7-5c8f-4f7c-916b-73bc2a98dc28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-344c833d-b08d-4ce9-965b-0b5080cdbec4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-344c833d-b08d-4ce9-965b-0b5080cdbec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6198e510-c1a7-4f9e-8eb9-54ddd6148621' class='xr-var-data-in' type='checkbox'><label for='data-6198e510-c1a7-4f9e-8eb9-54ddd6148621' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-606918c8-cff8-409c-be44-1bee8a5d0e44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-606918c8-cff8-409c-be44-1bee8a5d0e44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3d2df8b-6ff8-4cdc-af66-34c2994895e2' class='xr-var-data-in' type='checkbox'><label for='data-e3d2df8b-6ff8-4cdc-af66-34c2994895e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-fbdd8a80-627e-416a-a038-1e52b562c11b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbdd8a80-627e-416a-a038-1e52b562c11b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8b5a958-37b5-43b4-981f-cc8c5b47264f' class='xr-var-data-in' type='checkbox'><label for='data-f8b5a958-37b5-43b4-981f-cc8c5b47264f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACSWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-9f484352-2e02-4b78-857e-124cbb6d4d8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f484352-2e02-4b78-857e-124cbb6d4d8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c6f8fa0-0ca7-4acc-bf3d-2618874406c3' class='xr-var-data-in' type='checkbox'><label for='data-3c6f8fa0-0ca7-4acc-bf3d-2618874406c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
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+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-8578a599-229c-4a03-a302-a0ec8f1485fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8578a599-229c-4a03-a302-a0ec8f1485fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ac403d0-959f-432a-8138-896b4b71f781' class='xr-var-data-in' type='checkbox'><label for='data-0ac403d0-959f-432a-8138-896b4b71f781' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-5110c925-c1eb-4994-9369-c3126a7e4546' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5110c925-c1eb-4994-9369-c3126a7e4546' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37eaa590-828e-4b85-ac47-5afafa63c7f0' class='xr-var-data-in' type='checkbox'><label for='data-37eaa590-828e-4b85-ac47-5afafa63c7f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-845284b1-e887-4510-87aa-4b26df5e42d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-845284b1-e887-4510-87aa-4b26df5e42d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7185f2da-1d77-4531-a6fb-0b038647a96e' class='xr-var-data-in' type='checkbox'><label for='data-7185f2da-1d77-4531-a6fb-0b038647a96e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-daa84e36-e4a3-4a50-a1e2-852ef11faa12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-daa84e36-e4a3-4a50-a1e2-852ef11faa12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9e33f23-447e-438e-bc35-60ec27e8949a' class='xr-var-data-in' type='checkbox'><label for='data-d9e33f23-447e-438e-bc35-60ec27e8949a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-ea7cf30b-05da-4ec6-8fac-4bc70624c0a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea7cf30b-05da-4ec6-8fac-4bc70624c0a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fdb02c4-dbb9-432c-8478-e1d20ce07b56' class='xr-var-data-in' type='checkbox'><label for='data-5fdb02c4-dbb9-432c-8478-e1d20ce07b56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1f072405-d113-428b-b9cb-ef2ba7e34f01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f072405-d113-428b-b9cb-ef2ba7e34f01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bba9a2d-fe8b-403c-950d-5b0c67f3973d' class='xr-var-data-in' type='checkbox'><label for='data-0bba9a2d-fe8b-403c-950d-5b0c67f3973d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR UPWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-9d7946ef-4c0d-4a6e-af2a-4b60e8eb8a87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d7946ef-4c0d-4a6e-af2a-4b60e8eb8a87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d68f306-e89c-483e-86fa-56a5da93e0d3' class='xr-var-data-in' type='checkbox'><label for='data-8d68f306-e89c-483e-86fa-56a5da93e0d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>I_ACLWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-0066c1cb-0016-4ac8-8e2b-5ed32b3fd39d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0066c1cb-0016-4ac8-8e2b-5ed32b3fd39d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1267bb50-ad1f-42c3-85e4-31115e3c3fde' class='xr-var-data-in' type='checkbox'><label for='data-1267bb50-ad1f-42c3-85e4-31115e3c3fde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BUCKET FOR DOWNWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4c96f40e-4123-49e8-a993-8e21ef4b5130' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c96f40e-4123-49e8-a993-8e21ef4b5130' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ec44fec-24e8-40f1-b81a-177ce33fe432' class='xr-var-data-in' type='checkbox'><label for='data-8ec44fec-24e8-40f1-b81a-177ce33fe432' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-db08c896-2afb-4db5-bf03-df244d53e934' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db08c896-2afb-4db5-bf03-df244d53e934' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-173263ef-9514-41b8-91e9-50887c2fb799' class='xr-var-data-in' type='checkbox'><label for='data-173263ef-9514-41b8-91e9-50887c2fb799' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-3cb6673d-f32b-4229-8fa8-45e19c81ca02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cb6673d-f32b-4229-8fa8-45e19c81ca02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3df53b94-1d3a-40dd-b0cc-5df40d3ad517' class='xr-var-data-in' type='checkbox'><label for='data-3df53b94-1d3a-40dd-b0cc-5df40d3ad517' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4ed6f141-f757-4db6-bc80-cb0002c13fee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ed6f141-f757-4db6-bc80-cb0002c13fee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-babbfa17-59a3-4da2-804a-c4629138edbc' class='xr-var-data-in' type='checkbox'><label for='data-babbfa17-59a3-4da2-804a-c4629138edbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-d3bc2cbc-c1a0-49ce-a9b4-d843fed6c94f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3bc2cbc-c1a0-49ce-a9b4-d843fed6c94f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6203b849-35f5-47ca-b5a2-4163b69a8d09' class='xr-var-data-in' type='checkbox'><label for='data-6203b849-35f5-47ca-b5a2-4163b69a8d09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e6994e78-e4e9-454e-bcef-7968aa514e73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6994e78-e4e9-454e-bcef-7968aa514e73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44779e29-f67d-42d4-bc19-51d5e90fbd62' class='xr-var-data-in' type='checkbox'><label for='data-44779e29-f67d-42d4-bc19-51d5e90fbd62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-468621c8-f961-472e-9e60-bd6580c656ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-468621c8-f961-472e-9e60-bd6580c656ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f875a4e5-3e2c-4133-b861-ef7291e1225f' class='xr-var-data-in' type='checkbox'><label for='data-f875a4e5-3e2c-4133-b861-ef7291e1225f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f4363c66-fbb4-4a52-802c-26bf08525050' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4363c66-fbb4-4a52-802c-26bf08525050' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fff9882-15e2-4cae-bba0-16360026ec9e' class='xr-var-data-in' type='checkbox'><label for='data-6fff9882-15e2-4cae-bba0-16360026ec9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING CLEAR SKY SHORTWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWUPT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-9765a6ca-ab66-48ae-aa9e-64b5b4ad3752' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9765a6ca-ab66-48ae-aa9e-64b5b4ad3752' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d381f84d-8919-4a44-a501-19e43ca2a514' class='xr-var-data-in' type='checkbox'><label for='data-d381f84d-8919-4a44-a501-19e43ca2a514' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWUPTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-5d6d0041-bfe0-4a57-96b5-6f20060fd720' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d6d0041-bfe0-4a57-96b5-6f20060fd720' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79d7762d-026b-4c64-84e6-cc586d80a43b' class='xr-var-data-in' type='checkbox'><label for='data-79d7762d-026b-4c64-84e6-cc586d80a43b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWDNT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-bc081ca7-7f3a-47ca-820a-820de1f7f0ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc081ca7-7f3a-47ca-820a-820de1f7f0ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4749353a-8ce8-4e7a-a68b-0d6c3e83114b' class='xr-var-data-in' type='checkbox'><label for='data-4749353a-8ce8-4e7a-a68b-0d6c3e83114b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWDNTC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-837c4cea-b4b4-48d5-a602-d315b9190661' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-837c4cea-b4b4-48d5-a602-d315b9190661' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77facd74-5663-4232-9fc1-24d018edb870' class='xr-var-data-in' type='checkbox'><label for='data-77facd74-5663-4232-9fc1-24d018edb870' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING CLEAR SKY LONGWAVE FLUX AT TOP</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWUPB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4ba609be-d0f5-4ecc-912a-5f6758ef6ace' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ba609be-d0f5-4ecc-912a-5f6758ef6ace' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81401b98-88d8-4545-b427-cd25a5791a8f' class='xr-var-data-in' type='checkbox'><label for='data-81401b98-88d8-4545-b427-cd25a5791a8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWUPBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-c7c753ff-ddc1-4f4a-b4a7-4681c5324b5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7c753ff-ddc1-4f4a-b4a7-4681c5324b5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97b90a08-c32f-40f1-ab36-fa1d1bc89c88' class='xr-var-data-in' type='checkbox'><label for='data-97b90a08-c32f-40f1-ab36-fa1d1bc89c88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS UPWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWDNB</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-cce3df6c-317d-4d34-a8ce-c29954d9d14d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cce3df6c-317d-4d34-a8ce-c29954d9d14d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc9ef090-138e-4ca3-92a9-8a84d08ae04c' class='xr-var-data-in' type='checkbox'><label for='data-bc9ef090-138e-4ca3-92a9-8a84d08ae04c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWDNBC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-27b8bcd0-cbdd-4722-8e24-4ca83768265b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27b8bcd0-cbdd-4722-8e24-4ca83768265b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffd60a9e-4724-48f1-91f9-27dc1fd7aef1' class='xr-var-data-in' type='checkbox'><label for='data-ffd60a9e-4724-48f1-91f9-27dc1fd7aef1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>INSTANTANEOUS DOWNWELLING CLEAR SKY LONGWAVE FLUX AT BOTTOM</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OLR</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-991d08c5-4d37-419d-856e-28a8317ffa7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-991d08c5-4d37-419d-856e-28a8317ffa7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80ad3c22-c5f9-4e4e-b6ce-63b66d19edd0' class='xr-var-data-in' type='checkbox'><label for='data-80ad3c22-c5f9-4e4e-b6ce-63b66d19edd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>TOA OUTGOING LONG WAVE</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALBEDO</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-bdd24395-6361-49fd-8250-56fd057d693b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdd24395-6361-49fd-8250-56fd057d693b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9abbcb95-9cb6-4393-aa1c-c882d49ee764' class='xr-var-data-in' type='checkbox'><label for='data-9abbcb95-9cb6-4393-aa1c-c882d49ee764' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ALBEDO</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALBBCK</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-713b09f3-4861-4bf7-9405-fd8dfc3e9fa1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-713b09f3-4861-4bf7-9405-fd8dfc3e9fa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5788385a-3525-47f4-bfb6-4ab65454e2ba' class='xr-var-data-in' type='checkbox'><label for='data-5788385a-3525-47f4-bfb6-4ab65454e2ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>BACKGROUND ALBEDO</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EMISS</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-2ded7eb3-55c8-444f-9b14-47fe9b80762c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ded7eb3-55c8-444f-9b14-47fe9b80762c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-931b09c4-012f-43aa-b3f2-ba2c98012c22' class='xr-var-data-in' type='checkbox'><label for='data-931b09c4-012f-43aa-b3f2-ba2c98012c22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SURFACE EMISSIVITY</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NOAHRES</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-7817ec83-7fa8-434c-b098-d59aad851446' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7817ec83-7fa8-434c-b098-d59aad851446' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0cbe10e-86c1-4c31-ba44-c81d9baccdcf' class='xr-var-data-in' type='checkbox'><label for='data-c0cbe10e-86c1-4c31-ba44-c81d9baccdcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>RESIDUAL OF THE NOAH SURFACE ENERGY BUDGET</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TMN</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-24382710-12b4-495c-b02e-ca5baf9b19cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24382710-12b4-495c-b02e-ca5baf9b19cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b67756e-bfa4-4a87-841e-510b9b584c7c' class='xr-var-data-in' type='checkbox'><label for='data-6b67756e-bfa4-4a87-841e-510b9b584c7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SOIL TEMPERATURE AT LOWER BOUNDARY</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLAND</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-6d2873ac-9b9f-42d3-a4a2-d5d85796598e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d2873ac-9b9f-42d3-a4a2-d5d85796598e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba7dfa29-5c44-4fe8-99b0-bdf629ebe5c1' class='xr-var-data-in' type='checkbox'><label for='data-ba7dfa29-5c44-4fe8-99b0-bdf629ebe5c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LAND MASK (1 FOR LAND, 2 FOR WATER)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UST</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-2c703481-b5cc-444e-b3bf-105c1d131162' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c703481-b5cc-444e-b3bf-105c1d131162' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04ced6f6-f861-47de-826f-f5170d68df20' class='xr-var-data-in' type='checkbox'><label for='data-04ced6f6-f861-47de-826f-f5170d68df20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>U* IN SIMILARITY THEORY</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PBLH</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-e5823a51-3d02-47dc-8527-23624904ffd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5823a51-3d02-47dc-8527-23624904ffd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b2ba8ee-c6f1-499f-978e-f68729f60577' class='xr-var-data-in' type='checkbox'><label for='data-4b2ba8ee-c6f1-499f-978e-f68729f60577' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>PBL HEIGHT</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>atmosphere_boundary_layer_thickness</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HFX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-66968086-4231-489e-9ee7-9cfdc6b9a6fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66968086-4231-489e-9ee7-9cfdc6b9a6fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f856805-f02f-47df-a875-eb039f09ba9b' class='xr-var-data-in' type='checkbox'><label for='data-8f856805-f02f-47df-a875-eb039f09ba9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>UPWARD HEAT FLUX AT THE SURFACE</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>surface_upward_heat_flux_in_air</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QFX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-78ce7a7c-c13f-44c3-b094-195019bd6e97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78ce7a7c-c13f-44c3-b094-195019bd6e97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76b6a344-4b10-4410-b527-be95049517b2' class='xr-var-data-in' type='checkbox'><label for='data-76b6a344-4b10-4410-b527-be95049517b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>UPWARD MOISTURE FLUX AT THE SURFACE</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LH</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-64b6cc1a-acb7-4ed1-b34c-5945a324da12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64b6cc1a-acb7-4ed1-b34c-5945a324da12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1205eec5-4171-44d7-b796-e0d2773ccfae' class='xr-var-data-in' type='checkbox'><label for='data-1205eec5-4171-44d7-b796-e0d2773ccfae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LATENT HEAT FLUX AT THE SURFACE</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>surface_upward_latent_heat_flux_in_air</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACHFX</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f5755416-a5c3-4cd5-972c-79c74f37689d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5755416-a5c3-4cd5-972c-79c74f37689d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f61e5e3-d7d5-4d22-94f5-42bf64ffc46c' class='xr-var-data-in' type='checkbox'><label for='data-3f61e5e3-d7d5-4d22-94f5-42bf64ffc46c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWARD HEAT FLUX AT THE SURFACE</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>integral_of_surface_upward_heat_flux_in_air_wrt_time</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ACLHF</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-a9b750c8-3206-4e6d-b405-3f68ca16ba79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9b750c8-3206-4e6d-b405-3f68ca16ba79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07801c2a-2b81-43f5-8dbc-77135413651c' class='xr-var-data-in' type='checkbox'><label for='data-07801c2a-2b81-43f5-8dbc-77135413651c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED UPWARD LATENT HEAT FLUX AT THE SURFACE</dd><dt><span>units :</span></dt><dd>J m-2</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>integral_of_surface_upward_latent_heat_flux_in_air_wrf_time</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOWC</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f018109a-068e-414f-ad8c-fdd28fc97f40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f018109a-068e-414f-ad8c-fdd28fc97f40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c263856a-0525-4a49-9616-af1137c907ba' class='xr-var-data-in' type='checkbox'><label for='data-c263856a-0525-4a49-9616-af1137c907ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>FLAG INDICATING SNOW COVERAGE (1 FOR SNOW COVER)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 125, 125) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                        <th> Data type </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SR</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f2d11a00-85dc-4c0b-8ece-90565a6958e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2d11a00-85dc-4c0b-8ece-90565a6958e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ac661bc-3248-4516-a2af-f2defff44f32' class='xr-var-data-in' type='checkbox'><label for='data-5ac661bc-3248-4516-a2af-f2defff44f32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>fraction of frozen precipitation</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SAVE_TOPO_FROM_REAL</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-561f6d39-447d-4cd1-9d87-5a98e96f44f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-561f6d39-447d-4cd1-9d87-5a98e96f44f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6def0cf1-d814-4122-b129-3ba6281ce318' class='xr-var-data-in' type='checkbox'><label for='data-6def0cf1-d814-4122-b129-3ba6281ce318' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>1=original topo from real/0=topo modified by WRF</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ISEEDARR_RAND_PERTURB</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-615c979a-2db6-46d1-8cfe-54e901028c2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-615c979a-2db6-46d1-8cfe-54e901028c2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc8df573-bd98-4ad9-8a05-969bde865976' class='xr-var-data-in' type='checkbox'><label for='data-cc8df573-bd98-4ad9-8a05-969bde865976' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Array to hold seed for restart, RAND_PERT</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ISEEDARR_SPPT</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-c1649640-f55f-464d-ac9d-b892fe686392' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1649640-f55f-464d-ac9d-b892fe686392' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ffe51da-15d6-46f3-9432-73184e5eab7f' class='xr-var-data-in' type='checkbox'><label for='data-1ffe51da-15d6-46f3-9432-73184e5eab7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Array to hold seed for restart, SPPT</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ISEEDARR_SKEBS</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-5d060c8e-7def-4881-8f8c-a2a4587e9040' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d060c8e-7def-4881-8f8c-a2a4587e9040' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fe2c553-6a81-4162-96fa-6168ba6cb336' class='xr-var-data-in' type='checkbox'><label for='data-2fe2c553-6a81-4162-96fa-6168ba6cb336' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>Array to hold seed for restart, SKEBS</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m11</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-71f907fd-6bcd-42e3-840b-d072f64b2763' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71f907fd-6bcd-42e3-840b-d072f64b2763' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39dab95e-e34b-4a46-b618-8e375ff07f30' class='xr-var-data-in' type='checkbox'><label for='data-39dab95e-e34b-4a46-b618-8e375ff07f30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>11 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
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+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m22</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1cba5767-1cc2-401b-8ac2-00059ecc466d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cba5767-1cc2-401b-8ac2-00059ecc466d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a7744f4-02b1-46e6-b887-db87273fcf6c' class='xr-var-data-in' type='checkbox'><label for='data-8a7744f4-02b1-46e6-b887-db87273fcf6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>22 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m33</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-fa76d9fe-8a87-4392-b8f1-c802b6ac3f2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa76d9fe-8a87-4392-b8f1-c802b6ac3f2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-268cef0c-678f-4e4b-8769-9b02e1620591' class='xr-var-data-in' type='checkbox'><label for='data-268cef0c-678f-4e4b-8769-9b02e1620591' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>33 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m12</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-cff83f70-3f6a-4c88-bd88-d98be4300a89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cff83f70-3f6a-4c88-bd88-d98be4300a89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3501ffa-6ec4-4e42-8f20-677b855db666' class='xr-var-data-in' type='checkbox'><label for='data-a3501ffa-6ec4-4e42-8f20-677b855db666' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>12 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m13</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-be80e7d3-b102-44e2-a9fc-cd1ba568b6f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be80e7d3-b102-44e2-a9fc-cd1ba568b6f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25e60585-69c6-4152-9329-fc31f826a462' class='xr-var-data-in' type='checkbox'><label for='data-25e60585-69c6-4152-9329-fc31f826a462' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>13 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m23</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-8a3ed94d-550d-47e0-a3e8-994f03c3ced2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a3ed94d-550d-47e0-a3e8-994f03c3ced2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31f3e5f6-e844-4f5b-a22e-e9c05136f610' class='xr-var-data-in' type='checkbox'><label for='data-31f3e5f6-e844-4f5b-a22e-e9c05136f610' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>23 component of NBA subgrid stress tensor</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U_LS</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-618056f6-4797-450a-bed6-6ba00fef9091' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-618056f6-4797-450a-bed6-6ba00fef9091' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6cbf787-c19d-4071-b8f0-8c6574b7c4a3' class='xr-var-data-in' type='checkbox'><label for='data-d6cbf787-c19d-4071-b8f0-8c6574b7c4a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>large-scale zonal wind velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U_LS_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-3fdd2fcd-8968-4da5-a4f1-72a3199316b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fdd2fcd-8968-4da5-a4f1-72a3199316b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3d11bd3-57c8-47d6-9264-59beb6756d4f' class='xr-var-data-in' type='checkbox'><label for='data-b3d11bd3-57c8-47d6-9264-59beb6756d4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency large-scale zonal wind velocity</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
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+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "    <tr>\n",
+       "        <td>\n",
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+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V_LS_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-95989766-3337-4f39-81dd-41ae5105db21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95989766-3337-4f39-81dd-41ae5105db21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc59dc18-e617-45ec-8b06-6138013fff55' class='xr-var-data-in' type='checkbox'><label for='data-fc59dc18-e617-45ec-8b06-6138013fff55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency large-scale meridional wind velocity</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_LS</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-8feea093-d0cc-4353-8bba-df5a3daf230c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8feea093-d0cc-4353-8bba-df5a3daf230c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ddd76e1-7543-4247-b64e-0b677459e9e7' class='xr-var-data-in' type='checkbox'><label for='data-6ddd76e1-7543-4247-b64e-0b677459e9e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>large-scale vertical velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Colored Rectangle -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_LS_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-001b641b-7e1d-49ba-80a4-fa5fef789cb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-001b641b-7e1d-49ba-80a4-fa5fef789cb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24ecaeb5-8a17-4101-99a8-6cf693d607fa' class='xr-var-data-in' type='checkbox'><label for='data-24ecaeb5-8a17-4101-99a8-6cf693d607fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency large-scale vertical velocity</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>INV_TAU_S</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-11b5c553-dc63-49de-95dd-0c98a983593c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11b5c553-dc63-49de-95dd-0c98a983593c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcb02cce-7451-447e-a0ae-21771b6035a3' class='xr-var-data-in' type='checkbox'><label for='data-fcb02cce-7451-447e-a0ae-21771b6035a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>inverse relaxation time for scalars</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>INV_TAU_M</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-bf6b19d3-edb2-4084-b733-9a5ca48d0798' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf6b19d3-edb2-4084-b733-9a5ca48d0798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba9885be-9c31-48e5-b328-e91123480059' class='xr-var-data-in' type='checkbox'><label for='data-ba9885be-9c31-48e5-b328-e91123480059' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>inverse relaxation time for scalars</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
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+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TH_ADV</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-a145c5c9-5ab2-49b4-bc95-6f250a989d7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a145c5c9-5ab2-49b4-bc95-6f250a989d7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f23bbe32-dc37-4bd3-8164-af872ee14e6d' class='xr-var-data-in' type='checkbox'><label for='data-f23bbe32-dc37-4bd3-8164-af872ee14e6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency theta advection</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
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+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
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+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TH_ADV_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-095113f3-fba9-4ad9-a2ac-a980372c5f01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-095113f3-fba9-4ad9-a2ac-a980372c5f01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-285c3462-5eef-4915-bd7d-6a1eca03ba97' class='xr-var-data-in' type='checkbox'><label for='data-285c3462-5eef-4915-bd7d-6a1eca03ba97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency of tendency theta advection</dd><dt><span>units :</span></dt><dd>K s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
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+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
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+       "  <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
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+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
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+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"59.632104\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >751</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TH_RLX</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-261f8ec3-a434-4df0-a6cf-c6b6a7b4f87a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-261f8ec3-a434-4df0-a6cf-c6b6a7b4f87a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-796a5662-4561-443e-bd57-0c4433b63daf' class='xr-var-data-in' type='checkbox'><label for='data-796a5662-4561-443e-bd57-0c4433b63daf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>theta relaxation</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TH_RLX_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-cb7bd1c7-b2f2-445c-992c-4b058c7642db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb7bd1c7-b2f2-445c-992c-4b058c7642db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dd42f16-8d58-4280-9950-f1f1ae3c9a91' class='xr-var-data-in' type='checkbox'><label for='data-8dd42f16-8d58-4280-9950-f1f1ae3c9a91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency theta relaxation</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV_ADV</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-656c491c-2c81-4426-b364-7aee2cf2e6d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-656c491c-2c81-4426-b364-7aee2cf2e6d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e988b36-7845-43cd-be1b-07ae841b19a1' class='xr-var-data-in' type='checkbox'><label for='data-4e988b36-7845-43cd-be1b-07ae841b19a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency qv advection</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,39.632103775113606 0.0,39.632103775113606\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV_ADV_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-3f09638f-4143-43c3-938e-0afb962e57fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f09638f-4143-43c3-938e-0afb962e57fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c4edb8b-088c-4a58-9dc9-2fee885c0cc3' class='xr-var-data-in' type='checkbox'><label for='data-3c4edb8b-088c-4a58-9dc9-2fee885c0cc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency of tendency qv advection</dd><dt><span>units :</span></dt><dd>kg kg-1 s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
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+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV_RLX</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-97f76e17-086d-4972-b0a4-113e90b41182' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97f76e17-086d-4972-b0a4-113e90b41182' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52c26c6e-552a-4e11-b7f1-f693a762123c' class='xr-var-data-in' type='checkbox'><label for='data-52c26c6e-552a-4e11-b7f1-f693a762123c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv relaxation</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QV_RLX_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-3247e6d2-e0a0-46af-ad27-f285f91abfed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3247e6d2-e0a0-46af-ad27-f285f91abfed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bee9a7b-a0b1-4e87-ab2d-0c78092705da' class='xr-var-data-in' type='checkbox'><label for='data-8bee9a7b-a0b1-4e87-ab2d-0c78092705da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency qv relaxation</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z_LS</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-8d2073bd-9d13-47ab-9f10-dc33466be9eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d2073bd-9d13-47ab-9f10-dc33466be9eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a2319a5-8418-451e-8081-ec5ad6ab570b' class='xr-var-data-in' type='checkbox'><label for='data-1a2319a5-8418-451e-8081-ec5ad6ab570b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>z of large-scale forcings</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z_LS_TEND</span></div><div class='xr-var-dims'>(Time, force_layers)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 751), meta=np.ndarray&gt;</div><input id='attrs-0e505f54-6e17-4b8b-a20a-7feb1fe788e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e505f54-6e17-4b8b-a20a-7feb1fe788e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6ba5fb4-73fb-48ba-9c83-736fc6ae678c' class='xr-var-data-in' type='checkbox'><label for='data-b6ba5fb4-73fb-48ba-9c83-736fc6ae678c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>tendency of z of large-scale forcings</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 266.96 kiB </td>\n",
+       "                        <td> 2.93 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 751) </td>\n",
+       "                        <td> (1, 751) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"89\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"39\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"39\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_SH_FLX</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-d5f23472-b228-4906-b819-631c41867317' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5f23472-b228-4906-b819-631c41867317' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6938459f-7795-4172-9a7d-69409ea88dc6' class='xr-var-data-in' type='checkbox'><label for='data-6938459f-7795-4172-9a7d-69409ea88dc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>prescribed sensible heat flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_SH_FLX_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-bf1ab9ce-6d68-4a76-8f8e-d8358a6845c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf1ab9ce-6d68-4a76-8f8e-d8358a6845c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6406987-3e02-4dd8-9d30-19397f2c3946' class='xr-var-data-in' type='checkbox'><label for='data-c6406987-3e02-4dd8-9d30-19397f2c3946' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>tendency prescribed sensible heat flux</dd><dt><span>units :</span></dt><dd>W m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_LH_FLX</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-1bf91cd1-44a7-4111-ac67-3bdea74db63d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bf91cd1-44a7-4111-ac67-3bdea74db63d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ba73891-92db-4715-8dd6-406bc99fafb2' class='xr-var-data-in' type='checkbox'><label for='data-8ba73891-92db-4715-8dd6-406bc99fafb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>prescribed latent heat flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_LH_FLX_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-b0f6225f-d6ee-4e7d-a224-d38b16250deb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0f6225f-d6ee-4e7d-a224-d38b16250deb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b0ead87-91c8-482e-a0a5-91deef172e11' class='xr-var-data-in' type='checkbox'><label for='data-8b0ead87-91c8-482e-a0a5-91deef172e11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>tendency prescribed latent heat flux</dd><dt><span>units :</span></dt><dd>W m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"25\" />\n",
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+       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
+       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
+       "  <line x1=\"79\" y1=\"0\" x2=\"79\" y2=\"25\" />\n",
+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_ALBEDO</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-0b860d0b-8501-4d2e-bfb0-c0cd1179b93b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b860d0b-8501-4d2e-bfb0-c0cd1179b93b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c529346e-aa14-41bd-bf91-fbca591fa962' class='xr-var-data-in' type='checkbox'><label for='data-c529346e-aa14-41bd-bf91-fbca591fa962' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>prescribed albedo</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Vertical lines -->\n",
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+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_ALBEDO_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-18598c21-bb0e-4c02-85ac-0e0b0125c236' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18598c21-bb0e-4c02-85ac-0e0b0125c236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3374c48f-2d7c-4cbf-a392-028554658042' class='xr-var-data-in' type='checkbox'><label for='data-3374c48f-2d7c-4cbf-a392-028554658042' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>tendency prescribed albedo</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_TSK</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-74132684-d441-4796-80ac-8a1e7ffbfa18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74132684-d441-4796-80ac-8a1e7ffbfa18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ec0c1bb-d3fa-4da0-be04-4f1e3bef10ea' class='xr-var-data-in' type='checkbox'><label for='data-5ec0c1bb-d3fa-4da0-be04-4f1e3bef10ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>prescribed skin temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.000000\" y=\"12.934923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.934923)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRE_TSK_TEND</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-7ef05841-dc80-480a-8f56-968870e19de5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ef05841-dc80-480a-8f56-968870e19de5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f36324d-89a2-4d76-80f4-173917f5d87f' class='xr-var-data-in' type='checkbox'><label for='data-4f36324d-89a2-4d76-80f4-173917f5d87f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>tendency prescribed skin temperature</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"25\" />\n",
+       "  <line x1=\"110\" y1=\"0\" x2=\"110\" y2=\"25\" />\n",
+       "  <line x1=\"118\" y1=\"0\" x2=\"118\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.869847\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NUM_MODES_AER</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6,), meta=np.ndarray&gt;</div><input id='attrs-1dc9c5d3-ad65-4eab-8790-c33ffb3a5d49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dc9c5d3-ad65-4eab-8790-c33ffb3a5d49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c58fc7da-8f58-4db8-8a5c-afb0438219ef' class='xr-var-data-in' type='checkbox'><label for='data-c58fc7da-8f58-4db8-8a5c-afb0438219ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>106</dd><dt><span>MemoryOrder :</span></dt><dd>0  </dd><dt><span>description :</span></dt><dd>Number of aerosol distribution modes</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 364 B </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (6,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 16 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RULSTEN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-eca5b604-143b-4bce-9d84-ee7fd4be0aca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eca5b604-143b-4bce-9d84-ee7fd4be0aca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4328e87f-5556-4e34-827f-7ad98416548b' class='xr-var-data-in' type='checkbox'><label for='data-4328e87f-5556-4e34-827f-7ad98416548b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>coupled u tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>Pa m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RVLSTEN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-2c071c02-0971-4f01-af97-1931f18676fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c071c02-0971-4f01-af97-1931f18676fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d8fc0f7-59e9-4ff5-b577-77b0f6d0e228' class='xr-var-data-in' type='checkbox'><label for='data-9d8fc0f7-59e9-4ff5-b577-77b0f6d0e228' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>coupled v tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>Pa m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RTHLSTEN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-7f87a435-f7ca-489b-b6b1-1d04d2d6c309' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f87a435-f7ca-489b-b6b1-1d04d2d6c309' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91b9a9cc-f992-4b4c-af34-45ccedf120c1' class='xr-var-data-in' type='checkbox'><label for='data-91b9a9cc-f992-4b4c-af34-45ccedf120c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>coupled theta tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>Pa K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RQVLSTEN</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4945b3b5-a171-4e54-a0f6-4ff8a3894dca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4945b3b5-a171-4e54-a0f6-4ff8a3894dca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b813a16-406f-4138-a002-a616de40d4db' class='xr-var-data-in' type='checkbox'><label for='data-8b813a16-406f-4138-a002-a616de40d4db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>coupled Q_V tendency due to LS forcing</dd><dt><span>units :</span></dt><dd>Pa kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_DTHDZ</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-6608fed7-c75b-4889-81b2-c51fb8ec2113' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6608fed7-c75b-4889-81b2-c51fb8ec2113' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d62b75ba-7760-41e5-af68-064c1b260833' class='xr-var-data-in' type='checkbox'><label for='data-d62b75ba-7760-41e5-af68-064c1b260833' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>th tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_DQVDZ</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-4bd04ac0-aee8-4a36-87e2-b3999ffbfd06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bd04ac0-aee8-4a36-87e2-b3999ffbfd06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbd91eac-a937-4263-be22-6beb6326b45e' class='xr-var-data-in' type='checkbox'><label for='data-cbd91eac-a937-4263-be22-6beb6326b45e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>qv tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_DUDZ</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-8d59006c-a2e6-44c8-abea-7eda838fe25e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d59006c-a2e6-44c8-abea-7eda838fe25e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e21ae07a-5ea7-4f0c-80f6-dcd1a1385904' class='xr-var-data-in' type='checkbox'><label for='data-e21ae07a-5ea7-4f0c-80f6-dcd1a1385904' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>u tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>W_DVDZ</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-cf5f4865-4f83-4143-a674-064ae2287649' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf5f4865-4f83-4143-a674-064ae2287649' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92fb9b1b-f1e1-49c0-86b5-2a6cbf3053c3' class='xr-var-data-in' type='checkbox'><label for='data-92fb9b1b-f1e1-49c0-86b5-2a6cbf3053c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>v tendency due to LS vertical adv</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THDT_LSHOR</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-55672966-7f16-4790-be01-a284cf272f74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55672966-7f16-4790-be01-a284cf272f74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1af78cf3-97d5-4da6-a95b-91f51d2dba17' class='xr-var-data-in' type='checkbox'><label for='data-1af78cf3-97d5-4da6-a95b-91f51d2dba17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>th tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QVDT_LSHOR</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-7815bc86-dc45-4b97-ba0b-059e5e5d7534' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7815bc86-dc45-4b97-ba0b-059e5e5d7534' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a45fe7a-159b-48d0-b039-fa2351fafed0' class='xr-var-data-in' type='checkbox'><label for='data-3a45fe7a-159b-48d0-b039-fa2351fafed0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv tendency due to LS horizontal adv</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
+       "  <text x=\"140.000000\" y=\"24.159292\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,24.159292)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-40a59a32-b897-41b9-8bac-d41ba3cdfc21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40a59a32-b897-41b9-8bac-d41ba3cdfc21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-070334a1-59f2-4c12-b0f4-0694d46d1c94' class='xr-var-data-in' type='checkbox'><label for='data-070334a1-59f2-4c12-b0f4-0694d46d1c94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>th tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>K s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
+       "  <text x=\"140.000000\" y=\"24.159292\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,24.159292)\">91</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QVDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-3c1ce6b3-303d-48da-9d07-67fe8f1c5049' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c1ce6b3-303d-48da-9d07-67fe8f1c5049' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-636c276b-4aee-485d-b354-8bf31edf44f0' class='xr-var-data-in' type='checkbox'><label for='data-636c276b-4aee-485d-b354-8bf31edf44f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>qv tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>kg kg-1 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"32\" x2=\"120\" y2=\"32\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"48\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,48.31858407079646 0.0,48.31858407079646\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"68.318584\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >226</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-6a62ee46-8b0a-475b-a7a3-e2c4e9ef2408' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a62ee46-8b0a-475b-a7a3-e2c4e9ef2408' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e7de917-d229-45a5-9064-cb19f94e5ea3' class='xr-var-data-in' type='checkbox'><label for='data-7e7de917-d229-45a5-9064-cb19f94e5ea3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>u tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"98\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
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+       "\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VDT_LSRLX</span></div><div class='xr-var-dims'>(Time, z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226), meta=np.ndarray&gt;</div><input id='attrs-b362f1ff-02d4-4585-a649-7eb7b1481b33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b362f1ff-02d4-4585-a649-7eb7b1481b33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cec5ef5-f0d6-4d02-bcee-e73a9731f6f3' class='xr-var-data-in' type='checkbox'><label for='data-8cec5ef5-f0d6-4d02-bcee-e73a9731f6f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>Z  </dd><dt><span>description :</span></dt><dd>v tendency due to relaxation to LS</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80.34 kiB </td>\n",
+       "                        <td> 904 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226) </td>\n",
+       "                        <td> (1, 226) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 91 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EFFCS</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-cc05ae09-83bd-44da-9eff-ee9e032be570' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc05ae09-83bd-44da-9eff-ee9e032be570' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8693eafa-92d6-40e3-9a97-61dadd02158a' class='xr-var-data-in' type='checkbox'><label for='data-8693eafa-92d6-40e3-9a97-61dadd02158a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>CLOUD DROPLET EFFECTIVE RADIUS</dd><dt><span>units :</span></dt><dd>micron</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWF0</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-3d8d5131-faab-47bc-a330-451197e3ca14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d8d5131-faab-47bc-a330-451197e3ca14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ba4929e-7af7-43de-a606-ffdfa7fd0240' class='xr-var-data-in' type='checkbox'><label for='data-6ba4929e-7af7-43de-a606-ffdfa7fd0240' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Net LW radiative flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWF1</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-39012671-f138-474f-a17d-ad74cbe97a28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39012671-f138-474f-a17d-ad74cbe97a28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e192e7f-45c0-4f3d-b90e-e3b3219a65a5' class='xr-var-data-in' type='checkbox'><label for='data-7e192e7f-45c0-4f3d-b90e-e3b3219a65a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Net LW radiative flux, term1</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWF2</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f5448ea8-d181-4152-bd9e-62db99ba95f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5448ea8-d181-4152-bd9e-62db99ba95f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b7a57ba-9b6e-4203-8205-91036a05fe3a' class='xr-var-data-in' type='checkbox'><label for='data-0b7a57ba-9b6e-4203-8205-91036a05fe3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Net LW radiative flux, term2</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWF3</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-f4f4b2a0-31ef-4152-bb5c-349b6408816d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4f4b2a0-31ef-4152-bb5c-349b6408816d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a79c1da5-6af6-405a-a277-9a1f0952c86d' class='xr-var-data-in' type='checkbox'><label for='data-a79c1da5-6af6-405a-a277-9a1f0952c86d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Net LW radiative flux, term3</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ZI_QT8</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-8470f518-0241-4f17-be53-3d4447be640e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8470f518-0241-4f17-be53-3d4447be640e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1d2330d-6da7-4491-8c16-6d02908b2ae6' class='xr-var-data-in' type='checkbox'><label for='data-d1d2330d-6da7-4491-8c16-6d02908b2ae6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>zi defined by qt</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SEDFQC</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-9355f5b7-7022-424b-95d7-e519b12a5837' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9355f5b7-7022-424b-95d7-e519b12a5837' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9cf3c34-9121-4963-84fb-ba78bc3d33ba' class='xr-var-data-in' type='checkbox'><label for='data-f9cf3c34-9121-4963-84fb-ba78bc3d33ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>sedimentation flux of cloud water</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SEDFQR</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-bbb33244-a208-48d0-b1bc-8ca11c2d473c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbb33244-a208-48d0-b1bc-8ca11c2d473c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ddf6f5f-6b3e-494b-9824-0f5c74e28028' class='xr-var-data-in' type='checkbox'><label for='data-1ddf6f5f-6b3e-494b-9824-0f5c74e28028' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>sedimentation flux of rain water</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QVDT_PR</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-2b714b55-5089-498d-8adf-a1f97df2bda9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b714b55-5089-498d-8adf-a1f97df2bda9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-673d6ef8-7eab-4ef4-b8b8-f57a12d0bff3' class='xr-var-data-in' type='checkbox'><label for='data-673d6ef8-7eab-4ef4-b8b8-f57a12d0bff3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Production rate of vapor by conversion to rain</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QVDT_COND</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-852f30e2-e777-4cfd-9e96-108a32531411' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-852f30e2-e777-4cfd-9e96-108a32531411' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53033798-e898-4f97-b258-cc1ec22bd77d' class='xr-var-data-in' type='checkbox'><label for='data-53033798-e898-4f97-b258-cc1ec22bd77d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Production rate of vapor by conversion to rain</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QCDT_PR</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-9babb299-4329-412d-b2e3-665836d5c952' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9babb299-4329-412d-b2e3-665836d5c952' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-991845fb-9803-485e-91aa-5a5f738e5454' class='xr-var-data-in' type='checkbox'><label for='data-991845fb-9803-485e-91aa-5a5f738e5454' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Production rate of vapor by conversion to rain</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QCDT_SED</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-c260d227-0055-4835-935d-b7613752a4c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c260d227-0055-4835-935d-b7613752a4c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1dea099-c64e-4380-b95d-07638d611e20' class='xr-var-data-in' type='checkbox'><label for='data-e1dea099-c64e-4380-b95d-07638d611e20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Tendency of cloud water due to sedimentation</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QRDT_SED</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-bd7630b2-9a58-41b9-9b77-1e499cc53cfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd7630b2-9a58-41b9-9b77-1e499cc53cfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3be18dff-f325-4cde-b591-d6382fa3c5a5' class='xr-var-data-in' type='checkbox'><label for='data-3be18dff-f325-4cde-b591-d6382fa3c5a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Tendency of rain water due to sedimentation</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SMAXACT</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-2f0aa601-4b93-4f2e-bc44-20ca5b22986a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f0aa601-4b93-4f2e-bc44-20ca5b22986a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba969d30-e170-48bb-a75e-c2ae20438950' class='xr-var-data-in' type='checkbox'><label for='data-ba969d30-e170-48bb-a75e-c2ae20438950' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Maximum supersaturation in Morrison microphysics</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RMINACT</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-6c804996-2753-409d-b14a-739a48852393' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c804996-2753-409d-b14a-739a48852393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d854bb8-c429-403c-aa45-e88931be852b' class='xr-var-data-in' type='checkbox'><label for='data-7d854bb8-c429-403c-aa45-e88931be852b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Minimum activated radius in Morrison microphysics</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LANDMASK</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-ae632b77-d121-4483-8e0a-940290bfb09b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae632b77-d121-4483-8e0a-940290bfb09b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13eb4163-1b57-41ef-86eb-81d66477e8a6' class='xr-var-data-in' type='checkbox'><label for='data-13eb4163-1b57-41ef-86eb-81d66477e8a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LAND MASK (1 FOR LAND, 0 FOR WATER)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LAKEMASK</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-0d1aaea2-5342-4379-9789-a3a2e9c53825' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d1aaea2-5342-4379-9789-a3a2e9c53825' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-152160a3-3d74-42a4-8041-c171bab7dc82' class='xr-var-data-in' type='checkbox'><label for='data-152160a3-3d74-42a4-8041-c171bab7dc82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>LAKE MASK (1 FOR LAKE, 0 FOR NON-LAKE)</dd><dt><span>units :</span></dt><dd></dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SST</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-1c494101-94d8-4d28-bc48-dafcb003fd3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c494101-94d8-4d28-bc48-dafcb003fd3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-673dd07a-fdfd-4377-a7ae-1109484125f9' class='xr-var-data-in' type='checkbox'><label for='data-673dd07a-fdfd-4377-a7ae-1109484125f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SEA SURFACE TEMPERATURE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SST_INPUT</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-323640b1-31eb-4ce8-b8a0-56ddbc43df8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-323640b1-31eb-4ce8-b8a0-56ddbc43df8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be66ef47-1fd5-444a-be90-ee3960035e94' class='xr-var-data-in' type='checkbox'><label for='data-be66ef47-1fd5-444a-be90-ee3960035e94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>SEA SURFACE TEMPERATURE FROM WRFLOWINPUT FILE</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 33 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_potential_temperature</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-fd328dc3-cfd1-4ce7-a6cb-4790372b76ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd328dc3-cfd1-4ce7-a6cb-4790372b76ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b62110e-e4bf-4e00-ae02-acb5d217e372' class='xr-var-data-in' type='checkbox'><label for='data-0b62110e-e4bf-4e00-ae02-acb5d217e372' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>standard_name :</span></dt><dd>air_potential_temperature</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 34 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_pressure</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-8325780f-000f-4487-86f0-ed8274d40ba1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8325780f-000f-4487-86f0-ed8274d40ba1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-797f5325-7350-41bf-916e-5d6776facb06' class='xr-var-data-in' type='checkbox'><label for='data-797f5325-7350-41bf-916e-5d6776facb06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>Pa</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 67 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geopotential</span></div><div class='xr-var-dims'>(Time, z_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 227, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b1003c9a-74d5-4607-bfd2-b749cc9b4a8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1003c9a-74d5-4607-bfd2-b749cc9b4a8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-210f5938-a718-4f49-854e-feaedb00e81c' class='xr-var-data-in' type='checkbox'><label for='data-210f5938-a718-4f49-854e-feaedb00e81c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.81 GiB </td>\n",
+       "                        <td> 13.53 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 227, 250, 250) </td>\n",
+       "                        <td> (1, 227, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 67 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geopotential_height</span></div><div class='xr-var-dims'>(Time, z_stag, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 227, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-683f245c-1862-4944-9dd7-c02f2ff3a9ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-683f245c-1862-4944-9dd7-c02f2ff3a9ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1336e51-b0e5-407f-9cd7-3cd47b1970b1' class='xr-var-data-in' type='checkbox'><label for='data-f1336e51-b0e5-407f-9cd7-3cd47b1970b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>geopotential_height</dd><dt><span>stagger :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.81 GiB </td>\n",
+       "                        <td> 13.53 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 227, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 68 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_east</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-46b7c648-21fb-44dd-8c7d-3617f03a0643' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46b7c648-21fb-44dd-8c7d-3617f03a0643' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1405a40a-a532-4a27-add2-eb7a9ac62647' class='xr-var-data-in' type='checkbox'><label for='data-1405a40a-a532-4a27-add2-eb7a9ac62647' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative x-wind component</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wrf_projection</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 819 chunks in 155 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_north</span></div><div class='xr-var-dims'>(Time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 226, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-b483781b-c6e6-4635-b9b4-733810383774' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b483781b-c6e6-4635-b9b4-733810383774' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc9f8ada-26aa-47fa-bc7c-bc0441177c81' class='xr-var-data-in' type='checkbox'><label for='data-fc9f8ada-26aa-47fa-bc7c-bc0441177c81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative y-wind component</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wrf_projection</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.79 GiB </td>\n",
+       "                        <td> 13.47 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 226, 250, 250) </td>\n",
+       "                        <td> (1, 226, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 819 chunks in 155 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_east_10</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-3321b28c-f1ee-436f-a300-4e9f75d6f21b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3321b28c-f1ee-436f-a300-4e9f75d6f21b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-789dd2c7-e923-4d99-9ab2-ce5e0d8a5c3b' class='xr-var-data-in' type='checkbox'><label for='data-789dd2c7-e923-4d99-9ab2-ce5e0d8a5c3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative 10m x-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wrf_projection</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 135 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_north_10</span></div><div class='xr-var-dims'>(Time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 125, 125), meta=np.ndarray&gt;</div><input id='attrs-c163143f-9307-444f-89e5-fdf927e2ec96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c163143f-9307-444f-89e5-fdf927e2ec96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aed7a29-bd98-4707-b4f8-f7a512054ca6' class='xr-var-data-in' type='checkbox'><label for='data-9aed7a29-bd98-4707-b4f8-f7a512054ca6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative 10m y-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wrf_projection</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 21.70 MiB </td>\n",
+       "                        <td> 61.04 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91, 250, 250) </td>\n",
+       "                        <td> (1, 125, 125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 364 chunks in 135 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"205\" height=\"195\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-840b16fa-7cdc-48b6-92a3-e0b3d8cd49b2' class='xr-section-summary-in' type='checkbox'  ><label for='section-840b16fa-7cdc-48b6-92a3-e0b3d8cd49b2' class='xr-section-summary' >Indexes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5eab9929-db35-4a2c-9234-3f88483756e9' class='xr-index-data-in' type='checkbox'/><label for='index-5eab9929-db35-4a2c-9234-3f88483756e9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.9979435801506042,    0.9938474893569946,    0.9897772073745728,\n",
+       "          0.9857232570648193,    0.9816849231719971,    0.9776613116264343,\n",
+       "          0.9736500978469849,    0.9696520566940308,    0.9656686782836914,\n",
+       "          0.9616985321044922,\n",
+       "       ...\n",
+       "         0.07436569780111313,   0.06433471292257309,  0.054764386266469955,\n",
+       "         0.04565010964870453,   0.03694861754775047,   0.02862708829343319,\n",
+       "        0.020665660500526428,  0.013039967976510525,  0.005742852110415697,\n",
+       "       0.0010888208635151386],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;z&#x27;, length=226))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z_stag</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b46300a6-2e4b-482e-be5c-827ad7daad04' class='xr-index-data-in' type='checkbox'/><label for='index-b46300a6-2e4b-482e-be5c-827ad7daad04' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                  1.0,    0.9958871603012085,    0.9918078780174255,\n",
+       "            0.98774653673172,    0.9837000370025635,    0.9796698093414307,\n",
+       "           0.975652813911438,    0.9716474413871765,    0.9676567316055298,\n",
+       "          0.9636806845664978,\n",
+       "       ...\n",
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+       "         0.04120250791311264,  0.032694727182388306,  0.024559449404478073,\n",
+       "        0.016771873459219933,  0.009308062493801117, 0.0021776417270302773,\n",
+       "                         0.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;z_stag&#x27;, length=227))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-da5ffe0c-1198-469e-97b9-b31262762182' class='xr-index-data-in' type='checkbox'/><label for='index-da5ffe0c-1198-469e-97b9-b31262762182' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-04-04 12:00:00&#x27;, &#x27;2019-04-04 12:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 12:20:00&#x27;, &#x27;2019-04-04 12:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 12:40:00&#x27;, &#x27;2019-04-04 12:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 13:00:00&#x27;, &#x27;2019-04-04 13:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 13:20:00&#x27;, &#x27;2019-04-04 13:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 13:40:00&#x27;, &#x27;2019-04-04 13:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 14:00:00&#x27;, &#x27;2019-04-04 14:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 14:20:00&#x27;, &#x27;2019-04-04 14:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 14:40:00&#x27;, &#x27;2019-04-04 14:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 15:00:00&#x27;, &#x27;2019-04-04 15:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 15:20:00&#x27;, &#x27;2019-04-04 15:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 15:40:00&#x27;, &#x27;2019-04-04 15:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 16:00:00&#x27;, &#x27;2019-04-04 16:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 16:20:00&#x27;, &#x27;2019-04-04 16:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 16:40:00&#x27;, &#x27;2019-04-04 16:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 17:00:00&#x27;, &#x27;2019-04-04 17:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 17:20:00&#x27;, &#x27;2019-04-04 17:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 17:40:00&#x27;, &#x27;2019-04-04 17:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 18:00:00&#x27;, &#x27;2019-04-04 18:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 18:20:00&#x27;, &#x27;2019-04-04 18:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 18:40:00&#x27;, &#x27;2019-04-04 18:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 19:00:00&#x27;, &#x27;2019-04-04 19:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 19:20:00&#x27;, &#x27;2019-04-04 19:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 19:40:00&#x27;, &#x27;2019-04-04 19:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 20:00:00&#x27;, &#x27;2019-04-04 20:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 20:20:00&#x27;, &#x27;2019-04-04 20:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 20:40:00&#x27;, &#x27;2019-04-04 20:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 21:00:00&#x27;, &#x27;2019-04-04 21:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 21:20:00&#x27;, &#x27;2019-04-04 21:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 21:40:00&#x27;, &#x27;2019-04-04 21:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 22:00:00&#x27;, &#x27;2019-04-04 22:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 22:20:00&#x27;, &#x27;2019-04-04 22:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 22:40:00&#x27;, &#x27;2019-04-04 22:50:00&#x27;,\n",
+       "               &#x27;2019-04-04 23:00:00&#x27;, &#x27;2019-04-04 23:10:00&#x27;,\n",
+       "               &#x27;2019-04-04 23:20:00&#x27;, &#x27;2019-04-04 23:30:00&#x27;,\n",
+       "               &#x27;2019-04-04 23:40:00&#x27;, &#x27;2019-04-04 23:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 00:00:00&#x27;, &#x27;2019-04-05 00:10:00&#x27;,\n",
+       "               &#x27;2019-04-05 00:20:00&#x27;, &#x27;2019-04-05 00:30:00&#x27;,\n",
+       "               &#x27;2019-04-05 00:40:00&#x27;, &#x27;2019-04-05 00:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 01:00:00&#x27;, &#x27;2019-04-05 01:10:00&#x27;,\n",
+       "               &#x27;2019-04-05 01:20:00&#x27;, &#x27;2019-04-05 01:30:00&#x27;,\n",
+       "               &#x27;2019-04-05 01:40:00&#x27;, &#x27;2019-04-05 01:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 02:00:00&#x27;, &#x27;2019-04-05 02:10:00&#x27;,\n",
+       "               &#x27;2019-04-05 02:20:00&#x27;, &#x27;2019-04-05 02:30:00&#x27;,\n",
+       "               &#x27;2019-04-05 02:40:00&#x27;, &#x27;2019-04-05 02:50:00&#x27;,\n",
+       "               &#x27;2019-04-05 03:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_stag</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d1846288-2570-48ec-8f65-d87f9a165387' class='xr-index-data-in' type='checkbox'/><label for='index-d1846288-2570-48ec-8f65-d87f9a165387' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12500.0, -12400.0, -12300.0, -12200.0, -12100.0, -12000.0, -11900.0,\n",
+       "       -11800.0, -11700.0, -11600.0,\n",
+       "       ...\n",
+       "        11600.0,  11700.0,  11800.0,  11900.0,  12000.0,  12100.0,  12200.0,\n",
+       "        12300.0,  12400.0,  12500.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y_stag&#x27;, length=251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-44dbe0d6-f383-43e8-aaec-a98de908b8bb' class='xr-index-data-in' type='checkbox'/><label for='index-44dbe0d6-f383-43e8-aaec-a98de908b8bb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12450.0, -12350.0, -12250.0, -12150.0, -12050.0, -11950.0, -11850.0,\n",
+       "       -11750.0, -11650.0, -11550.0,\n",
+       "       ...\n",
+       "        11550.0,  11650.0,  11750.0,  11850.0,  11950.0,  12050.0,  12150.0,\n",
+       "        12250.0,  12350.0,  12450.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=250))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c789a085-adde-4e2c-93e3-c002a9aaa8f2' class='xr-index-data-in' type='checkbox'/><label for='index-c789a085-adde-4e2c-93e3-c002a9aaa8f2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12450.0, -12350.0, -12250.0, -12150.0, -12050.0, -11950.0, -11850.0,\n",
+       "       -11750.0, -11650.0, -11550.0,\n",
+       "       ...\n",
+       "        11550.0,  11650.0,  11750.0,  11850.0,  11950.0,  12050.0,  12150.0,\n",
+       "        12250.0,  12350.0,  12450.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=250))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x_stag</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2ed5efba-f27a-474a-a5a2-47a2fd945c38' class='xr-index-data-in' type='checkbox'/><label for='index-2ed5efba-f27a-474a-a5a2-47a2fd945c38' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12500.0, -12400.0, -12300.0, -12200.0, -12100.0, -12000.0, -11900.0,\n",
+       "       -11800.0, -11700.0, -11600.0,\n",
+       "       ...\n",
+       "        11600.0,  11700.0,  11800.0,  11900.0,  12000.0,  12100.0,  12200.0,\n",
+       "        12300.0,  12400.0,  12500.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x_stag&#x27;, length=251))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b8b6ccb-2549-491f-8f36-316b021dee47' class='xr-section-summary-in' type='checkbox'  ><label for='section-0b8b6ccb-2549-491f-8f36-316b021dee47' class='xr-section-summary' >Attributes: <span>(142)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TITLE :</span></dt><dd> OUTPUT FROM WRF V3.8.1 MODEL</dd><dt><span>START_DATE :</span></dt><dd>2019-04-04_12:00:00</dd><dt><span>SIMULATION_START_DATE :</span></dt><dd>2019-04-04_12:00:00</dd><dt><span>WEST-EAST_GRID_DIMENSION :</span></dt><dd>251</dd><dt><span>SOUTH-NORTH_GRID_DIMENSION :</span></dt><dd>251</dd><dt><span>BOTTOM-TOP_GRID_DIMENSION :</span></dt><dd>227</dd><dt><span>DX :</span></dt><dd>100.0</dd><dt><span>DY :</span></dt><dd>100.0</dd><dt><span>SKEBS_ON :</span></dt><dd>0</dd><dt><span>SPEC_BDY_FINAL_MU :</span></dt><dd>1</dd><dt><span>USE_Q_DIABATIC :</span></dt><dd>0</dd><dt><span>GRIDTYPE :</span></dt><dd>C</dd><dt><span>DIFF_OPT :</span></dt><dd>2</dd><dt><span>KM_OPT :</span></dt><dd>2</dd><dt><span>DAMP_OPT :</span></dt><dd>3</dd><dt><span>DAMPCOEF :</span></dt><dd>0.2</dd><dt><span>KHDIF :</span></dt><dd>1.0</dd><dt><span>KVDIF :</span></dt><dd>1.0</dd><dt><span>MP_PHYSICS :</span></dt><dd>8</dd><dt><span>RA_LW_PHYSICS :</span></dt><dd>4</dd><dt><span>RA_SW_PHYSICS :</span></dt><dd>4</dd><dt><span>SF_SFCLAY_PHYSICS :</span></dt><dd>1</dd><dt><span>SF_SURFACE_PHYSICS :</span></dt><dd>1</dd><dt><span>BL_PBL_PHYSICS :</span></dt><dd>0</dd><dt><span>CU_PHYSICS :</span></dt><dd>0</dd><dt><span>SF_LAKE_PHYSICS :</span></dt><dd>0</dd><dt><span>SURFACE_INPUT_SOURCE :</span></dt><dd>3</dd><dt><span>SST_UPDATE :</span></dt><dd>0</dd><dt><span>GRID_FDDA :</span></dt><dd>0</dd><dt><span>GFDDA_INTERVAL_M :</span></dt><dd>0</dd><dt><span>GFDDA_END_H :</span></dt><dd>0</dd><dt><span>GRID_SFDDA :</span></dt><dd>0</dd><dt><span>SGFDDA_INTERVAL_M :</span></dt><dd>0</dd><dt><span>SGFDDA_END_H :</span></dt><dd>0</dd><dt><span>HYPSOMETRIC_OPT :</span></dt><dd>1</dd><dt><span>USE_THETA_M :</span></dt><dd>1</dd><dt><span>SF_URBAN_PHYSICS :</span></dt><dd>0</dd><dt><span>SHCU_PHYSICS :</span></dt><dd>0</dd><dt><span>MFSHCONV :</span></dt><dd>0</dd><dt><span>FEEDBACK :</span></dt><dd>0</dd><dt><span>SMOOTH_OPTION :</span></dt><dd>0</dd><dt><span>SWRAD_SCAT :</span></dt><dd>1.0</dd><dt><span>W_DAMPING :</span></dt><dd>0</dd><dt><span>RADT :</span></dt><dd>1.0</dd><dt><span>BLDT :</span></dt><dd>0.0</dd><dt><span>CUDT :</span></dt><dd>0.0</dd><dt><span>AER_OPT :</span></dt><dd>0</dd><dt><span>SWINT_OPT :</span></dt><dd>0</dd><dt><span>AER_TYPE :</span></dt><dd>1</dd><dt><span>AER_AOD550_OPT :</span></dt><dd>1</dd><dt><span>AER_ANGEXP_OPT :</span></dt><dd>1</dd><dt><span>AER_SSA_OPT :</span></dt><dd>1</dd><dt><span>AER_ASY_OPT :</span></dt><dd>1</dd><dt><span>AER_AOD550_VAL :</span></dt><dd>0.12</dd><dt><span>AER_ANGEXP_VAL :</span></dt><dd>1.3</dd><dt><span>AER_SSA_VAL :</span></dt><dd>1e-45</dd><dt><span>AER_ASY_VAL :</span></dt><dd>1e-45</dd><dt><span>MOIST_ADV_OPT :</span></dt><dd>2</dd><dt><span>SCALAR_ADV_OPT :</span></dt><dd>2</dd><dt><span>TKE_ADV_OPT :</span></dt><dd>2</dd><dt><span>DIFF_6TH_OPT :</span></dt><dd>0</dd><dt><span>DIFF_6TH_FACTOR :</span></dt><dd>0.12</dd><dt><span>OBS_NUDGE_OPT :</span></dt><dd>0</dd><dt><span>BUCKET_MM :</span></dt><dd>-1.0</dd><dt><span>BUCKET_J :</span></dt><dd>-1.0</dd><dt><span>PREC_ACC_DT :</span></dt><dd>0.0</dd><dt><span>SF_OCEAN_PHYSICS :</span></dt><dd>0</dd><dt><span>ISFTCFLX :</span></dt><dd>0</dd><dt><span>ISHALLOW :</span></dt><dd>0</dd><dt><span>ISFFLX :</span></dt><dd>11</dd><dt><span>ICLOUD :</span></dt><dd>1</dd><dt><span>ICLOUD_CU :</span></dt><dd>0</dd><dt><span>TRACER_PBLMIX :</span></dt><dd>1</dd><dt><span>SCALAR_PBLMIX :</span></dt><dd>0</dd><dt><span>YSU_TOPDOWN_PBLMIX :</span></dt><dd>0</dd><dt><span>GRAV_SETTLING :</span></dt><dd>0</dd><dt><span>DFI_OPT :</span></dt><dd>0</dd><dt><span>SIMULATION_INITIALIZATION_TYPE :</span></dt><dd>IDEALIZED DATA</dd><dt><span>WEST-EAST_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>WEST-EAST_PATCH_END_UNSTAG :</span></dt><dd>250</dd><dt><span>WEST-EAST_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>WEST-EAST_PATCH_END_STAG :</span></dt><dd>251</dd><dt><span>SOUTH-NORTH_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>SOUTH-NORTH_PATCH_END_UNSTAG :</span></dt><dd>250</dd><dt><span>SOUTH-NORTH_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>SOUTH-NORTH_PATCH_END_STAG :</span></dt><dd>251</dd><dt><span>BOTTOM-TOP_PATCH_START_UNSTAG :</span></dt><dd>1</dd><dt><span>BOTTOM-TOP_PATCH_END_UNSTAG :</span></dt><dd>226</dd><dt><span>BOTTOM-TOP_PATCH_START_STAG :</span></dt><dd>1</dd><dt><span>BOTTOM-TOP_PATCH_END_STAG :</span></dt><dd>227</dd><dt><span>GRID_ID :</span></dt><dd>1</dd><dt><span>PARENT_ID :</span></dt><dd>0</dd><dt><span>I_PARENT_START :</span></dt><dd>0</dd><dt><span>J_PARENT_START :</span></dt><dd>0</dd><dt><span>PARENT_GRID_RATIO :</span></dt><dd>1</dd><dt><span>DT :</span></dt><dd>0.5</dd><dt><span>CEN_LAT :</span></dt><dd>0.0</dd><dt><span>CEN_LON :</span></dt><dd>0.0</dd><dt><span>TRUELAT1 :</span></dt><dd>0.0</dd><dt><span>TRUELAT2 :</span></dt><dd>0.0</dd><dt><span>MOAD_CEN_LAT :</span></dt><dd>0.0</dd><dt><span>STAND_LON :</span></dt><dd>0.0</dd><dt><span>POLE_LAT :</span></dt><dd>0.0</dd><dt><span>POLE_LON :</span></dt><dd>0.0</dd><dt><span>GMT :</span></dt><dd>0.0</dd><dt><span>JULYR :</span></dt><dd>0</dd><dt><span>JULDAY :</span></dt><dd>1</dd><dt><span>MAP_PROJ :</span></dt><dd>0</dd><dt><span>MAP_PROJ_CHAR :</span></dt><dd>Cartesian</dd><dt><span>MMINLU :</span></dt><dd></dd><dt><span>NUM_LAND_CAT :</span></dt><dd>21</dd><dt><span>ISWATER :</span></dt><dd>16</dd><dt><span>ISLAKE :</span></dt><dd>0</dd><dt><span>ISICE :</span></dt><dd>0</dd><dt><span>ISURBAN :</span></dt><dd>0</dd><dt><span>ISOILWATER :</span></dt><dd>0</dd><dt><span>doi :</span></dt><dd>10.5439/1342961</dd><dt><span>contacts :</span></dt><dd>lasso@arm.gov, LASSO PI: William Gustafson (William.Gustafson@pnnl.gov), LASSO Co-PI: Andrew Vogelmann (vogelmann@bnl.gov)</dd><dt><span>site_id :</span></dt><dd>sgp</dd><dt><span>facility_id :</span></dt><dd>C1</dd><dt><span>location_description :</span></dt><dd>Southern Great Plains (SGP), Lamont, Oklahoma</dd><dt><span>date :</span></dt><dd>20190404</dd><dt><span>simulation_id_number :</span></dt><dd>4</dd><dt><span>model_type :</span></dt><dd>WRF</dd><dt><span>model_version :</span></dt><dd>3.8.1</dd><dt><span>model_github_hash :</span></dt><dd>b6b6a5cc4229eec1ea9b005746b5ebef2205fb07</dd><dt><span>output_domain_size :</span></dt><dd>25.0 km</dd><dt><span>output_number_of_levels :</span></dt><dd>226</dd><dt><span>output_horizontal_grid_spacing :</span></dt><dd>100 m</dd><dt><span>config_large_scale_forcing :</span></dt><dd>ECMWF</dd><dt><span>config_large_scale_forcing_scale :</span></dt><dd>114 km</dd><dt><span>config_large_scale_forcing_specifics :</span></dt><dd>sgpecmwfvarX1.c1,sgpecmwftenX1.c1,sgpecmwfsfc1lX1.c1,sgpecmwfsfceX1.c1 (v20191206)</dd><dt><span>config_surface_treatment :</span></dt><dd>VARANAL</dd><dt><span>config_surface_treatment_specifics :</span></dt><dd>sgp60varanarapC1.c1 (v20191126)</dd><dt><span>config_initial_condition :</span></dt><dd>Sounding</dd><dt><span>config_initial_condition_specifics :</span></dt><dd>sgpsondewnpnC1</dd><dt><span>config_aerosol :</span></dt><dd>NA</dd><dt><span>config_forecast_time :</span></dt><dd>15.0 h</dd><dt><span>config_boundary_method :</span></dt><dd>Periodic</dd><dt><span>config_microphysics :</span></dt><dd>Thompson (mp_physics=8)</dd><dt><span>config_nickname :</span></dt><dd>runlas20190404v1addhm</dd><dt><span>simulation_origin_host :</span></dt><dd>cumulus-login2.ccs.ornl.gov</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 255GB\n",
+       "Dimensions:                    (Time: 91, y: 250, x: 250, soil_layers_stag: 5,\n",
+       "                                z: 226, x_stag: 251, y_stag: 251, z_stag: 227,\n",
+       "                                force_layers: 751)\n",
+       "Coordinates: (12/15)\n",
+       "    CLAT                       (y, x) float32 250kB dask.array<chunksize=(125, 125), meta=np.ndarray>\n",
+       "    XLAT                       (y, x) float32 250kB dask.array<chunksize=(125, 125), meta=np.ndarray>\n",
+       "    XLONG                      (y, x) float32 250kB dask.array<chunksize=(125, 125), meta=np.ndarray>\n",
+       "    XTIME                      (Time) datetime64[ns] 728B dask.array<chunksize=(6,), meta=np.ndarray>\n",
+       "    XLAT_U                     (y, x_stag) float32 251kB dask.array<chunksize=(125, 126), meta=np.ndarray>\n",
+       "    XLONG_U                    (y, x_stag) float32 251kB dask.array<chunksize=(125, 126), meta=np.ndarray>\n",
+       "    ...                         ...\n",
+       "  * z_stag                     (z_stag) float32 908B 1.0 0.9959 ... 0.002178 0.0\n",
+       "  * Time                       (Time) datetime64[ns] 728B 2019-04-04T12:00:00...\n",
+       "  * y_stag                     (y_stag) float64 2kB -1.25e+04 ... 1.25e+04\n",
+       "  * y                          (y) float64 2kB -1.245e+04 ... 1.245e+04\n",
+       "  * x                          (x) float64 2kB -1.245e+04 ... 1.245e+04\n",
+       "  * x_stag                     (x_stag) float64 2kB -1.25e+04 ... 1.25e+04\n",
+       "Dimensions without coordinates: soil_layers_stag, force_layers\n",
+       "Data variables: (12/251)\n",
+       "    Times                      (Time) |S19 2kB dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    LU_INDEX                   (Time, y, x) float32 23MB dask.array<chunksize=(1, 125, 125), meta=np.ndarray>\n",
+       "    ZS                         (Time, soil_layers_stag) float32 2kB dask.array<chunksize=(1, 5), meta=np.ndarray>\n",
+       "    DZS                        (Time, soil_layers_stag) float32 2kB dask.array<chunksize=(1, 5), meta=np.ndarray>\n",
+       "    VAR_SSO                    (Time, y, x) float32 23MB dask.array<chunksize=(1, 125, 125), meta=np.ndarray>\n",
+       "    U                          (Time, z, y, x_stag) float32 5GB dask.array<chunksize=(1, 226, 125, 126), meta=np.ndarray>\n",
+       "    ...                         ...\n",
+       "    geopotential               (Time, z_stag, y, x) float32 5GB dask.array<chunksize=(1, 227, 125, 125), meta=np.ndarray>\n",
+       "    geopotential_height        (Time, z_stag, y, x) float32 5GB dask.array<chunksize=(1, 227, 125, 125), meta=np.ndarray>\n",
+       "    wind_east                  (Time, z, y, x) float32 5GB dask.array<chunksize=(1, 226, 125, 125), meta=np.ndarray>\n",
+       "    wind_north                 (Time, z, y, x) float32 5GB dask.array<chunksize=(1, 226, 125, 125), meta=np.ndarray>\n",
+       "    wind_east_10               (Time, y, x) float32 23MB dask.array<chunksize=(1, 125, 125), meta=np.ndarray>\n",
+       "    wind_north_10              (Time, y, x) float32 23MB dask.array<chunksize=(1, 125, 125), meta=np.ndarray>\n",
+       "Attributes: (12/142)\n",
+       "    TITLE:                                  OUTPUT FROM WRF V3.8.1 MODEL\n",
+       "    START_DATE:                            2019-04-04_12:00:00\n",
+       "    SIMULATION_START_DATE:                 2019-04-04_12:00:00\n",
+       "    WEST-EAST_GRID_DIMENSION:              251\n",
+       "    SOUTH-NORTH_GRID_DIMENSION:            251\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:             227\n",
+       "    ...                                    ...\n",
+       "    config_aerosol:                        NA\n",
+       "    config_forecast_time:                  15.0 h\n",
+       "    config_boundary_method:                Periodic\n",
+       "    config_microphysics:                   Thompson (mp_physics=8)\n",
+       "    config_nickname:                       runlas20190404v1addhm\n",
+       "    simulation_origin_host:                cumulus-login2.ccs.ornl.gov"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}