diff --git a/notebooks/c05_Big_Data/Working_with_Big_Data.ipynb b/notebooks/c05_Big_Data/Working_with_Big_Data.ipynb
index b0c8b0a..24fccdf 100644
--- a/notebooks/c05_Big_Data/Working_with_Big_Data.ipynb
+++ b/notebooks/c05_Big_Data/Working_with_Big_Data.ipynb
@@ -2001,7 +2001,8 @@
     "ExecuteTime": {
      "end_time": "2020-10-04T07:14:15.680101Z",
      "start_time": "2020-10-04T07:14:15.607250Z"
-    }
+    },
+    "scrolled": true
    },
    "outputs": [
     {
@@ -2073,7 +2074,8 @@
     "ExecuteTime": {
      "end_time": "2020-10-04T07:14:15.702891Z",
      "start_time": "2020-10-04T07:14:15.683207Z"
-    }
+    },
+    "scrolled": true
    },
    "outputs": [
     {
@@ -2275,11 +2277,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T08:10:18.705068Z",
-     "start_time": "2020-10-12T08:10:18.539317Z"
+     "end_time": "2020-10-13T11:22:29.816836Z",
+     "start_time": "2020-10-13T11:22:28.707555Z"
     }
    },
    "outputs": [
@@ -2656,42 +2658,42 @@
        "    comment:                   Output from the Variable Infiltration Capacity...\n",
        "    nco_openmp_thread_number:  1\n",
        "    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\n",
-       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...</pre><div class='xr-wrap' hidden><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-120f486b-5721-4a12-87f8-c67c02513717' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-120f486b-5721-4a12-87f8-c67c02513717' 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>: 36</li><li><span>x</span>: 275</li><li><span>y</span>: 205</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c4cf9255-4510-429d-8dd6-de1cd6ad3851' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c4cf9255-4510-429d-8dd6-de1cd6ad3851' class='xr-section-summary' >Coordinates: <span>(3)</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'>object</div><div class='xr-var-preview xr-preview'>1980-09-16 12:00:00 ... 1983-08-...</div><input id='attrs-967165a5-4c45-4382-8c03-13266036ec17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-967165a5-4c45-4382-8c03-13266036ec17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dc01eb0-9163-46cc-903d-843954f4534c' class='xr-var-data-in' type='checkbox'><label for='data-0dc01eb0-9163-46cc-903d-843954f4534c' title='Show/Hide data repr'><svg 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>type_preferred :</span></dt><dd>int</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1980, 9, 16, 12, 0, 0, 0, 5, 259),\n",
-       "       cftime.DatetimeNoLeap(1980, 10, 17, 0, 0, 0, 0, 1, 290),\n",
-       "       cftime.DatetimeNoLeap(1980, 11, 16, 12, 0, 0, 0, 3, 320),\n",
-       "       cftime.DatetimeNoLeap(1980, 12, 17, 0, 0, 0, 0, 6, 351),\n",
-       "       cftime.DatetimeNoLeap(1981, 1, 17, 0, 0, 0, 0, 2, 17),\n",
-       "       cftime.DatetimeNoLeap(1981, 2, 15, 12, 0, 0, 0, 3, 46),\n",
-       "       cftime.DatetimeNoLeap(1981, 3, 17, 0, 0, 0, 0, 5, 76),\n",
-       "       cftime.DatetimeNoLeap(1981, 4, 16, 12, 0, 0, 0, 0, 106),\n",
-       "       cftime.DatetimeNoLeap(1981, 5, 17, 0, 0, 0, 0, 3, 137),\n",
-       "       cftime.DatetimeNoLeap(1981, 6, 16, 12, 0, 0, 0, 5, 167),\n",
-       "       cftime.DatetimeNoLeap(1981, 7, 17, 0, 0, 0, 0, 1, 198),\n",
-       "       cftime.DatetimeNoLeap(1981, 8, 17, 0, 0, 0, 0, 4, 229),\n",
-       "       cftime.DatetimeNoLeap(1981, 9, 16, 12, 0, 0, 0, 6, 259),\n",
-       "       cftime.DatetimeNoLeap(1981, 10, 17, 0, 0, 0, 0, 2, 290),\n",
-       "       cftime.DatetimeNoLeap(1981, 11, 16, 12, 0, 0, 0, 4, 320),\n",
-       "       cftime.DatetimeNoLeap(1981, 12, 17, 0, 0, 0, 0, 0, 351),\n",
-       "       cftime.DatetimeNoLeap(1982, 1, 17, 0, 0, 0, 0, 3, 17),\n",
-       "       cftime.DatetimeNoLeap(1982, 2, 15, 12, 0, 0, 0, 4, 46),\n",
-       "       cftime.DatetimeNoLeap(1982, 3, 17, 0, 0, 0, 0, 6, 76),\n",
-       "       cftime.DatetimeNoLeap(1982, 4, 16, 12, 0, 0, 0, 1, 106),\n",
-       "       cftime.DatetimeNoLeap(1982, 5, 17, 0, 0, 0, 0, 4, 137),\n",
-       "       cftime.DatetimeNoLeap(1982, 6, 16, 12, 0, 0, 0, 6, 167),\n",
-       "       cftime.DatetimeNoLeap(1982, 7, 17, 0, 0, 0, 0, 2, 198),\n",
-       "       cftime.DatetimeNoLeap(1982, 8, 17, 0, 0, 0, 0, 5, 229),\n",
-       "       cftime.DatetimeNoLeap(1982, 9, 16, 12, 0, 0, 0, 0, 259),\n",
-       "       cftime.DatetimeNoLeap(1982, 10, 17, 0, 0, 0, 0, 3, 290),\n",
-       "       cftime.DatetimeNoLeap(1982, 11, 16, 12, 0, 0, 0, 5, 320),\n",
-       "       cftime.DatetimeNoLeap(1982, 12, 17, 0, 0, 0, 0, 1, 351),\n",
-       "       cftime.DatetimeNoLeap(1983, 1, 17, 0, 0, 0, 0, 4, 17),\n",
-       "       cftime.DatetimeNoLeap(1983, 2, 15, 12, 0, 0, 0, 5, 46),\n",
-       "       cftime.DatetimeNoLeap(1983, 3, 17, 0, 0, 0, 0, 0, 76),\n",
-       "       cftime.DatetimeNoLeap(1983, 4, 16, 12, 0, 0, 0, 2, 106),\n",
-       "       cftime.DatetimeNoLeap(1983, 5, 17, 0, 0, 0, 0, 5, 137),\n",
-       "       cftime.DatetimeNoLeap(1983, 6, 16, 12, 0, 0, 0, 0, 167),\n",
-       "       cftime.DatetimeNoLeap(1983, 7, 17, 0, 0, 0, 0, 3, 198),\n",
-       "       cftime.DatetimeNoLeap(1983, 8, 17, 0, 0, 0, 0, 6, 229)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-779c662c-855a-4ef6-8ef4-1341a7ead05d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-779c662c-855a-4ef6-8ef4-1341a7ead05d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3995eec8-002a-47d0-858f-78d17312aadb' class='xr-var-data-in' type='checkbox'><label for='data-3995eec8-002a-47d0-858f-78d17312aadb' title='Show/Hide data repr'><svg 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>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...</pre><div class='xr-wrap' hidden><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-71bd86b0-0d26-48c3-86e3-fc0ec215c049' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-71bd86b0-0d26-48c3-86e3-fc0ec215c049' 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>: 36</li><li><span>x</span>: 275</li><li><span>y</span>: 205</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3defb812-3e53-4ab0-b5df-7e2369b8e53c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3defb812-3e53-4ab0-b5df-7e2369b8e53c' class='xr-section-summary' >Coordinates: <span>(3)</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'>object</div><div class='xr-var-preview xr-preview'>1980-09-16 12:00:00 ... 1983-08-...</div><input id='attrs-99381d9b-458a-488f-be4a-c856326131f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99381d9b-458a-488f-be4a-c856326131f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e19509df-b8ab-4db0-ad50-6e54852ef813' class='xr-var-data-in' type='checkbox'><label for='data-e19509df-b8ab-4db0-ad50-6e54852ef813' title='Show/Hide data repr'><svg 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>type_preferred :</span></dt><dd>int</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1980, 9, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1980, 10, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1980, 11, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1980, 12, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 1, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 2, 15, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 3, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 4, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 5, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 6, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 7, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 8, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 9, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 10, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 11, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 12, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 1, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 2, 15, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 3, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 4, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 5, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 6, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 7, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 8, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 9, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 10, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 11, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 12, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1983, 1, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1983, 2, 15, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1983, 3, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1983, 4, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1983, 5, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1983, 6, 16, 12, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1983, 7, 17, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1983, 8, 17, 0, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-25f6c78e-b612-4e65-b1ad-06a044d88439' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25f6c78e-b612-4e65-b1ad-06a044d88439' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81110fb3-5529-41fe-bbf3-6616645334fb' class='xr-var-data-in' type='checkbox'><label for='data-81110fb3-5529-41fe-bbf3-6616645334fb' title='Show/Hide data repr'><svg 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>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -2726,7 +2728,7 @@
        "</svg>\n",
        "</td>\n",
        "</tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-633c2140-2500-4219-b219-4bc583a0267a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-633c2140-2500-4219-b219-4bc583a0267a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6efb530b-a902-46d9-8394-8481a16e9198' class='xr-var-data-in' type='checkbox'><label for='data-6efb530b-a902-46d9-8394-8481a16e9198' title='Show/Hide data repr'><svg 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>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-5db8833f-34ab-457e-b7c3-7f8877823947' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5db8833f-34ab-457e-b7c3-7f8877823947' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae49f35c-5dd1-4eac-833c-7f8273655569' class='xr-var-data-in' type='checkbox'><label for='data-ae49f35c-5dd1-4eac-833c-7f8273655569' title='Show/Hide data repr'><svg 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>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -2761,7 +2763,7 @@
        "</svg>\n",
        "</td>\n",
        "</tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6d9f68b-4e40-4f47-ad4e-f7ac86ef6f47' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6d9f68b-4e40-4f47-ad4e-f7ac86ef6f47' class='xr-section-summary' >Data variables: <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>Tair</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10, 205, 275), meta=np.ndarray&gt;</div><input id='attrs-fcb2ee79-e28b-4496-a457-5a41f6c75c48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcb2ee79-e28b-4496-a457-5a41f6c75c48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c7738f6-e9a3-42c7-a91e-65c333827f8c' class='xr-var-data-in' type='checkbox'><label for='data-1c7738f6-e9a3-42c7-a91e-65c333827f8c' title='Show/Hide data repr'><svg 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>C</dd><dt><span>long_name :</span></dt><dd>Surface air temperature</dd><dt><span>type_preferred :</span></dt><dd>double</dd><dt><span>time_rep :</span></dt><dd>instantaneous</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b32621bb-61e7-4f92-8f3b-a76ceabb7065' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b32621bb-61e7-4f92-8f3b-a76ceabb7065' class='xr-section-summary' >Data variables: <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>Tair</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10, 205, 275), meta=np.ndarray&gt;</div><input id='attrs-83cdd983-8701-43dd-beb6-47b0bf270a88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83cdd983-8701-43dd-beb6-47b0bf270a88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30e78dba-59f7-4aa2-9613-5715a7d87641' class='xr-var-data-in' type='checkbox'><label for='data-30e78dba-59f7-4aa2-9613-5715a7d87641' title='Show/Hide data repr'><svg 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>C</dd><dt><span>long_name :</span></dt><dd>Surface air temperature</dd><dt><span>type_preferred :</span></dt><dd>double</dd><dt><span>time_rep :</span></dt><dd>instantaneous</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -2825,7 +2827,7 @@
        "</svg>\n",
        "</td>\n",
        "</tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0707da92-158d-4fe1-8008-082f3a5dd12a' class='xr-section-summary-in' type='checkbox'  ><label for='section-0707da92-158d-4fe1-8008-082f3a5dd12a' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>/workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc</dd><dt><span>institution :</span></dt><dd>U.W.</dd><dt><span>source :</span></dt><dd>RACM R1002RBRxaaa01a</dd><dt><span>output_frequency :</span></dt><dd>daily</dd><dt><span>output_mode :</span></dt><dd>averaged</dd><dt><span>convention :</span></dt><dd>CF-1.4</dd><dt><span>references :</span></dt><dd>Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.</dd><dt><span>comment :</span></dt><dd>Output from the Variable Infiltration Capacity (VIC) model.</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>history :</span></dt><dd>Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d,, rasm.nc\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fdc2c6ac-85c3-4b5b-a6e6-beaab62f0c59' class='xr-section-summary-in' type='checkbox'  ><label for='section-fdc2c6ac-85c3-4b5b-a6e6-beaab62f0c59' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>/workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc</dd><dt><span>institution :</span></dt><dd>U.W.</dd><dt><span>source :</span></dt><dd>RACM R1002RBRxaaa01a</dd><dt><span>output_frequency :</span></dt><dd>daily</dd><dt><span>output_mode :</span></dt><dd>averaged</dd><dt><span>convention :</span></dt><dd>CF-1.4</dd><dt><span>references :</span></dt><dd>Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.</dd><dt><span>comment :</span></dt><dd>Output from the Variable Infiltration Capacity (VIC) model.</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>history :</span></dt><dd>Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d,, rasm.nc\n",
        "Tue Dec 27 14:15:22 2016: ncatted -a dimensions,,d,, rasm.nc rasm.nc\n",
        "Tue Dec 27 13:38:40 2016: ncks -3 rasm.nc rasm.nc\n",
        "history deleted for brevity</dd></dl></div></li></ul></div></div>"
@@ -2854,7 +2856,7 @@
        "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d..."
       ]
      },
-     "execution_count": 27,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2872,11 +2874,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T08:14:21.253683Z",
-     "start_time": "2020-10-12T08:14:20.618495Z"
+     "end_time": "2020-10-13T11:22:30.194756Z",
+     "start_time": "2020-10-13T11:22:29.818290Z"
     }
    },
    "outputs": [
@@ -3253,7 +3255,7 @@
        "    comment:                   Output from the Variable Infiltration Capacity...\n",
        "    nco_openmp_thread_number:  1\n",
        "    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\n",
-       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...</pre><div class='xr-wrap' hidden><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-33c97cf6-92fe-4df6-b917-36ff815ee83c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-33c97cf6-92fe-4df6-b917-36ff815ee83c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 275</li><li><span>y</span>: 205</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-60b1e987-3b09-455e-969f-21e34c4617ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-60b1e987-3b09-455e-969f-21e34c4617ce' class='xr-section-summary' >Coordinates: <span>(3)</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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1981-07-17 00:00:00</div><input id='attrs-5b69636b-05a5-45d1-86e3-e73c9b1d0557' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b69636b-05a5-45d1-86e3-e73c9b1d0557' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a70bc568-0f32-4b6d-8799-8b037c06b47c' class='xr-var-data-in' type='checkbox'><label for='data-a70bc568-0f32-4b6d-8799-8b037c06b47c' title='Show/Hide data repr'><svg 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>type_preferred :</span></dt><dd>int</dd></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(1981, 7, 17, 0, 0, 0, 0, 1, 198), dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-c4a91d6b-afec-4766-828f-cfebc2b34fd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4a91d6b-afec-4766-828f-cfebc2b34fd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3391e69c-d594-4b35-ba22-adcb4526162d' class='xr-var-data-in' type='checkbox'><label for='data-3391e69c-d594-4b35-ba22-adcb4526162d' title='Show/Hide data repr'><svg 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>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...</pre><div class='xr-wrap' hidden><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-9be7b3be-ba18-472e-9607-d355f529f580' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9be7b3be-ba18-472e-9607-d355f529f580' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 275</li><li><span>y</span>: 205</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b96a0bb4-a0d8-4ac1-94a3-25f6c6929eab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b96a0bb4-a0d8-4ac1-94a3-25f6c6929eab' class='xr-section-summary' >Coordinates: <span>(3)</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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1981-07-17 00:00:00</div><input id='attrs-11f4aeab-35fb-46a6-a488-57c2e04b5fe5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11f4aeab-35fb-46a6-a488-57c2e04b5fe5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72a61709-f85c-4115-92af-b84289150d20' class='xr-var-data-in' type='checkbox'><label for='data-72a61709-f85c-4115-92af-b84289150d20' title='Show/Hide data repr'><svg 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>type_preferred :</span></dt><dd>int</dd></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(1981, 7, 17, 0, 0, 0, 0), dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-9b15c354-c0fe-47bb-8ba2-caf5c51c9136' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b15c354-c0fe-47bb-8ba2-caf5c51c9136' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfe975d3-fbc4-420c-a3ea-bb524af62e27' class='xr-var-data-in' type='checkbox'><label for='data-dfe975d3-fbc4-420c-a3ea-bb524af62e27' title='Show/Hide data repr'><svg 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>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -3288,7 +3290,7 @@
        "</svg>\n",
        "</td>\n",
        "</tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-397cba3b-4460-4341-8523-d8ed6d8298c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-397cba3b-4460-4341-8523-d8ed6d8298c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2bf4974-805e-49dd-ab86-fc3efcc9b821' class='xr-var-data-in' type='checkbox'><label for='data-d2bf4974-805e-49dd-ab86-fc3efcc9b821' title='Show/Hide data repr'><svg 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>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-0bd6e816-2efc-41cb-98f7-c7b51f681bff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bd6e816-2efc-41cb-98f7-c7b51f681bff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05baa348-2b16-4f07-b946-588cdf8de4f8' class='xr-var-data-in' type='checkbox'><label for='data-05baa348-2b16-4f07-b946-588cdf8de4f8' title='Show/Hide data repr'><svg 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>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -3323,7 +3325,7 @@
        "</svg>\n",
        "</td>\n",
        "</tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-57270d3e-bf67-43e7-b9fc-dffa171276ff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57270d3e-bf67-43e7-b9fc-dffa171276ff' class='xr-section-summary' >Data variables: <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>Tair</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-d01a4a2e-d2ca-4e8d-8f16-7cb16d1c3b53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d01a4a2e-d2ca-4e8d-8f16-7cb16d1c3b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39acb83b-e1e3-44c6-bb07-de057fd761ab' class='xr-var-data-in' type='checkbox'><label for='data-39acb83b-e1e3-44c6-bb07-de057fd761ab' title='Show/Hide data repr'><svg 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>C</dd><dt><span>long_name :</span></dt><dd>Surface air temperature</dd><dt><span>type_preferred :</span></dt><dd>double</dd><dt><span>time_rep :</span></dt><dd>instantaneous</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2bb6be4-b5e8-4465-ae2f-b0d829d87751' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a2bb6be4-b5e8-4465-ae2f-b0d829d87751' class='xr-section-summary' >Data variables: <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>Tair</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-8978b6fa-6e09-4fbd-ad88-c9bc4d7167a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8978b6fa-6e09-4fbd-ad88-c9bc4d7167a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-630e2b4c-2c15-4e14-ad6a-c68d5343fdb1' class='xr-var-data-in' type='checkbox'><label for='data-630e2b4c-2c15-4e14-ad6a-c68d5343fdb1' title='Show/Hide data repr'><svg 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>C</dd><dt><span>long_name :</span></dt><dd>Surface air temperature</dd><dt><span>type_preferred :</span></dt><dd>double</dd><dt><span>time_rep :</span></dt><dd>instantaneous</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -3358,7 +3360,7 @@
        "</svg>\n",
        "</td>\n",
        "</tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-33a269fd-9c19-47f6-932b-74cb39731485' class='xr-section-summary-in' type='checkbox'  ><label for='section-33a269fd-9c19-47f6-932b-74cb39731485' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>/workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc</dd><dt><span>institution :</span></dt><dd>U.W.</dd><dt><span>source :</span></dt><dd>RACM R1002RBRxaaa01a</dd><dt><span>output_frequency :</span></dt><dd>daily</dd><dt><span>output_mode :</span></dt><dd>averaged</dd><dt><span>convention :</span></dt><dd>CF-1.4</dd><dt><span>references :</span></dt><dd>Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.</dd><dt><span>comment :</span></dt><dd>Output from the Variable Infiltration Capacity (VIC) model.</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>history :</span></dt><dd>Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d,, rasm.nc\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-02430aee-3783-4427-98e1-d4a2e6682281' class='xr-section-summary-in' type='checkbox'  ><label for='section-02430aee-3783-4427-98e1-d4a2e6682281' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>/workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc</dd><dt><span>institution :</span></dt><dd>U.W.</dd><dt><span>source :</span></dt><dd>RACM R1002RBRxaaa01a</dd><dt><span>output_frequency :</span></dt><dd>daily</dd><dt><span>output_mode :</span></dt><dd>averaged</dd><dt><span>convention :</span></dt><dd>CF-1.4</dd><dt><span>references :</span></dt><dd>Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.</dd><dt><span>comment :</span></dt><dd>Output from the Variable Infiltration Capacity (VIC) model.</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>history :</span></dt><dd>Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d,, rasm.nc\n",
        "Tue Dec 27 14:15:22 2016: ncatted -a dimensions,,d,, rasm.nc rasm.nc\n",
        "Tue Dec 27 13:38:40 2016: ncks -3 rasm.nc rasm.nc\n",
        "history deleted for brevity</dd></dl></div></li></ul></div></div>"
@@ -3387,13 +3389,13 @@
        "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d..."
       ]
      },
-     "execution_count": 55,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -3415,11 +3417,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T08:14:04.619997Z",
-     "start_time": "2020-10-12T08:14:04.542987Z"
+     "end_time": "2020-10-13T11:22:30.241311Z",
+     "start_time": "2020-10-13T11:22:30.199187Z"
     }
    },
    "outputs": [
@@ -3781,7 +3783,7 @@
        "Coordinates:\n",
        "  * time     (time) object 1980-12-31 00:00:00 ... 1983-12-31 00:00:00\n",
        "    xc       float64 dask.array&lt;chunksize=(), meta=np.ndarray&gt;\n",
-       "    yc       float64 dask.array&lt;chunksize=(), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Tair'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2f664edb-595d-4a72-a101-dc6a4e6bb055' class='xr-array-in' type='checkbox' checked><label for='section-2f664edb-595d-4a72-a101-dc6a4e6bb055' 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>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    yc       float64 dask.array&lt;chunksize=(), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Tair'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7fa72b70-0470-4324-8c73-65c0ffefbee7' class='xr-array-in' type='checkbox' checked><label for='section-7fa72b70-0470-4324-8c73-65c0ffefbee7' 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>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -3819,10 +3821,10 @@
        "</svg>\n",
        "</td>\n",
        "</tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-e41882bb-7452-4f62-998c-6a47fc840417' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e41882bb-7452-4f62-998c-6a47fc840417' class='xr-section-summary' >Coordinates: <span>(3)</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'>object</div><div class='xr-var-preview xr-preview'>1980-12-31 00:00:00 ... 1983-12-...</div><input id='attrs-ed8200bd-cb5c-4f6a-8a32-f05f303928b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed8200bd-cb5c-4f6a-8a32-f05f303928b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a38d6d21-a0a7-4676-a904-9da3b1b5c499' class='xr-var-data-in' type='checkbox'><label for='data-a38d6d21-a0a7-4676-a904-9da3b1b5c499' title='Show/Hide data 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([cftime.DatetimeNoLeap(1980, 12, 31, 0, 0, 0, 0, 6, 365),\n",
-       "       cftime.DatetimeNoLeap(1981, 12, 31, 0, 0, 0, 0, 0, 365),\n",
-       "       cftime.DatetimeNoLeap(1982, 12, 31, 0, 0, 0, 0, 1, 365),\n",
-       "       cftime.DatetimeNoLeap(1983, 12, 31, 0, 0, 0, 0, 2, 365)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-1b9b85f8-132d-4ca3-b52d-fb73f944109e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b9b85f8-132d-4ca3-b52d-fb73f944109e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e013c43f-d1b5-4584-94c6-4c19d2216dc0' class='xr-var-data-in' type='checkbox'><label for='data-e013c43f-d1b5-4584-94c6-4c19d2216dc0' title='Show/Hide data repr'><svg 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>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-e160cc55-787b-4dd0-ac0b-2dee1b8e1042' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e160cc55-787b-4dd0-ac0b-2dee1b8e1042' class='xr-section-summary' >Coordinates: <span>(3)</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'>object</div><div class='xr-var-preview xr-preview'>1980-12-31 00:00:00 ... 1983-12-...</div><input id='attrs-6ca9d880-a92a-44dd-91ee-065d6e597d90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ca9d880-a92a-44dd-91ee-065d6e597d90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c2c8456-c2cd-4536-af59-72eeaf055384' class='xr-var-data-in' type='checkbox'><label for='data-2c2c8456-c2cd-4536-af59-72eeaf055384' title='Show/Hide data 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([cftime.DatetimeNoLeap(1980, 12, 31, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 12, 31, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 12, 31, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1983, 12, 31, 0, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-dfdeed19-53e5-47e0-913e-91e89d15b936' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfdeed19-53e5-47e0-913e-91e89d15b936' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25415565-c9cc-468d-8279-8dfe1761278e' class='xr-var-data-in' type='checkbox'><label for='data-25415565-c9cc-468d-8279-8dfe1761278e' title='Show/Hide data repr'><svg 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>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -3841,7 +3843,7 @@
        "\n",
        "</td>\n",
        "</tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-d686a3a4-6ed6-4ec6-bea1-28ecc7a53149' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d686a3a4-6ed6-4ec6-bea1-28ecc7a53149' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e6cf069-f0ae-41ba-a851-9cdb5ce732fb' class='xr-var-data-in' type='checkbox'><label for='data-3e6cf069-f0ae-41ba-a851-9cdb5ce732fb' title='Show/Hide data repr'><svg 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>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-5baf827d-6f19-459c-9f0f-b6d6acac632f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5baf827d-6f19-459c-9f0f-b6d6acac632f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c39b4e05-b3ee-45a6-bf50-eed45a8e9aed' class='xr-var-data-in' type='checkbox'><label for='data-c39b4e05-b3ee-45a6-bf50-eed45a8e9aed' title='Show/Hide data repr'><svg 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>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
        "<tr>\n",
        "<td>\n",
        "<table>\n",
@@ -3860,7 +3862,7 @@
        "\n",
        "</td>\n",
        "</tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1344c9e-75cb-4249-be0a-67a52713a509' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e1344c9e-75cb-4249-be0a-67a52713a509' 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>"
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2f21f6c6-d67b-4458-bfb4-673ee60b74e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2f21f6c6-d67b-4458-bfb4-673ee60b74e4' 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 'Tair' (time: 4)>\n",
@@ -3871,7 +3873,7 @@
        "    yc       float64 dask.array<chunksize=(), meta=np.ndarray>"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3885,11 +3887,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-12T08:14:13.932955Z",
-     "start_time": "2020-10-12T08:14:13.880801Z"
+     "end_time": "2020-10-13T11:22:30.348149Z",
+     "start_time": "2020-10-13T11:22:30.246775Z"
     }
    },
    "outputs": [
@@ -3897,7 +3899,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/anaconda/envs/py37_pytorch/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
       "  x = np.divide(x1, x2, out)\n"
      ]
     },
@@ -4259,10 +4261,10 @@
        "Coordinates:\n",
        "  * time     (time) object 1980-12-31 00:00:00 ... 1983-12-31 00:00:00\n",
        "    xc       float64 42.47\n",
-       "    yc       float64 44.82</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Tair'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-676ec267-4883-4df3-8d0d-4594236d69e6' class='xr-array-in' type='checkbox' checked><label for='section-676ec267-4883-4df3-8d0d-4594236d69e6' 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>6.757 8.975 10.49 9.599</span></div><div class='xr-array-data'><pre>array([ 6.75662201,  8.97479849, 10.49235584,  9.59892096])</pre></div></div></li><li class='xr-section-item'><input id='section-73481cc5-450a-4d43-a2f0-2dc0c20fbc45' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73481cc5-450a-4d43-a2f0-2dc0c20fbc45' class='xr-section-summary' >Coordinates: <span>(3)</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'>object</div><div class='xr-var-preview xr-preview'>1980-12-31 00:00:00 ... 1983-12-...</div><input id='attrs-33d70350-46d8-4a9c-b8ce-4e7ea56bf916' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33d70350-46d8-4a9c-b8ce-4e7ea56bf916' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2a76b5f-76af-4f96-86e0-8d8bc57aa8d6' class='xr-var-data-in' type='checkbox'><label for='data-d2a76b5f-76af-4f96-86e0-8d8bc57aa8d6' title='Show/Hide data 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([cftime.DatetimeNoLeap(1980, 12, 31, 0, 0, 0, 0, 6, 365),\n",
-       "       cftime.DatetimeNoLeap(1981, 12, 31, 0, 0, 0, 0, 0, 365),\n",
-       "       cftime.DatetimeNoLeap(1982, 12, 31, 0, 0, 0, 0, 1, 365),\n",
-       "       cftime.DatetimeNoLeap(1983, 12, 31, 0, 0, 0, 0, 2, 365)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.47</div><input id='attrs-f7cb2f9c-95fa-47c9-8896-6e9265f84af6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7cb2f9c-95fa-47c9-8896-6e9265f84af6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-732d7dfb-8648-4fc3-b0a9-21c53c393782' class='xr-var-data-in' type='checkbox'><label for='data-732d7dfb-8648-4fc3-b0a9-21c53c393782' title='Show/Hide data repr'><svg 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>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array(42.4748837)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.82</div><input id='attrs-54d167b4-bcf9-4dd7-9311-3f954316d5c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54d167b4-bcf9-4dd7-9311-3f954316d5c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f435bee4-2f08-435c-81ee-8676ac644a41' class='xr-var-data-in' type='checkbox'><label for='data-f435bee4-2f08-435c-81ee-8676ac644a41' title='Show/Hide data repr'><svg 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>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array(44.82000698)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-95b4647a-6596-49bd-a901-37b5d5b17b43' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-95b4647a-6596-49bd-a901-37b5d5b17b43' 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>"
+       "    yc       float64 44.82</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Tair'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f875ced3-d90d-4ff6-a06d-a9105b5ed153' class='xr-array-in' type='checkbox' checked><label for='section-f875ced3-d90d-4ff6-a06d-a9105b5ed153' 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>6.757 8.975 10.49 9.599</span></div><div class='xr-array-data'><pre>array([ 6.75662201,  8.97479849, 10.49235584,  9.59892096])</pre></div></div></li><li class='xr-section-item'><input id='section-74cbe536-306e-4f5d-ab38-32ecf1502478' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74cbe536-306e-4f5d-ab38-32ecf1502478' class='xr-section-summary' >Coordinates: <span>(3)</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'>object</div><div class='xr-var-preview xr-preview'>1980-12-31 00:00:00 ... 1983-12-...</div><input id='attrs-9573f0fc-d370-41d3-8433-2f714750ca99' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9573f0fc-d370-41d3-8433-2f714750ca99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3911fbfd-6c9b-48e6-8d42-387f368f6f36' class='xr-var-data-in' type='checkbox'><label for='data-3911fbfd-6c9b-48e6-8d42-387f368f6f36' title='Show/Hide data 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([cftime.DatetimeNoLeap(1980, 12, 31, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1981, 12, 31, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1982, 12, 31, 0, 0, 0, 0),\n",
+       "       cftime.DatetimeNoLeap(1983, 12, 31, 0, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.47</div><input id='attrs-7a7603d8-395d-4b71-826f-fee9f67c00f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a7603d8-395d-4b71-826f-fee9f67c00f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-858671c9-77dd-446a-96fa-03b64c728fb2' class='xr-var-data-in' type='checkbox'><label for='data-858671c9-77dd-446a-96fa-03b64c728fb2' title='Show/Hide data repr'><svg 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>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array(42.4748837)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.82</div><input id='attrs-e6bf8f83-ad28-4fc5-a175-81dd8731dd95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6bf8f83-ad28-4fc5-a175-81dd8731dd95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3186f079-13e7-4dbe-a459-00ac28e88a4b' class='xr-var-data-in' type='checkbox'><label for='data-3186f079-13e7-4dbe-a459-00ac28e88a4b' title='Show/Hide data repr'><svg 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>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array(44.82000698)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2f0adba8-835e-412a-8684-b387140e578e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2f0adba8-835e-412a-8684-b387140e578e' 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 'Tair' (time: 4)>\n",
@@ -4273,7 +4275,7 @@
        "    yc       float64 44.82"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4283,6 +4285,60 @@
     "res.compute()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-13T11:31:32.414803Z",
+     "start_time": "2020-10-13T11:31:31.326331Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "  <h2>Exercise</h2>\n",
+    "\n",
+    "  1. Look at the output of `ds.time.dt.season`\n",
+    "  2. Try grouping by season and getting the mean\n",
+    "  3. Plot each season (use the plotting code from above)\n",
+    "      \n",
+    "\n",
+    "  <details>\n",
+    "  <summary><b>→ Hints</b></summary>\n",
+    "\n",
+    "  * You do a for loop over groups `for season, ds_season in ds.groupby(ds.time.dt.season):`\n",
+    "  * You need to remove the time dimension, with `.mean('time')`\n",
+    "  * Use  `mean['Tair'].plot.pcolormesh()` to plot\n",
+    "\n",
+    "  </details>\n",
+    "\n",
+    "  <br/>\n",
+    "  <br/>\n",
+    "  <details>\n",
+    "  <summary>\n",
+    "    <b>→ Solution</b>\n",
+    "  </summary>\n",
+    "\n",
+    "  ```python\n",
+    "    for season, ds_season in ds.groupby(ds.time.dt.season):    \n",
+    "        mean = ds_season.mean('time')\n",
+    "        mean['Tair'].plot.pcolormesh(\n",
+    "            vmin=-30, vmax=30)\n",
+    "        plt.title(season)\n",
+    "        plt.show()\n",
+    "  ```\n",
+    "\n",
+    "  </details>\n",
+    "\n",
+    "  </div>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -4313,9 +4369,9 @@
    "formats": "ipynb,py"
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "deep_ml_curriculum",
    "language": "python",
-   "name": "python3"
+   "name": "deep_ml_curriculum"
   },
   "language_info": {
    "codemirror_mode": {
@@ -4327,7 +4383,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/notebooks/c05_Big_Data/Working_with_Big_Data.py b/notebooks/c05_Big_Data/Working_with_Big_Data.py
index 6331c08..ea26a63 100644
--- a/notebooks/c05_Big_Data/Working_with_Big_Data.py
+++ b/notebooks/c05_Big_Data/Working_with_Big_Data.py
@@ -9,9 +9,9 @@
 #       format_version: '1.5'
 #       jupytext_version: 1.6.0
 #   kernelspec:
-#     display_name: Python 3
+#     display_name: deep_ml_curriculum
 #     language: python
-#     name: python3
+#     name: deep_ml_curriculum
 # ---
 
 # # Introduction to Dask
@@ -554,6 +554,45 @@ def can_compile(x):
 # But you can use .compute
 res.compute()
 
+
+
+# <div class="alert alert-success">
+#   <h2>Exercise</h2>
+#
+#   1. Look at the output of `ds.time.dt.season`
+#   2. Try grouping by season and getting the mean
+#   3. Plot each season (use the plotting code from above)
+#       
+#
+#   <details>
+#   <summary><b>→ Hints</b></summary>
+#
+#   * You do a for loop over groups `for season, ds_season in ds.groupby(ds.time.dt.season):`
+#   * You need to remove the time dimension, with `.mean('time')`
+#   * Use  `mean['Tair'].plot.pcolormesh()` to plot
+#
+#   </details>
+#
+#   <br/>
+#   <br/>
+#   <details>
+#   <summary>
+#     <b>→ Solution</b>
+#   </summary>
+#
+#   ```python
+#     for season, ds_season in ds.groupby(ds.time.dt.season):    
+#         mean = ds_season.mean('time')
+#         mean['Tair'].plot.pcolormesh(
+#             vmin=-30, vmax=30)
+#         plt.title(season)
+#         plt.show()
+#   ```
+#
+#   </details>
+#
+#   </div>
+
 # # References
 # The following sources where used for creation of this notebook:
 # - https://github.com/NCAR/ncar-python-tutorial