CTP-HI_low_CONUS.ipynb

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    "## The Goal: Compute CTP and HI_low over CONUS at 12 UTC \n",
    "<br>\n",
    "<b>Author:</b> Meg D. Fowler \n",
    "<br>\n",
    "<b>Date:</b>   2 Sept 2020 \n",
    "<br><br>\n",
    "<b>Short description:</b> Building off of lots of scratch work in CTP-HI_low_CONUS_scratch.ipynb, uses a python version of the fortran script to compute CTP and HI_low at each point over CONUS. The .py version's slightly more sluggish (~18s for 5k profiles as opposed to ~12s), but it's easy to read and understand. \n",
    "<br>"
   ]
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     "end_time": "2020-09-08T18:55:57.630759Z",
     "start_time": "2020-09-08T18:55:56.141047Z"
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    "# Import libraries \n",
    "import numpy as np \n",
    "import xarray as xr \n",
    "import pickle\n",
    "import pandas as pd\n",
    "import datetime \n",
    "import time \n",
    "import regionmask\n",
    "from CTP_hilow import CTP_hilow \n",
    "\n",
    "# Plotting utils \n",
    "import matplotlib.pyplot as plt \n",
    "import cartopy\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.util\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "ExecuteTime": {
     "end_time": "2020-09-08T18:55:57.687132Z",
     "start_time": "2020-09-08T18:55:57.683777Z"
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   },
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   "source": [
    "# Set file names \n",
    "mainDir = '/Users/meganfowler/Documents/NCAR/Analysis/Coupling_initial/data/hrSim_CONUS/'\n",
    "\n",
    "fileQ1 = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1979_hrQ-UTCsel.nc'\n",
    "fileQ2 = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1980_hrQ-UTCsel.nc'\n",
    "fileQ3 = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1981_hrQ-UTCsel.nc'\n",
    "\n",
    "fileT1 = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1979_hrT-UTCsel.nc'\n",
    "fileT2 = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1980_hrT-UTCsel.nc'\n",
    "fileT3 = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1981_hrT-UTCsel.nc'\n",
    "\n",
    "fileP  = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1979-1981_hrP-levels-UTCsel.nc'\n",
    "\n",
    "filePS  = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1979-1981_hrPS-UTCsel.nc'\n",
    "fileT2m = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1979-1981_hrTREFHT-UTCsel.nc'\n",
    "fileQ2m = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1979-1981_hrQREFHT-UTCsel.nc'\n",
    "fileSH  = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1979-1981_hrSHFLX-UTCsel.nc'\n"
   ]
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   "execution_count": 3,
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     "end_time": "2020-09-08T18:56:11.264218Z",
     "start_time": "2020-09-08T18:55:58.298101Z"
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     "text": [
      "Done with Q...\n",
      "Done with T... \n",
      "Done with P...\n"
     ]
    }
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   "source": [
    "# Read in files that have profiles and concat into one DS\n",
    "Q_1979 = xr.open_dataset(fileQ1, decode_times=True)\n",
    "Q_1980 = xr.open_dataset(fileQ2, decode_times=True)\n",
    "Q_1981 = xr.open_dataset(fileQ3, decode_times=True)\n",
    "Q_full = xr.concat([Q_1979, Q_1980,Q_1981], dim=\"time\")\n",
    "print('Done with Q...')\n",
    "\n",
    "T_1979 = xr.open_dataset(fileT1, decode_times=True)\n",
    "T_1980 = xr.open_dataset(fileT2, decode_times=True)\n",
    "T_1981 = xr.open_dataset(fileT3, decode_times=True)\n",
    "T_full = xr.concat([T_1979, T_1980,T_1981], dim=\"time\")\n",
    "print('Done with T... ')\n",
    "\n",
    "P_all  = xr.open_dataset(fileP,  decode_times=True)\n",
    "print('Done with P...')\n"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T18:56:12.385112Z",
     "start_time": "2020-09-08T18:56:11.267024Z"
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   "source": [
    "# Now read in files (all years, sfc only)\n",
    "PS  = xr.open_dataset(filePS,  decode_times=True)\n",
    "T2m = xr.open_dataset(fileT2m, decode_times=True)\n",
    "Q2m = xr.open_dataset(fileQ2m, decode_times=True)\n",
    "SH  = xr.open_dataset(fileSH, decode_times=True)\n"
   ]
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     "end_time": "2020-09-08T18:56:16.314540Z",
     "start_time": "2020-09-08T18:56:12.387002Z"
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    "# Assemble into single dataset (xarray)\n",
    "ds_Full = Q_full\n",
    "ds_Full['Tlev'] = (('time','lev','lat','lon'), T_full.T)\n",
    "ds_Full['Plev'] = (('time','lev','lat','lon'), P_all.PRESSURE)\n",
    "ds_Full['PS'] = (('time','lat','lon'), PS.PS)\n",
    "ds_Full['T2m'] = (('time','lat','lon'), T2m.TREFHT)\n",
    "ds_Full['Q2m'] = (('time','lat','lon'), Q2m.QREFHT)\n",
    "ds_Full['SHF'] = (('time','lat','lon'), SH.SHFLX)\n"
   ]
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   "cell_type": "code",
   "execution_count": 6,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 43, lev: 32, lon: 65, time: 5475)\n",
       "Coordinates:\n",
       "  * lev      (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6\n",
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       "    UTC_mon  (time) float32 1.0 1.0 1.0 1.0 1.0 1.0 ... 12.0 12.0 12.0 12.0 12.0\n",
       "    UTC_yr   (time) float32 1979.0 1979.0 1979.0 1979.0 ... 1981.0 1981.0 1981.0\n",
       "    Tlev     (time, lev, lat, lon) float32 250.42609 250.28642 ... 270.61188\n",
       "    Plev     (time, lev, lat, lon) float32 364.34656 364.34656 ... 97498.84\n",
       "    PS       (time, lat, lon) float32 102068.03 102079.78 ... 98192.0 98230.06\n",
       "    T2m      (time, lat, lon) float32 293.90747 293.6864 ... 271.09384 271.51926\n",
       "    Q2m      (time, lat, lon) float32 0.011486784 0.011303191 ... 0.0027535888\n",
       "    SHF      (time, lat, lon) float32 39.52112 41.92458 ... 26.336369 28.1356\n",
       "Attributes:\n",
       "    creation_date:  Mon Aug 31 17:46:50 MDT 2020\n",
       "    Conventions:    None\n",
       "    source_file:    /glade/scratch/mdfowler/processedData/f.e21.FHIST_BGC.f09...\n",
       "    title:          NCL used to get UTC; 4 early morning hours saved out.</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-78abaec7-05e6-41d7-9750-8b704c96db60' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-78abaec7-05e6-41d7-9750-8b704c96db60' 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'>lat</span>: 43</li><li><span class='xr-has-index'>lev</span>: 32</li><li><span class='xr-has-index'>lon</span>: 65</li><li><span class='xr-has-index'>time</span>: 5475</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-be8d3a1e-af57-41b9-a840-c5a471c5b797' class='xr-section-summary-in' type='checkbox'  checked><label for='section-be8d3a1e-af57-41b9-a840-c5a471c5b797' class='xr-section-summary' >Coordinates: <span>(4)</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'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.643 7.595 14.36 ... 976.3 992.6</div><input id='attrs-7e2c3033-68cc-43e2-b15e-2c5b0b0d0c22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e2c3033-68cc-43e2-b15e-2c5b0b0d0c22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb4edd6f-7f9c-4499-9b2b-afdaa77a3e89' class='xr-var-data-in' type='checkbox'><label for='data-eb4edd6f-7f9c-4499-9b2b-afdaa77a3e89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>formula_terms :</span></dt><dd>a: hyam b: hybm p0: P0 ps: PS</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>long_name :</span></dt><dd>hybrid level at midpoints (1000*(A+B))</dd></dl></div><div class='xr-var-data'><pre>array([  3.643466,   7.59482 ,  14.356632,  24.61222 ,  35.92325 ,  43.19375 ,\n",
       "        51.677499,  61.520498,  73.750958,  87.82123 , 103.317127, 121.547241,\n",
       "       142.994039, 168.22508 , 197.908087, 232.828619, 273.910817, 322.241902,\n",
       "       379.100904, 445.992574, 524.687175, 609.778695, 691.38943 , 763.404481,\n",
       "       820.858369, 859.534767, 887.020249, 912.644547, 936.198398, 957.48548 ,\n",
       "       976.325407, 992.556095])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>220.0 221.2 222.5 ... 298.8 300.0</div><input id='attrs-b9a700cd-956b-409d-8038-8aa503f8c69a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9a700cd-956b-409d-8038-8aa503f8c69a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53df6237-bf04-4eed-a366-5bec58a487fa' class='xr-var-data-in' type='checkbox'><label for='data-53df6237-bf04-4eed-a366-5bec58a487fa' title='Show/Hide data repr'><svg 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</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([220.  , 221.25, 222.5 , 223.75, 225.  , 226.25, 227.5 , 228.75, 230.  ,\n",
       "       231.25, 232.5 , 233.75, 235.  , 236.25, 237.5 , 238.75, 240.  , 241.25,\n",
       "       242.5 , 243.75, 245.  , 246.25, 247.5 , 248.75, 250.  , 251.25, 252.5 ,\n",
       "       253.75, 255.  , 256.25, 257.5 , 258.75, 260.  , 261.25, 262.5 , 263.75,\n",
       "       265.  , 266.25, 267.5 , 268.75, 270.  , 271.25, 272.5 , 273.75, 275.  ,\n",
       "       276.25, 277.5 , 278.75, 280.  , 281.25, 282.5 , 283.75, 285.  , 286.25,\n",
       "       287.5 , 288.75, 290.  , 291.25, 292.5 , 293.75, 295.  , 296.25, 297.5 ,\n",
       "       298.75, 300.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.26 21.2 22.15 ... 58.9 59.84</div><input id='attrs-e7d6b716-e11e-4d52-81b0-d726227f88a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7d6b716-e11e-4d52-81b0-d726227f88a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ccf6aae-c5d0-438a-9b50-d05e42f2d666' class='xr-var-data-in' type='checkbox'><label for='data-7ccf6aae-c5d0-438a-9b50-d05e42f2d666' title='Show/Hide data repr'><svg 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</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([20.26178 , 21.204188, 22.146597, 23.089005, 24.031414, 24.973822,\n",
       "       25.91623 , 26.858639, 27.801047, 28.743455, 29.685864, 30.628272,\n",
       "       31.570681, 32.513089, 33.455497, 34.397906, 35.340314, 36.282723,\n",
       "       37.225131, 38.167539, 39.109948, 40.052356, 40.994764, 41.937173,\n",
       "       42.879581, 43.82199 , 44.764398, 45.706806, 46.649215, 47.591623,\n",
       "       48.534031, 49.47644 , 50.418848, 51.361257, 52.303665, 53.246073,\n",
       "       54.188482, 55.13089 , 56.073298, 57.015707, 57.958115, 58.900524,\n",
       "       59.842932])</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'>object</div><div class='xr-var-preview xr-preview'>1979-01-01 10:00:00 ... 1981-12-...</div><input id='attrs-2d7fa75f-5a8b-4a7c-89ae-30a8c1a60119' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d7fa75f-5a8b-4a7c-89ae-30a8c1a60119' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-984b778d-f3ae-4a88-a931-9d12412ec65b' class='xr-var-data-in' type='checkbox'><label for='data-984b778d-f3ae-4a88-a931-9d12412ec65b' title='Show/Hide data repr'><svg 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>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1979, 1, 1, 10, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 1, 11, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 1, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(1981, 12, 31, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1981, 12, 31, 13, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1981, 12, 31, 14, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a1399d18-60b0-42ba-865c-df5548ff77d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a1399d18-60b0-42ba-865c-df5548ff77d5' class='xr-section-summary' >Data variables: <span>(11)</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>Q</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.7808608e-06 ... 0.0025378529</div><input id='attrs-07681002-4a58-4b7c-90a7-ff38fd10297c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07681002-4a58-4b7c-90a7-ff38fd10297c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-489e29a1-5030-4c82-a97b-e5d91e0b4867' class='xr-var-data-in' type='checkbox'><label for='data-489e29a1-5030-4c82-a97b-e5d91e0b4867' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>Specific humidity in UTC time.</dd><dt><span>mixing_ratio :</span></dt><dd>wet</dd><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>mdims :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([[[[2.78086077e-06, 2.78040625e-06, 2.77977551e-06, ...,\n",
       "          2.79973801e-06, 2.80023528e-06, 2.80099812e-06],\n",
       "         [2.78136258e-06, 2.78060043e-06, 2.77998038e-06, ...,\n",
       "          2.81086818e-06, 2.81165944e-06, 2.81261282e-06],\n",
       "         [2.78712332e-06, 2.78514221e-06, 2.78353559e-06, ...,\n",
       "          2.81744019e-06, 2.81727193e-06, 2.81735606e-06],\n",
       "         ...,\n",
       "         [2.89198283e-06, 2.89368700e-06, 2.89650188e-06, ...,\n",
       "          2.98169834e-06, 2.98098939e-06, 2.98023133e-06],\n",
       "         [2.91195033e-06, 2.91786478e-06, 2.92447885e-06, ...,\n",
       "          2.99068438e-06, 2.98977261e-06, 2.98876512e-06],\n",
       "         [2.94461665e-06, 2.94866436e-06, 2.95203313e-06, ...,\n",
       "          3.00234251e-06, 3.00111628e-06, 2.99972635e-06]],\n",
       "\n",
       "        [[2.83152576e-06, 2.83247459e-06, 2.83333407e-06, ...,\n",
       "          2.77008212e-06, 2.76819924e-06, 2.76262631e-06],\n",
       "         [2.83592408e-06, 2.83452982e-06, 2.83444001e-06, ...,\n",
       "          2.78089556e-06, 2.77564573e-06, 2.76867604e-06],\n",
       "         [2.84836506e-06, 2.84422231e-06, 2.84101657e-06, ...,\n",
       "          2.78041762e-06, 2.77457343e-06, 2.76732771e-06],\n",
       "...\n",
       "          1.76861335e-03, 1.58359925e-03, 1.61160249e-03],\n",
       "         [9.00063082e-04, 7.07263651e-04, 1.76084781e-04, ...,\n",
       "          1.88376429e-03, 1.94511400e-03, 1.96180330e-03],\n",
       "         [5.98822080e-04, 3.08478368e-04, 1.64129378e-04, ...,\n",
       "          2.39867205e-03, 2.48796889e-03, 2.48786039e-03]],\n",
       "\n",
       "        [[1.02614658e-02, 1.01593593e-02, 1.01258010e-02, ...,\n",
       "          1.48009053e-02, 1.45442449e-02, 1.43121704e-02],\n",
       "         [1.03878444e-02, 1.03129353e-02, 1.01744710e-02, ...,\n",
       "          1.40793817e-02, 1.41021237e-02, 1.39209637e-02],\n",
       "         [1.04870852e-02, 1.03323124e-02, 1.01964539e-02, ...,\n",
       "          1.34062218e-02, 1.33754974e-02, 1.32172545e-02],\n",
       "         ...,\n",
       "         [1.16167532e-03, 1.09279959e-03, 9.77688353e-04, ...,\n",
       "          1.80401420e-03, 1.60616951e-03, 1.63279858e-03],\n",
       "         [9.48438363e-04, 7.73845299e-04, 1.92549269e-04, ...,\n",
       "          1.91587466e-03, 1.97351235e-03, 1.99264078e-03],\n",
       "         [6.56158663e-04, 4.40484553e-04, 1.01461417e-04, ...,\n",
       "          2.44922843e-03, 2.55337171e-03, 2.53785285e-03]]]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTC_hr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.0 11.0 12.0 ... 12.0 13.0 14.0</div><input id='attrs-b581fb84-ecc9-47da-82f7-b2115bd85559' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b581fb84-ecc9-47da-82f7-b2115bd85559' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51249914-0874-404c-b805-9a197cccfa3f' class='xr-var-data-in' type='checkbox'><label for='data-51249914-0874-404c-b805-9a197cccfa3f' title='Show/Hide data repr'><svg 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>Hour in UTC</dd><dt><span>calendar :</span></dt><dd>noleap</dd></dl></div><div class='xr-var-data'><pre>array([10., 11., 12., ..., 12., 13., 14.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTC_day</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 31.0 31.0 31.0</div><input id='attrs-27dfad8c-532a-47c8-a052-6e9d952e8da8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27dfad8c-532a-47c8-a052-6e9d952e8da8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5955e313-0cbb-4e8b-a47b-54463b38fdde' class='xr-var-data-in' type='checkbox'><label for='data-5955e313-0cbb-4e8b-a47b-54463b38fdde' title='Show/Hide data repr'><svg 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>Day in UTC</dd><dt><span>calendar :</span></dt><dd>noleap</dd></dl></div><div class='xr-var-data'><pre>array([ 1.,  1.,  1., ..., 31., 31., 31.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTC_mon</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 12.0 12.0 12.0</div><input id='attrs-54ef2502-e6ff-40bf-96d8-1609220a4504' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54ef2502-e6ff-40bf-96d8-1609220a4504' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2c29401-d343-49b3-96c0-e143f4a5a89a' class='xr-var-data-in' type='checkbox'><label for='data-f2c29401-d343-49b3-96c0-e143f4a5a89a' title='Show/Hide data repr'><svg 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>Mon in UTC</dd><dt><span>calendar :</span></dt><dd>noleap</dd></dl></div><div class='xr-var-data'><pre>array([ 1.,  1.,  1., ..., 12., 12., 12.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTC_yr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1979.0 1979.0 ... 1981.0 1981.0</div><input id='attrs-704c7707-6808-4950-b970-85e9b2727b13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-704c7707-6808-4950-b970-85e9b2727b13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa70fadf-8930-4b28-ad4e-921b65ae3fd7' class='xr-var-data-in' type='checkbox'><label for='data-fa70fadf-8930-4b28-ad4e-921b65ae3fd7' title='Show/Hide data repr'><svg 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>Yr in UTC</dd><dt><span>calendar :</span></dt><dd>noleap</dd></dl></div><div class='xr-var-data'><pre>array([1979., 1979., 1979., ..., 1981., 1981., 1981.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Tlev</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>250.42609 250.28642 ... 270.61188</div><input id='attrs-b89d067c-bd44-4423-b27b-20417af2396d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b89d067c-bd44-4423-b27b-20417af2396d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b485b668-8d9e-4e1b-a420-4d3cde99f60a' class='xr-var-data-in' type='checkbox'><label for='data-b485b668-8d9e-4e1b-a420-4d3cde99f60a' title='Show/Hide data 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([[[[250.42609, 250.28642, 250.14624, ..., 248.18066, 248.23183,\n",
       "          248.29518],\n",
       "         [250.26886, 250.1536 , 250.033  , ..., 248.08951, 248.14632,\n",
       "          248.22221],\n",
       "         [250.06104, 249.97658, 249.88376, ..., 247.8755 , 247.93837,\n",
       "          248.02246],\n",
       "         ...,\n",
       "         [218.46867, 218.38773, 218.20667, ..., 212.1269 , 212.12334,\n",
       "          212.08595],\n",
       "         [217.56087, 217.50012, 217.30353, ..., 211.62042, 211.61778,\n",
       "          211.59836],\n",
       "         [216.57446, 216.49031, 216.29308, ..., 211.11174, 211.10512,\n",
       "          211.0914 ]],\n",
       "\n",
       "        [[235.94069, 235.84145, 235.74237, ..., 234.66425, 234.71324,\n",
       "          234.7687 ],\n",
       "         [235.71893, 235.6486 , 235.58376, ..., 234.82843, 234.87785,\n",
       "          234.94028],\n",
       "         [235.3057 , 235.2583 , 235.22467, ..., 234.87209, 234.93665,\n",
       "          235.00438],\n",
       "...\n",
       "         [261.1544 , 258.60855, 255.74565, ..., 265.94687, 267.09314,\n",
       "          267.53833],\n",
       "         [258.02972, 253.15683, 244.74489, ..., 267.19034, 268.02277,\n",
       "          268.42886],\n",
       "         [250.37347, 244.28221, 239.8702 , ..., 268.28964, 268.925  ,\n",
       "          269.31384]],\n",
       "\n",
       "        [[293.3083 , 292.98926, 292.68802, ..., 297.0505 , 296.97266,\n",
       "          296.93478],\n",
       "         [293.10632, 292.7815 , 292.5029 , ..., 296.70483, 296.57758,\n",
       "          296.51996],\n",
       "         [292.8373 , 292.52957, 292.30405, ..., 296.2661 , 296.23694,\n",
       "          296.2622 ],\n",
       "         ...,\n",
       "         [262.46725, 259.9523 , 257.14902, ..., 267.2067 , 268.36346,\n",
       "          268.80783],\n",
       "         [259.37192, 254.61285, 239.69476, ..., 268.4626 , 269.30063,\n",
       "          269.7102 ],\n",
       "         [251.77733, 245.56032, 234.18895, ..., 269.57468, 270.21024,\n",
       "          270.61188]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Plev</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>364.34656 364.34656 ... 97498.84</div><input id='attrs-6e7307c6-51bb-4152-b9b7-40efacf2894e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e7307c6-51bb-4152-b9b7-40efacf2894e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8f2b220-b4d2-4a0c-8607-1abc582969eb' class='xr-var-data-in' type='checkbox'><label for='data-e8f2b220-b4d2-4a0c-8607-1abc582969eb' title='Show/Hide data 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([[[[   364.34656,    364.34656,    364.34656, ...,    364.34656,\n",
       "             364.34656,    364.34656],\n",
       "         [   364.34656,    364.34656,    364.34656, ...,    364.34656,\n",
       "             364.34656,    364.34656],\n",
       "         [   364.34656,    364.34656,    364.34656, ...,    364.34656,\n",
       "             364.34656,    364.34656],\n",
       "         ...,\n",
       "         [   364.34656,    364.34656,    364.34656, ...,    364.34656,\n",
       "             364.34656,    364.34656],\n",
       "         [   364.34656,    364.34656,    364.34656, ...,    364.34656,\n",
       "             364.34656,    364.34656],\n",
       "         [   364.34656,    364.34656,    364.34656, ...,    364.34656,\n",
       "             364.34656,    364.34656]],\n",
       "\n",
       "        [[   759.482  ,    759.482  ,    759.482  , ...,    759.482  ,\n",
       "             759.482  ,    759.482  ],\n",
       "         [   759.482  ,    759.482  ,    759.482  , ...,    759.482  ,\n",
       "             759.482  ,    759.482  ],\n",
       "         [   759.482  ,    759.482  ,    759.482  , ...,    759.482  ,\n",
       "             759.482  ,    759.482  ],\n",
       "...\n",
       "         [ 99369.125  ,  98597.69   ,  97500.85   , ...,  93203.664  ,\n",
       "           94915.73   ,  95726.484  ],\n",
       "         [ 97159.25   ,  95314.875  ,  93284.164  , ...,  94236.67   ,\n",
       "           95438.375  ,  95882.42   ],\n",
       "         [ 89667.81   ,  88454.48   ,  87731.695  , ...,  95409.85   ,\n",
       "           95870.94   ,  95908.02   ]],\n",
       "\n",
       "        [[101078.82   , 101102.92   , 101120.375  , ..., 101398.21   ,\n",
       "          101418.23   , 101431.01   ],\n",
       "         [101139.73   , 101163.43   , 101183.03   , ..., 101467.39   ,\n",
       "          101480.766  , 101497.38   ],\n",
       "         [101207.59   , 101232.6    , 101249.11   , ..., 101545.7    ,\n",
       "          101551.54   , 101559.96   ],\n",
       "         ...,\n",
       "         [101024.67   , 100238.805  ,  99121.46   , ...,  94743.914  ,\n",
       "           96487.99   ,  97313.91   ],\n",
       "         [ 98773.47   ,  96894.61   ,  94825.914  , ...,  95796.24   ,\n",
       "           97020.414  ,  97472.76   ],\n",
       "         [ 91141.945  ,  89905.914  ,  89169.62   , ...,  96991.36   ,\n",
       "           97461.07   ,  97498.84   ]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PS</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>102068.03 102079.78 ... 98230.06</div><input id='attrs-0f94998f-6baf-41cf-94ec-f47ce72ca5cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f94998f-6baf-41cf-94ec-f47ce72ca5cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12bf2129-9ef7-49ac-aa6c-67bd27daeee1' class='xr-var-data-in' type='checkbox'><label for='data-12bf2129-9ef7-49ac-aa6c-67bd27daeee1' title='Show/Hide data 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([[[102068.03 , 102079.78 , 102098.38 , ..., 101945.01 ,\n",
       "         101926.77 , 101904.86 ],\n",
       "        [102176.31 , 102195.305, 102211.03 , ..., 102056.336,\n",
       "         102045.96 , 102026.766],\n",
       "        [102286.6  , 102307.26 , 102314.41 , ..., 102182.66 ,\n",
       "         102164.46 , 102146.35 ],\n",
       "        ...,\n",
       "        [100037.39 ,  99459.68 ,  98601.4  , ...,  96038.84 ,\n",
       "          97893.2  ,  98744.5  ],\n",
       "        [ 97703.734,  96107.97 ,  94273.61 , ...,  96789.23 ,\n",
       "          98111.15 ,  98580.75 ],\n",
       "        [ 90511.08 ,  89315.39 ,  88640.6  , ...,  97764.83 ,\n",
       "          98284.19 ,  98321.766]],\n",
       "\n",
       "       [[102024.99 , 102032.04 , 102047.586, ..., 101969.79 ,\n",
       "         101953.56 , 101932.664],\n",
       "        [102132.5  , 102147.99 , 102162.15 , ..., 102077.164,\n",
       "         102067.26 , 102051.15 ],\n",
       "        [102245.82 , 102262.72 , 102271.05 , ..., 102196.58 ,\n",
       "         102178.72 , 102164.05 ],\n",
       "...\n",
       "        [101783.42 , 100995.71 ,  99867.02 , ...,  95423.414,\n",
       "          97185.11 ,  98036.13 ],\n",
       "        [ 99514.54 ,  97622.08 ,  95535.22 , ...,  96519.4  ,\n",
       "          97764.984,  98232.22 ],\n",
       "        [ 91833.76 ,  90588.91 ,  89841.03 , ...,  97719.62 ,\n",
       "          98213.586,  98270.35 ]],\n",
       "\n",
       "       [[101836.88 , 101861.164, 101878.75 , ..., 102158.67 ,\n",
       "         102178.836, 102191.71 ],\n",
       "        [101898.25 , 101922.125, 101941.875, ..., 102228.37 ,\n",
       "         102241.84 , 102258.586],\n",
       "        [101966.625, 101991.82 , 102008.45 , ..., 102307.266,\n",
       "         102313.15 , 102321.63 ],\n",
       "        ...,\n",
       "        [101782.33 , 100990.57 ,  99864.84 , ...,  95454.47 ,\n",
       "          97211.625,  98043.734],\n",
       "        [ 99514.24 ,  97621.29 ,  95537.086, ...,  96514.69 ,\n",
       "          97748.04 ,  98203.78 ],\n",
       "        [ 91825.484,  90580.19 ,  89838.37 , ...,  97718.766,\n",
       "          98192.   ,  98230.06 ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T2m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>293.90747 293.6864 ... 271.51926</div><input id='attrs-134579be-1b76-4624-9c45-db9cc6ea0f54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-134579be-1b76-4624-9c45-db9cc6ea0f54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b327ecd7-a932-4410-bc79-b3b0a8897024' class='xr-var-data-in' type='checkbox'><label for='data-b327ecd7-a932-4410-bc79-b3b0a8897024' title='Show/Hide data 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([[[293.90747, 293.6864 , 293.51004, ..., 297.85977, 297.7836 ,\n",
       "         297.79803],\n",
       "        [293.52835, 293.30334, 293.05682, ..., 297.56564, 297.48126,\n",
       "         297.43344],\n",
       "        [293.19296, 292.92856, 292.69818, ..., 297.23895, 297.1924 ,\n",
       "         297.11664],\n",
       "        ...,\n",
       "        [278.95963, 278.57352, 276.82712, ..., 247.6405 , 252.39215,\n",
       "         256.4636 ],\n",
       "        [276.93912, 276.0765 , 271.914  , ..., 252.1727 , 255.57816,\n",
       "         258.6631 ],\n",
       "        [273.68378, 271.59717, 267.28824, ..., 255.0083 , 258.09164,\n",
       "         260.27948]],\n",
       "\n",
       "       [[293.8441 , 293.62173, 293.4606 , ..., 297.86307, 297.8145 ,\n",
       "         297.81638],\n",
       "        [293.48282, 293.2407 , 293.0083 , ..., 297.5916 , 297.50552,\n",
       "         297.46597],\n",
       "        [293.13004, 292.8779 , 292.6625 , ..., 297.23047, 297.22208,\n",
       "         297.111  ],\n",
       "...\n",
       "        [265.06982, 262.893  , 260.5198 , ..., 267.15927, 268.98206,\n",
       "         269.32565],\n",
       "        [262.38138, 257.7154 , 239.64775, ..., 269.0444 , 269.9521 ,\n",
       "         270.30597],\n",
       "        [254.16795, 245.35033, 233.83397, ..., 270.3913 , 270.9247 ,\n",
       "         271.2582 ]],\n",
       "\n",
       "       [[294.3354 , 294.03134, 293.71902, ..., 297.92224, 297.84293,\n",
       "         297.80713],\n",
       "        [294.1121 , 293.8042 , 293.51575, ..., 297.58405, 297.4628 ,\n",
       "         297.39835],\n",
       "        [293.83115, 293.54178, 293.3073 , ..., 297.16217, 297.11652,\n",
       "         297.11893],\n",
       "        ...,\n",
       "        [264.9682 , 262.85565, 260.40643, ..., 267.3478 , 269.32535,\n",
       "         269.78006],\n",
       "        [262.30142, 257.6048 , 239.4861 , ..., 269.23486, 270.2335 ,\n",
       "         270.64502],\n",
       "        [254.08711, 245.2119 , 233.63533, ..., 270.51587, 271.09384,\n",
       "         271.51926]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Q2m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.011486784 ... 0.0027535888</div><input id='attrs-0fe7c705-af94-4071-b31d-7cbbcfafc370' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0fe7c705-af94-4071-b31d-7cbbcfafc370' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05f64bf7-124f-4f66-8f59-a1e5b834eeef' class='xr-var-data-in' type='checkbox'><label for='data-05f64bf7-124f-4f66-8f59-a1e5b834eeef' title='Show/Hide data 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([[[1.14867836e-02, 1.13031911e-02, 1.10679083e-02, ...,\n",
       "         1.53330453e-02, 1.52907036e-02, 1.53392190e-02],\n",
       "        [1.11766579e-02, 1.09012071e-02, 1.06608151e-02, ...,\n",
       "         1.50418095e-02, 1.50017021e-02, 1.49660660e-02],\n",
       "        [1.07901786e-02, 1.05126482e-02, 1.03168357e-02, ...,\n",
       "         1.44011835e-02, 1.44448150e-02, 1.44830262e-02],\n",
       "        ...,\n",
       "        [5.24618011e-03, 5.14069572e-03, 4.93255025e-03, ...,\n",
       "         3.95295327e-04, 6.63765240e-04, 1.03074196e-03],\n",
       "        [5.01462445e-03, 4.75418475e-03, 3.60349007e-03, ...,\n",
       "         5.91021206e-04, 8.89405666e-04, 1.20275677e-03],\n",
       "        [4.23123455e-03, 3.71241849e-03, 2.65380042e-03, ...,\n",
       "         8.11859965e-04, 1.15270924e-03, 1.38282334e-03]],\n",
       "\n",
       "       [[1.14389667e-02, 1.12728626e-02, 1.10542849e-02, ...,\n",
       "         1.53047787e-02, 1.52970320e-02, 1.53121306e-02],\n",
       "        [1.11185154e-02, 1.08421128e-02, 1.06365513e-02, ...,\n",
       "         1.49868065e-02, 1.49109242e-02, 1.48709202e-02],\n",
       "        [1.07442504e-02, 1.05183739e-02, 1.03285322e-02, ...,\n",
       "         1.43724773e-02, 1.44062629e-02, 1.44620650e-02],\n",
       "...\n",
       "        [1.66518835e-03, 1.63106341e-03, 1.53079513e-03, ...,\n",
       "         1.95600931e-03, 1.84516748e-03, 1.83062791e-03],\n",
       "        [1.50897517e-03, 1.22680562e-03, 1.89824292e-04, ...,\n",
       "         1.94607431e-03, 1.98515644e-03, 1.97670865e-03],\n",
       "        [9.87842213e-04, 4.56926035e-04, 9.48626257e-05, ...,\n",
       "         2.60594254e-03, 2.63270643e-03, 2.66633532e-03]],\n",
       "\n",
       "       [[1.13693215e-02, 1.12330541e-02, 1.11443233e-02, ...,\n",
       "         1.56878177e-02, 1.54414363e-02, 1.52255744e-02],\n",
       "        [1.14101851e-02, 1.13048758e-02, 1.11352522e-02, ...,\n",
       "         1.49956066e-02, 1.49867600e-02, 1.48030967e-02],\n",
       "        [1.14367800e-02, 1.12730870e-02, 1.11194737e-02, ...,\n",
       "         1.43089704e-02, 1.42586576e-02, 1.40987365e-02],\n",
       "        ...,\n",
       "        [1.67058466e-03, 1.63822982e-03, 1.53528352e-03, ...,\n",
       "         1.96609786e-03, 1.84815773e-03, 1.86392432e-03],\n",
       "        [1.50637550e-03, 1.22192409e-03, 1.87451617e-04, ...,\n",
       "         2.11493368e-03, 2.19367142e-03, 2.22565210e-03],\n",
       "        [9.83052421e-04, 4.53165179e-04, 9.25816057e-05, ...,\n",
       "         2.63746129e-03, 2.73868674e-03, 2.75358884e-03]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHF</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>39.52112 41.92458 ... 28.1356</div><input id='attrs-4f6404cb-af02-4efe-b490-cc30f3b8396d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f6404cb-af02-4efe-b490-cc30f3b8396d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8963c42-cab7-4959-b4cb-c97aa32da49e' class='xr-var-data-in' type='checkbox'><label for='data-b8963c42-cab7-4959-b4cb-c97aa32da49e' title='Show/Hide data 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([[[ 3.95211182e+01,  4.19245796e+01,  4.46588898e+01, ...,\n",
       "          3.24018555e+01,  3.16118336e+01,  2.95429211e+01],\n",
       "        [ 3.88123016e+01,  4.18553276e+01,  4.35411797e+01, ...,\n",
       "          3.50743980e+01,  3.40490761e+01,  3.30452461e+01],\n",
       "        [ 3.81399002e+01,  4.19566040e+01,  4.22343025e+01, ...,\n",
       "          3.64033966e+01,  3.54695129e+01,  3.51769638e+01],\n",
       "        ...,\n",
       "        [ 6.16072655e-01,  1.08028316e+01,  3.70455894e+01, ...,\n",
       "          4.32918434e+01,  2.64307709e+02,  4.55240967e+02],\n",
       "        [ 3.10725632e+01,  9.75811100e+00, -5.33608627e+00, ...,\n",
       "          1.04829491e+02,  3.33787720e+02,  4.86352539e+02],\n",
       "        [ 4.79833841e+00,  1.15911274e+01, -7.36559391e-01, ...,\n",
       "          1.69749283e+02,  4.33580933e+02,  5.06442596e+02]],\n",
       "\n",
       "       [[ 4.08209572e+01,  4.34455566e+01,  4.61175194e+01, ...,\n",
       "          3.28471832e+01,  3.14349270e+01,  2.95784779e+01],\n",
       "        [ 3.96577911e+01,  4.31845779e+01,  4.47289848e+01, ...,\n",
       "          3.47676811e+01,  3.40179214e+01,  3.28369751e+01],\n",
       "        [ 3.92313538e+01,  4.28844070e+01,  4.28608551e+01, ...,\n",
       "          3.66022568e+01,  3.50215225e+01,  3.54837570e+01],\n",
       "...\n",
       "          1.02741909e+01,  4.28182755e+01,  4.10116997e+01],\n",
       "        [ 4.97044525e+02,  5.37596069e+02, -1.70879245e-01, ...,\n",
       "          2.84430523e+01,  3.12456398e+01,  3.05952682e+01],\n",
       "        [ 4.02628693e+02,  1.03635048e+02, -3.77055016e+01, ...,\n",
       "          2.73580399e+01,  2.27485332e+01,  2.60316029e+01]],\n",
       "\n",
       "       [[ 4.20090141e+01,  4.36087494e+01,  4.21003990e+01, ...,\n",
       "          2.01215897e+01,  1.97525749e+01,  1.91461029e+01],\n",
       "        [ 3.91356430e+01,  4.09737015e+01,  3.97378578e+01, ...,\n",
       "          2.07041016e+01,  2.10215511e+01,  2.02486973e+01],\n",
       "        [ 3.74241257e+01,  3.94961929e+01,  3.84292603e+01, ...,\n",
       "          2.08790874e+01,  2.05970078e+01,  1.89127083e+01],\n",
       "        ...,\n",
       "        [ 3.71989838e+02,  4.93933289e+02,  6.04174072e+02, ...,\n",
       "          8.54603481e+00,  3.95466614e+01,  3.71824837e+01],\n",
       "        [ 4.97750549e+02,  5.40169495e+02,  3.53434920e-01, ...,\n",
       "          2.91830769e+01,  3.22197723e+01,  3.17852879e+01],\n",
       "        [ 4.03546875e+02,  1.05670631e+02, -3.73937111e+01, ...,\n",
       "          3.20807495e+01,  2.63363686e+01,  2.81355991e+01]]],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bb2d229a-7630-405b-ac85-97397bfcd36e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bb2d229a-7630-405b-ac85-97397bfcd36e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>creation_date :</span></dt><dd>Mon Aug 31 17:46:50 MDT 2020</dd><dt><span>Conventions :</span></dt><dd>None</dd><dt><span>source_file :</span></dt><dd>/glade/scratch/mdfowler/processedData/f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1979_hrQ.nc</dd><dt><span>title :</span></dt><dd>NCL used to get UTC; 4 early morning hours saved out.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 43, lev: 32, lon: 65, time: 5475)\n",
       "Coordinates:\n",
       "  * lev      (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6\n",
       "  * lon      (lon) float64 220.0 221.2 222.5 223.8 ... 296.2 297.5 298.8 300.0\n",
       "  * lat      (lat) float64 20.26 21.2 22.15 23.09 ... 57.02 57.96 58.9 59.84\n",
       "  * time     (time) object 1979-01-01 10:00:00 ... 1981-12-31 14:00:00\n",
       "Data variables:\n",
       "    Q        (time, lev, lat, lon) float32 2.7808608e-06 ... 0.0025378529\n",
       "    UTC_hr   (time) float32 10.0 11.0 12.0 13.0 14.0 ... 11.0 12.0 13.0 14.0\n",
       "    UTC_day  (time) float32 1.0 1.0 1.0 1.0 1.0 2.0 ... 31.0 31.0 31.0 31.0 31.0\n",
       "    UTC_mon  (time) float32 1.0 1.0 1.0 1.0 1.0 1.0 ... 12.0 12.0 12.0 12.0 12.0\n",
       "    UTC_yr   (time) float32 1979.0 1979.0 1979.0 1979.0 ... 1981.0 1981.0 1981.0\n",
       "    Tlev     (time, lev, lat, lon) float32 250.42609 250.28642 ... 270.61188\n",
       "    Plev     (time, lev, lat, lon) float32 364.34656 364.34656 ... 97498.84\n",
       "    PS       (time, lat, lon) float32 102068.03 102079.78 ... 98192.0 98230.06\n",
       "    T2m      (time, lat, lon) float32 293.90747 293.6864 ... 271.09384 271.51926\n",
       "    Q2m      (time, lat, lon) float32 0.011486784 0.011303191 ... 0.0027535888\n",
       "    SHF      (time, lat, lon) float32 39.52112 41.92458 ... 26.336369 28.1356\n",
       "Attributes:\n",
       "    creation_date:  Mon Aug 31 17:46:50 MDT 2020\n",
       "    Conventions:    None\n",
       "    source_file:    /glade/scratch/mdfowler/processedData/f.e21.FHIST_BGC.f09...\n",
       "    title:          NCL used to get UTC; 4 early morning hours saved out."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_Full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T18:56:32.669685Z",
     "start_time": "2020-09-08T18:56:16.316155Z"
    }
   },
   "outputs": [],
   "source": [
    "# Pick out 12 UTC only \n",
    "ds_utc12 = ds_Full.where( ds_Full.UTC_hr==12.0 , drop=True )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T18:56:32.680998Z",
     "start_time": "2020-09-08T18:56:32.674113Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define dimensions \n",
    "nLat  = len(ds_utc12.lat)\n",
    "nLon  = len(ds_utc12.lon)\n",
    "nTime = len(ds_utc12.time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Compute CTP and HI_low "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with lat 0 of 43 \n",
      "Done with lat 1 of 43 \n",
      "Done with lat 2 of 43 \n",
      "Done with lat 3 of 43 \n",
      "Done with lat 4 of 43 \n",
      "Done with lat 5 of 43 \n",
      "Done with lat 6 of 43 \n",
      "Done with lat 7 of 43 \n",
      "Done with lat 8 of 43 \n",
      "Done with lat 9 of 43 \n",
      "Done with lat 10 of 43 \n",
      "Done with lat 11 of 43 \n",
      "Done with lat 12 of 43 \n",
      "Done with lat 13 of 43 \n",
      "Done with lat 14 of 43 \n",
      "Done with lat 15 of 43 \n",
      "Done with lat 16 of 43 \n",
      "Done with lat 17 of 43 \n",
      "Done with lat 18 of 43 \n",
      "Done with lat 19 of 43 \n",
      "Done with lat 20 of 43 \n",
      "Done with lat 21 of 43 \n",
      "Done with lat 22 of 43 \n",
      "Done with lat 23 of 43 \n",
      "Done with lat 24 of 43 \n",
      "Done with lat 25 of 43 \n",
      "Done with lat 26 of 43 \n",
      "Done with lat 27 of 43 \n",
      "Done with lat 28 of 43 \n",
      "Done with lat 29 of 43 \n",
      "Done with lat 30 of 43 \n",
      "Done with lat 31 of 43 \n",
      "Done with lat 32 of 43 \n",
      "Done with lat 33 of 43 \n",
      "Done with lat 34 of 43 \n",
      "Done with lat 35 of 43 \n",
      "Done with lat 36 of 43 \n",
      "Done with lat 37 of 43 \n",
      "Done with lat 38 of 43 \n",
      "Done with lat 39 of 43 \n",
      "Done with lat 40 of 43 \n",
      "Done with lat 41 of 43 \n",
      "Done with lat 42 of 43 \n",
      "Time elapsed for all points and times: 10752.282 sec\n"
     ]
    }
   ],
   "source": [
    "# Define empty arrays to save things into \n",
    "CTP_all   = np.full([nTime,nLat,nLon], np.nan)\n",
    "HIlow_all = np.full([nTime,nLat,nLon], np.nan)\n",
    "\n",
    "t_All = time.time()\n",
    "\n",
    "for iLat in range(nLat):\n",
    "    for iLon in range(nLon):\n",
    "        for iT in range(nTime):\n",
    "            \n",
    "            # Pick out specific point and time period \n",
    "            DF = ds_Full.isel(lat=iLat,lon=iLon,time=iT).to_dataframe()\n",
    "            \n",
    "            # Flip order of levels so that surface comes first (required for function)\n",
    "            DF = DF.reindex(index=DF.index[::-1])\n",
    "            \n",
    "            # Compute CTP and HI_low\n",
    "            CTP_all[iT,iLat,iLon], HIlow_all[iT,iLat,iLon] = CTP_hilow(DF['Tlev'].values, DF['Q'].values, \n",
    "                                                                   DF['Plev'].values, DF['T2m'].values[0], \n",
    "                                                                   DF['Q2m'].values[0], DF['PS'].values[0])\n",
    "    print('Done with lat %i of %i ' % (iLat, nLat))\n",
    "        \n",
    "\n",
    "print('Time elapsed for all points and times: %.3f sec' % (time.time() - t_All))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Save the array with CTP \n",
    "saveDir  = '/Users/meganfowler/Documents/NCAR/Analysis/Coupling_initial/Coupling_CAM6CLM5/processed_data/'\n",
    "saveFile = 'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.CTP_CONUS_12utc.p' \n",
    "\n",
    "pickle.dump( CTP_all, open( saveDir+saveFile, \"wb\" ), protocol=4 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Save the array with CTP \n",
    "saveDir  = '/Users/meganfowler/Documents/NCAR/Analysis/Coupling_initial/Coupling_CAM6CLM5/processed_data/'\n",
    "saveFile = 'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.HIlow_CONUS_12utc.p' \n",
    "\n",
    "pickle.dump( HIlow_all, open( saveDir+saveFile, \"wb\" ), protocol=4 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9867449999999995"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How long was compute time in hours?\n",
    "10752.282/60/60\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis time, yay!! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T18:56:32.714935Z",
     "start_time": "2020-09-08T18:56:32.682854Z"
    }
   },
   "outputs": [],
   "source": [
    "# Read in from pickle files \n",
    "saveDir        = '/Users/meganfowler/Documents/NCAR/Analysis/Coupling_initial/Coupling_CAM6CLM5/processed_data/'\n",
    "CTP_fileName   = saveDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.CTP_CONUS_12utc.p'\n",
    "HIlow_fileName = saveDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.HIlow_CONUS_12utc.p'\n",
    "\n",
    "CTP_all   = pickle.load(open(CTP_fileName, \"rb\"))\n",
    "HIlow_all = pickle.load(open(HIlow_fileName, \"rb\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T18:56:32.723161Z",
     "start_time": "2020-09-08T18:56:32.716531Z"
    }
   },
   "outputs": [],
   "source": [
    "# Probably could've done this first... But let's use just days from JJA \n",
    "iMon    = np.where((ds_utc12.UTC_mon.values>=6) & (ds_utc12.UTC_mon.values<=8) )[0]\n",
    "\n",
    "CTP_jja = CTP_all[iMon,:,:]\n",
    "HI_jja  = HIlow_all[iMon,:,:]\n",
    "\n",
    "# Going to also isolate the years here \n",
    "yrs_jja = ds_utc12.UTC_yr.values[iMon]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T18:56:32.729036Z",
     "start_time": "2020-09-08T18:56:32.726666Z"
    }
   },
   "outputs": [],
   "source": [
    "# So define dimensions again \n",
    "nTime = np.shape(CTP_jja)[0]\n",
    "nLat  = np.shape(CTP_jja)[1]\n",
    "nLon  = np.shape(CTP_jja)[2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T18:56:32.876625Z",
     "start_time": "2020-09-08T18:56:32.730971Z"
    }
   },
   "outputs": [],
   "source": [
    "# Could've done this before too - apply land mask \n",
    "dataDir  = '/Users/meganfowler/Documents/NCAR/Analysis/Coupling_initial/data/hrSim_CONUS/'\n",
    "fileName = 'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.clm2.h0.1980-01.nc'\n",
    "data = xr.open_dataset(dataDir+fileName, decode_times=False)\n",
    "\n",
    "# Read in land mask and limit to lat/lon used here\n",
    "landMask = data.landmask.values\n",
    "\n",
    "iLat = np.where((data.lat.values>=np.min(ds_utc12.lat.values)) & (data.lat.values<=np.max(ds_utc12.lat.values)))[0]\n",
    "iLon = np.where((data.lon.values>=np.min(ds_utc12.lon.values)) & (data.lon.values<=np.max(ds_utc12.lon.values)))[0]\n",
    "\n",
    "landMask_sel = landMask[iLat,:]\n",
    "landMask_sel = landMask_sel[:,iLon]\n",
    "\n",
    "CTP_jja = CTP_jja*landMask_sel\n",
    "HI_jja  = HI_jja*landMask_sel\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "To start, I'm going to apply the fairly rigid classification scheme from Findell and Eltahir (2003). Note though that there are valid reasons for *not* doing that and following something more flexible and data-dependent, like Ferguson and Wood (2011). We should apply that later and check for differences.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-03T19:12:25.843947Z",
     "start_time": "2020-09-03T19:12:25.716720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check distribution of HI_low\n",
    "#   FE03 keeps HI in units of ˚C, but ours in K. \n",
    "#   In retrospect though, since it's just a measure of the difference in T and Td between levels, \n",
    "#   that *difference* should be unaffected by which units of temperature were used (for ˚C vs. K, at least). \n",
    "\n",
    "plt.hist(HI_jja.reshape([nTime*nLat*nLon,1]))\n",
    "plt.show()\n",
    "\n",
    "# And that distribution looks quite reasonable! See FE03_part2 Fig. 4 for example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T17:33:40.087981Z",
     "start_time": "2020-09-08T17:33:37.676558Z"
    }
   },
   "outputs": [],
   "source": [
    "# Going to make a key for classification: \n",
    "#  1 -- Atmospherically controlled (too stable for rain)\n",
    "#  2 -- Atmospherically controlled (too dry for rain)\n",
    "#  3 -- Atmospherically controlled (rainfall likely regardless of soil moisture)\n",
    "#  4 -- Wet soil advantage \n",
    "#  5 -- Dry soil advantage \n",
    "#  6 -- Transition region \n",
    "#  7 -- Shallow clouds likely over wet, but no rain. This one doesn't really count. \n",
    "\n",
    "regime_jja = np.full([nTime, nLat, nLon], np.nan)\n",
    "\n",
    "# Look at each profile individually \n",
    "for iT in range(nTime):\n",
    "    for iLat in range(nLat):\n",
    "        for iLon in range(nLon):\n",
    "            \n",
    "            CTP_point = CTP_jja[iT, iLat, iLon]\n",
    "            HI_point  = HI_jja[iT, iLat, iLon]\n",
    "            \n",
    "            # First, let's pick out soundings that are atmospherically controlled (too stable)\n",
    "            if CTP_point<0: \n",
    "                regime_jja[iT, iLat, iLon] = 1\n",
    "            # Atmospherically controlled and too dry for rain    \n",
    "            elif ( (CTP_point>=0) & (HI_point>=15) ):\n",
    "                regime_jja[iT,iLat,iLon] = 2\n",
    "            # Atmospherically controlled and lead to rain over any soil moisture\n",
    "            elif ( (CTP_point>=0) & (HI_point<5) ):\n",
    "                regime_jja[iT,iLat,iLon] = 3\n",
    "            # Wet soil advantage for precipitation \n",
    "            elif ((CTP_point>=0) & (HI_point>=5) & (HI_point<10) ):\n",
    "                regime_jja[iT,iLat,iLon] = 4\n",
    "            # Dry soil advantage for precipitation     \n",
    "            elif ((CTP_point>=200) & (HI_point>=10) & (HI_point<15) ):\n",
    "                regime_jja[iT,iLat,iLon] = 5\n",
    "            # Transition region \n",
    "            elif ((CTP_point>=50) & (CTP_point<200) & (HI_point>=10) & (HI_point<15) ):\n",
    "                regime_jja[iT,iLat,iLon] = 6\n",
    "            # Section just to catch shallow clouds likely over wet; no rain expected in this category though\n",
    "            elif ((CTP_point<50) & (HI_point>=10) & (HI_point<15) ):\n",
    "                regime_jja[iT,iLat,iLon] = 7\n",
    "            # Add a catch-other regions option (ruh-roh section)\n",
    "            elif np.isfinite(CTP_point): \n",
    "                regime_jja[iT,iLat,iLon] = 8\n",
    "                \n",
    "regime_jja = regime_jja\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-03T21:25:41.816011Z",
     "start_time": "2020-09-03T21:25:41.813659Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define array of labels for each category \n",
    "labels = ['Atm', 'Atm', 'Atm','Wet SM', 'Dry SM','Transition','No rain']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-03T21:25:42.801968Z",
     "start_time": "2020-09-03T21:25:42.323666Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up figure to plot a map of regime (choose a random day)\n",
    "fig, ax1 = plt.subplots(1,1, figsize=(7, 5), \n",
    "                        facecolor='w', edgecolor='k',\n",
    "                        subplot_kw={'projection':ccrs.PlateCarree()})\n",
    "\n",
    "# Set US extent\n",
    "ax1.set_extent([220,300,18,55])\n",
    "\n",
    "cmap = plt.get_cmap('Set2', 7)\n",
    "\n",
    "contour_plot = ax1.pcolormesh(ds_utc12.lon,ds_utc12.lat,regime_jja[100,:,:],\n",
    "                                    vmin=1,\n",
    "                                    vmax=8,\n",
    "                                    cmap=cmap)\n",
    "ax1.coastlines()\n",
    "# Add colorbar\n",
    "ax_position = ax1.get_position()\n",
    "cbar_ax     = fig.add_axes([ax_position.x0, ax_position.y0-0.07, ax_position.width, 0.05])\n",
    "cbar        = plt.colorbar(contour_plot, orientation='horizontal', cax=cbar_ax)\n",
    "cbar.set_ticks(np.linspace(1.5,7.5,7))\n",
    "cbar.set_ticklabels(labels)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-03T21:25:49.528721Z",
     "start_time": "2020-09-03T21:25:49.388048Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of all gridcells and all days \n",
    "\n",
    "fig, ax1 = plt.subplots(1,1, figsize=(7, 5))\n",
    "\n",
    "ax1.hist(regime_jja.reshape([nTime*nLat*nLon, 1]), bins=[1,2,3,4,5,6,7,8], \n",
    "         align='left',\n",
    "         edgecolor='k',\n",
    "         color = 'powderblue')\n",
    "\n",
    "ax1.set_title('CI & HI Category: JJA [All days + All CONUS]')\n",
    "\n",
    "ax1.set_xticks(np.arange(1,8))\n",
    "ax1.set_xticklabels(labels,fontsize=12)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Follow methods of FE03_Part2 to classify each location by amount of time spent in each regime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on Findell and Eltahir (2003; Part 2), the organization should be as follows for each station (gridcell in our case) and each summer): \n",
    "* If 80%+ of days are atmospherically controlled, station is labeled as atmo controlled for the summer <br><br>\n",
    "* <b>Level 1 options (< 80% of days atmo. controlled): </b>\n",
    "* ---- Positive feedback (wet soil advantage): If more than 50% of the remaining days (those not atmo. controlled) are wet-soil advantage.\n",
    "* ---- Negative feedback (dry soil advantage): If more than 50% of remaining days fall in dry soil advantage. \n",
    "* ---- Transitional station: If more than 50% of remaining days are in the transition regime. <br><br>\n",
    "* <b> Level 2 options (<80% of days atmo controlled; <50% of remaining days fall into one category) </b>\n",
    "* ---- Positive feedback (wet soil advantage): less than 20% of days not atmo. controlled fall in dry soil advantage.\n",
    "* ---- Negative feedback (dry soil advantage): less than 20% of days not atmo. controlled fall into wet soil advantage. \n",
    "* ---- Transitional: Whatever's left after above filtering. \n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T17:33:51.257224Z",
     "start_time": "2020-09-08T17:33:51.251441Z"
    }
   },
   "outputs": [],
   "source": [
    "# Simplify regime scheme so that any atmosphere-controlled day has same value:\n",
    "regime_Simple                  = regime_jja.copy()\n",
    "regime_Simple[regime_Simple<4] = 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T17:33:51.771057Z",
     "start_time": "2020-09-08T17:33:51.762046Z"
    }
   },
   "outputs": [],
   "source": [
    "# Now separate each year... \n",
    "regimeSimple_yearly = np.full([3, 92, nLat, nLon], np.nan)  # Split the time dimension up to be year x JJA-days x lat x lon\n",
    "\n",
    "regimeSimple_yearly[0,:,:,:] = regime_Simple[np.where(yrs_jja==1979)[0],:,:]\n",
    "regimeSimple_yearly[1,:,:,:] = regime_Simple[np.where(yrs_jja==1980)[0],:,:]\n",
    "regimeSimple_yearly[2,:,:,:] = regime_Simple[np.where(yrs_jja==1981)[0],:,:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T17:33:52.710703Z",
     "start_time": "2020-09-08T17:33:52.632347Z"
    }
   },
   "outputs": [],
   "source": [
    "# Now let's classify each grid point per summer \n",
    "# New key: \n",
    "#    0 ('A')  -- Atmo controlled \n",
    "#    1 ('W1') -- Level 1 wet soil advantage \n",
    "#    2 ('D1') -- Level 1 dry soil advantage\n",
    "#    3 ('T1') -- Level 1 transitional regime\n",
    "#    4 ('W2') -- Level 2 wet soil advantage\n",
    "#    5 ('D2') -- Level 2 dry soil advantage \n",
    "#    6 ('T2') -- Level 2 transitional regime \n",
    "\n",
    "# Define empty array to house classification by summer \n",
    "summerClass = np.full([3, nLat, nLon], np.nan)\n",
    "\n",
    "# There are 92 days in JJA each year. So this is where we start for getting percentages \n",
    "allDays = 92\n",
    "atmoCutoff = round(allDays*0.8)\n",
    "\n",
    "for iYr in range(3):\n",
    "    for iLat in range(nLat):\n",
    "        for iLon in range(nLon):\n",
    "            yrData = regimeSimple_yearly[iYr,:,iLat,iLon]\n",
    "            \n",
    "            # Find percent of days atmospherically controlled \n",
    "            iAtmo = np.where(yrData==1)[0]\n",
    "            \n",
    "            if len(iAtmo) >= atmoCutoff:           # Easy: atmospherically controlled summer and grid point \n",
    "                summerClass[iYr,iLat,iLon] = 0\n",
    "                \n",
    "            else:                                  # Next step of processing: Is grid point level 1? \n",
    "                restOfDays = yrData[yrData>1]      # Isolate the days that aren't atmospherically controlled \n",
    "                nRestDays  = len(restOfDays)       # Number of days left to consider \n",
    "                \n",
    "                lev1cutoff = round(nRestDays*0.5)  # Number of days needed to be classified as level one\n",
    "                lev2cutoff = round(nRestDays*0.2)  # Number of days for classifying level two \n",
    "                \n",
    "                # Get number of remaining days falling into each category \n",
    "                iWet        = np.where(restOfDays==4)[0]\n",
    "                iDry        = np.where(restOfDays==5)[0]\n",
    "                iTransition = np.where(restOfDays==6)[0]\n",
    "                \n",
    "                # Assign level 1 classifications \n",
    "                if len(iWet)>=lev1cutoff: \n",
    "                    summerClass[iYr,iLat,iLon] = 1\n",
    "                elif len(iDry)>=lev1cutoff:\n",
    "                    summerClass[iYr,iLat,iLon] = 2\n",
    "                elif len(iTransition)>=lev1cutoff:\n",
    "                    summerClass[iYr,iLat,iLon] = 3\n",
    "                    \n",
    "                # If not the case, let's go into level 2 classifications \n",
    "                else:\n",
    "                    if len(iDry)<lev2cutoff:\n",
    "                        summerClass[iYr,iLat,iLon] = 4\n",
    "                    elif len(iWet)<lev2cutoff:\n",
    "                        summerClass[iYr,iLat,iLon] = 5\n",
    "                    else: \n",
    "                        summerClass[iYr,iLat,iLon] = 6\n",
    "                \n",
    "    summerClass[iYr,:,:] = summerClass[iYr,:,:]*landMask_sel\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-04T16:55:31.849771Z",
     "start_time": "2020-09-04T16:55:31.735317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(summerClass[1,:,:].reshape([nLat*nLon,1]),bins=[0,1,2,3,4,5,6,7],align='left')\n",
    "plt.show()\n",
    "         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T17:34:22.156690Z",
     "start_time": "2020-09-08T17:34:22.154194Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define array of labels for each category \n",
    "labels = ['A', 'W1', 'D1','T1', 'W2','D2','T2']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T17:35:12.992238Z",
     "start_time": "2020-09-08T17:35:12.869960Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up figure to plot a map of regime (choose a random day)\n",
    "fig, ax1 = plt.subplots(1,1, figsize=(7, 5), \n",
    "                        facecolor='w', edgecolor='k',\n",
    "                        subplot_kw={'projection':ccrs.PlateCarree()})\n",
    "\n",
    "# Set US extent\n",
    "ax1.set_extent([220,300,18,55])\n",
    "\n",
    "cmap = plt.get_cmap('Set2', 7)\n",
    "\n",
    "contour_plot = ax1.pcolormesh(ds_utc12.lon,ds_utc12.lat,summerClass[2,:,:],\n",
    "                                    vmin=0,\n",
    "                                    vmax=7,\n",
    "                                    cmap=cmap)\n",
    "\n",
    "ax1.coastlines()\n",
    "ax1.set_title('1981')\n",
    "# Add colorbar\n",
    "ax_position = ax1.get_position()\n",
    "cbar_ax     = fig.add_axes([ax_position.x0, ax_position.y0-0.07, ax_position.width, 0.05])\n",
    "cbar        = plt.colorbar(contour_plot, orientation='horizontal', cax=cbar_ax)\n",
    "cbar.set_ticks(np.linspace(0.5,6.5,7))\n",
    "cbar.set_ticklabels(labels)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's try a similar system, but since we only have three years let's look at 80% of all days vs. >50% of all JJA days, etc.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-04T17:47:49.072704Z",
     "start_time": "2020-09-04T17:47:49.064926Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmo Cutoff:  221\n",
      "Number of days atmospherically controlled:  225\n",
      "This station is deemed atmospherically controlled.\n"
     ]
    }
   ],
   "source": [
    "iLat = 23\n",
    "iLon = 46\n",
    "allData = regime_Simple[:,iLat,iLon]\n",
    "\n",
    "#plt.hist(yrData, bins=[0,1,2,3,4,5,6,7,8],align='left')\n",
    "\n",
    "allDays = nTime\n",
    "atmoCutoff = round(allDays*0.8)\n",
    "print('Atmo Cutoff: ', atmoCutoff)\n",
    "\n",
    "# Find percent of days atmospherically controlled \n",
    "iAtmo = np.where(allData==1)[0]\n",
    "print('Number of days atmospherically controlled: ', len(iAtmo))\n",
    "\n",
    "\n",
    "if len(iAtmo) >= atmoCutoff:           # Easy: atmospherically controlled summer and grid point \n",
    "    #summerClass[iYr,iLat,iLon] = 0\n",
    "    print('This station is deemed atmospherically controlled.')\n",
    "else: \n",
    "    print('Not atmo controlled... more analysis needed.')\n",
    "    print()\n",
    "    restOfDays = allData[allData>1]      # Isolate the days that aren't atmospherically controlled \n",
    "    nRestDays  = len(restOfDays)         # Number of days left to consider\n",
    "    print('Number of days not atmo controlled: ', nRestDays)\n",
    "    \n",
    "    lev1cutoff = round(nRestDays*0.5)  # Number of days needed to be classified as level one\n",
    "    lev2cutoff = round(nRestDays*0.2)  # Number of days for classifying level two \n",
    "    print('Number of days required to be one category to be level 1: ', lev1cutoff)\n",
    "    print('Number of days required to not be one category to be level 2: ', lev2cutoff)\n",
    "        \n",
    "    # Get number of remaining days falling into each category \n",
    "    iWet        = np.where(restOfDays==4)[0]\n",
    "    iDry        = np.where(restOfDays==5)[0]\n",
    "    iTransition = np.where(restOfDays==6)[0]\n",
    "    \n",
    "    print('Number of days in wet regime: ', len(iWet))\n",
    "    print('Number of days in dry regime: ', len(iDry))\n",
    "    print('Number of days in transition: ', len(iTransition))\n",
    "    \n",
    "    # Assign level 1 classifications \n",
    "    if len(iWet)>=lev1cutoff: \n",
    "        #summerClass_byDay[iYr,iLat,iLon] = 1\n",
    "        print('This station is level 1 wet advantage')\n",
    "    elif len(iDry)>=lev1cutoff:\n",
    "        #summerClass_byDay[iYr,iLat,iLon] = 2\n",
    "        print('This station is level 1 dry advantage')\n",
    "    elif len(iTransition)>=lev1cutoff:\n",
    "        #summerClass_byDay[iYr,iLat,iLon] = 3\n",
    "        print('This station is level 1 transitional')\n",
    "\n",
    "    # If not the case, let's go into level 2 classifications \n",
    "    else:\n",
    "        if len(iDry)<lev2cutoff:\n",
    "            #summerClass_byDay[iYr,iLat,iLon] = 4\n",
    "            print('This station is level 2 wet advantage')\n",
    "        elif len(iWet)<lev2cutoff:\n",
    "            #summerClass_byDay[iYr,iLat,iLon] = 5\n",
    "            print('This station is level 2 dry advantage')\n",
    "        else: \n",
    "            #summerClass_byDay[iYr,iLat,iLon] = 6\n",
    "            print('This station is level 2 transitional')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Actually execute the plan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T17:39:11.599248Z",
     "start_time": "2020-09-08T17:39:11.556763Z"
    }
   },
   "outputs": [],
   "source": [
    "# Now let's classify each grid point per summer \n",
    "# New key: \n",
    "#    0 ('A')  -- Atmo controlled \n",
    "#    1 ('W1') -- Level 1 wet soil advantage \n",
    "#    2 ('D1') -- Level 1 dry soil advantage\n",
    "#    3 ('T1') -- Level 1 transitional regime\n",
    "#    4 ('W2') -- Level 2 wet soil advantage\n",
    "#    5 ('D2') -- Level 2 dry soil advantage \n",
    "#    6 ('T2') -- Level 2 transitional regime \n",
    "\n",
    "# Define empty array to house classification by summer \n",
    "summerClass_byDay = np.full([nLat, nLon], np.nan)\n",
    "summerClass_atmoDays = np.full([nLat, nLon], np.nan)\n",
    "\n",
    "# There are 92 days in JJA each year. So this is where we start for getting percentages \n",
    "allDays = nTime\n",
    "atmoCutoff = round(allDays*0.80)\n",
    "\n",
    "for iLat in range(nLat):\n",
    "    for iLon in range(nLon):\n",
    "        allData = regime_Simple[:,iLat,iLon]\n",
    "\n",
    "        # Find percent of days atmospherically controlled \n",
    "        iAtmo = np.where(allData==1)[0]\n",
    "        summerClass_atmoDays[iLat,iLon] = len(iAtmo)\n",
    "\n",
    "        if len(iAtmo) >= atmoCutoff:           # Easy: atmospherically controlled summer and grid point \n",
    "            summerClass_byDay[iLat,iLon] = 0\n",
    "\n",
    "        else:                                  # Next step of processing: Is grid point level 1? \n",
    "            restOfDays = allData[allData>1]    # Isolate the days that aren't atmospherically controlled \n",
    "            nRestDays  = len(restOfDays)       # Number of days left to consider \n",
    "\n",
    "            lev1cutoff = round(nRestDays*0.5)  # Number of days needed to be classified as level one\n",
    "            lev2cutoff = round(nRestDays*0.2)  # Number of days for classifying level two \n",
    "\n",
    "            # Get number of remaining days falling into each category \n",
    "            iWet        = np.where(restOfDays==4)[0]\n",
    "            iDry        = np.where(restOfDays==5)[0]\n",
    "            iTransition = np.where(restOfDays==6)[0]\n",
    "\n",
    "            # Assign level 1 classifications \n",
    "            if len(iWet)>=lev1cutoff: \n",
    "                summerClass_byDay[iLat,iLon] = 1\n",
    "            elif len(iDry)>=lev1cutoff:\n",
    "                summerClass_byDay[iLat,iLon] = 2\n",
    "            elif len(iTransition)>=lev1cutoff:\n",
    "                summerClass_byDay[iLat,iLon] = 3\n",
    "\n",
    "            # If not the case, let's go into level 2 classifications \n",
    "            else:\n",
    "                if len(iDry)<lev2cutoff:\n",
    "                    summerClass_byDay[iLat,iLon] = 4\n",
    "                elif len(iWet)<lev2cutoff:\n",
    "                    summerClass_byDay[iLat,iLon] = 5\n",
    "                else: \n",
    "                    summerClass_byDay[iLat,iLon] = 6\n",
    "\n",
    "summerClass_byDay = summerClass_byDay*landMask_sel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T17:39:12.148599Z",
     "start_time": "2020-09-08T17:39:12.032997Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up figure to plot a map of regime (choose a random day)\n",
    "fig, ax1 = plt.subplots(1,1, figsize=(7, 5), \n",
    "                        facecolor='w', edgecolor='k',\n",
    "                        subplot_kw={'projection':ccrs.PlateCarree()})\n",
    "\n",
    "# Set US extent\n",
    "ax1.set_extent([220,300,18,55])\n",
    "\n",
    "cmap = plt.get_cmap('Set2', 7)\n",
    "\n",
    "contour_plot = ax1.pcolormesh(ds_utc12.lon,ds_utc12.lat,summerClass_byDay,\n",
    "                                    vmin=0,\n",
    "                                    vmax=7,\n",
    "                                    cmap=cmap)\n",
    "\n",
    "ax1.coastlines()\n",
    "# Add colorbar\n",
    "ax_position = ax1.get_position()\n",
    "cbar_ax     = fig.add_axes([ax_position.x0, ax_position.y0-0.07, ax_position.width, 0.05])\n",
    "cbar        = plt.colorbar(contour_plot, orientation='horizontal', cax=cbar_ax)\n",
    "cbar.set_ticks(np.linspace(0.5,6.5,7))\n",
    "cbar.set_ticklabels(labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T17:39:51.998048Z",
     "start_time": "2020-09-08T17:39:51.993429Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of days in record:             276\n",
      "Cutoff to be called atmo controlled:  221\n",
      "Number of gridcells hitting threshold of 80%:  1187\n",
      "Number of gridcells hitting threshold if 85%:  934\n",
      "Number of gridcells hitting threshold if 75%:  1358\n"
     ]
    }
   ],
   "source": [
    "# How sensitive is that 80% cutoff? That is, how many 'atmo controlled' classifications are within 5% of cutoff?\n",
    "allDays = nTime\n",
    "atmoCutoff = round(allDays*0.8)\n",
    "\n",
    "print('Number of days in record:            ', allDays)\n",
    "print('Cutoff to be called atmo controlled: ', atmoCutoff)\n",
    "print('Number of gridcells hitting threshold of 80%: ', len(np.where(summerClass_atmoDays>=atmoCutoff)[0]))\n",
    "\n",
    "cutoff75 = round(allDays*0.75)\n",
    "cutoff85 = round(allDays*0.85)\n",
    "print('Number of gridcells hitting threshold if 85%: ', len(np.where(summerClass_atmoDays>=cutoff85)[0]))\n",
    "print('Number of gridcells hitting threshold if 75%: ', len(np.where(summerClass_atmoDays>=cutoff75)[0]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How does CTP/HI compare to afternoon precip occurrence? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T19:29:40.781106Z",
     "start_time": "2020-09-08T19:29:32.429320Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with Precip...\n"
     ]
    }
   ],
   "source": [
    "mainDir = '/Users/meganfowler/Documents/NCAR/Analysis/Coupling_initial/data/hrSim_CONUS/'\n",
    "\n",
    "filePr1 = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1979_PRECT-3hrly_Try2.nc'\n",
    "filePr2 = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1980_PRECT-3hrly_Try2.nc'\n",
    "filePr3 = mainDir+'f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1981_PRECT-3hrly_Try2.nc'\n",
    "\n",
    "# Read in files that have profiles and concat into one DS\n",
    "Pr_1979 = xr.open_dataset(filePr1, decode_times=True)\n",
    "Pr_1980 = xr.open_dataset(filePr2, decode_times=True)\n",
    "Pr_1981 = xr.open_dataset(filePr3, decode_times=True)\n",
    "Pr_full = xr.concat([Pr_1979, Pr_1980, Pr_1981], dim=\"time\")\n",
    "print('Done with Precip...')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T19:36:08.490160Z",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 192, lon: 288, time: 8760)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
       "  * time     (time) object 1979-01-01 00:00:00 ... 1981-12-31 21:00:00\n",
       "Data variables:\n",
       "    P_3hr    (time, lat, lon) float32 6.000612e-19 ... 4.0360966e-08\n",
       "    UTC_hr   (time) float32 0.0 3.0 6.0 9.0 12.0 ... 9.0 12.0 15.0 18.0 21.0\n",
       "    UTC_day  (time) float32 1.0 1.0 1.0 1.0 1.0 1.0 ... 31.0 31.0 31.0 31.0 31.0\n",
       "    UTC_mon  (time) float32 1.0 1.0 1.0 1.0 1.0 1.0 ... 12.0 12.0 12.0 12.0 12.0\n",
       "    UTC_yr   (time) float32 1979.0 1979.0 1979.0 1979.0 ... 1981.0 1981.0 1981.0\n",
       "Attributes:\n",
       "    creation_date:  Tue Sep  8 13:22:28 MDT 2020\n",
       "    Conventions:    None\n",
       "    source_file:    /glade/scratch/mdfowler/processedData/f.e21.FHIST_BGC.f09...\n",
       "    title:          NCL used to get UTC and compute 3-hr sum of PRECT.</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-4907731f-5c9e-4b3d-9d9a-1d599f2c9577' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4907731f-5c9e-4b3d-9d9a-1d599f2c9577' 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'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>time</span>: 8760</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-173c1b72-c420-4ba3-be28-917a4ac90c7b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-173c1b72-c420-4ba3-be28-917a4ac90c7b' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-4afc7744-695e-4e3f-a324-c5f5fd99db40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4afc7744-695e-4e3f-a324-c5f5fd99db40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5acc6a7-b3d2-4549-8179-5cf965de1f45' class='xr-var-data-in' type='checkbox'><label for='data-a5acc6a7-b3d2-4549-8179-5cf965de1f45' title='Show/Hide data repr'><svg 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</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-3429c722-bda1-4e62-92ef-a427b2c10269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3429c722-bda1-4e62-92ef-a427b2c10269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d888ebc-2ca1-4578-ad1e-e22e05c09174' class='xr-var-data-in' type='checkbox'><label for='data-5d888ebc-2ca1-4578-ad1e-e22e05c09174' title='Show/Hide data repr'><svg 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</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</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'>object</div><div class='xr-var-preview xr-preview'>1979-01-01 00:00:00 ... 1981-12-...</div><input id='attrs-6f5fb673-e604-484f-81df-232a968c1a50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f5fb673-e604-484f-81df-232a968c1a50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d95709d3-1f1a-48bb-9ed3-887c976d8f4a' class='xr-var-data-in' type='checkbox'><label for='data-d95709d3-1f1a-48bb-9ed3-887c976d8f4a' title='Show/Hide data repr'><svg 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>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1979, 1, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 1, 3, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 1, 6, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(1981, 12, 31, 15, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1981, 12, 31, 18, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1981, 12, 31, 21, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3914b919-18e9-482a-863a-6e54bf29c1b4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3914b919-18e9-482a-863a-6e54bf29c1b4' class='xr-section-summary' >Data variables: <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-var-name'><span>P_3hr</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>6.000612e-19 ... 4.0360966e-08</div><input id='attrs-36feb61b-044e-434b-95ae-280a4d7f7142' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36feb61b-044e-434b-95ae-280a4d7f7142' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9943c2ba-204b-4e7e-adb1-d586c9e42dab' class='xr-var-data-in' type='checkbox'><label for='data-9943c2ba-204b-4e7e-adb1-d586c9e42dab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>long_name :</span></dt><dd>3-hr accumulated Precip</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[[6.00061218e-19, 6.00061218e-19, 6.00061218e-19, ...,\n",
       "         6.00061218e-19, 6.00061218e-19, 6.00061218e-19],\n",
       "        [1.14658529e-19, 9.45967626e-20, 7.09554237e-20, ...,\n",
       "         1.49121794e-19, 1.41386738e-19, 1.29808704e-19],\n",
       "        [3.98409970e-18, 4.09625381e-18, 4.05519133e-18, ...,\n",
       "         3.58345229e-18, 3.68044253e-18, 3.82453905e-18],\n",
       "        ...,\n",
       "        [1.27180974e-10, 1.14652905e-10, 1.03233491e-10, ...,\n",
       "         1.66209130e-10, 1.53245347e-10, 1.40203862e-10],\n",
       "        [2.27994880e-11, 2.05610338e-11, 1.81243284e-11, ...,\n",
       "         2.73501048e-11, 2.50582177e-11, 2.37441976e-11],\n",
       "        [1.63518955e-11, 1.63519077e-11, 1.63519181e-11, ...,\n",
       "         1.63518591e-11, 1.63518712e-11, 1.63518712e-11]],\n",
       "\n",
       "       [[3.25278023e-19, 3.25278023e-19, 3.25278023e-19, ...,\n",
       "         3.25278023e-19, 3.25278023e-19, 3.25278023e-19],\n",
       "        [2.42659976e-20, 1.80561298e-20, 1.33459008e-20, ...,\n",
       "         5.11129505e-20, 4.09157167e-20, 3.19385679e-20],\n",
       "        [1.11697114e-18, 9.00562493e-19, 6.94272179e-19, ...,\n",
       "         1.66086741e-18, 1.49522863e-18, 1.31405395e-18],\n",
       "...\n",
       "        [8.16551733e-08, 8.35936333e-08, 8.36129743e-08, ...,\n",
       "         6.90721080e-08, 7.50312594e-08, 7.91202197e-08],\n",
       "        [6.15389197e-08, 6.06683415e-08, 5.94098779e-08, ...,\n",
       "         6.33680557e-08, 6.30739123e-08, 6.25501357e-08],\n",
       "        [3.87644583e-08, 3.87644619e-08, 3.87644619e-08, ...,\n",
       "         3.87644619e-08, 3.87644619e-08, 3.87644619e-08]],\n",
       "\n",
       "       [[3.25417684e-17, 3.25417750e-17, 3.25417750e-17, ...,\n",
       "         3.25417684e-17, 3.25417684e-17, 3.25417684e-17],\n",
       "        [7.09985599e-20, 7.57015340e-20, 8.07350831e-20, ...,\n",
       "         2.03872588e-20, 2.14471696e-20, 2.41932283e-20],\n",
       "        [9.61179187e-21, 1.45133201e-20, 2.21142019e-20, ...,\n",
       "         7.83908565e-21, 6.90272851e-21, 8.02542586e-21],\n",
       "        ...,\n",
       "        [3.17349702e-08, 3.32620544e-08, 3.54363614e-08, ...,\n",
       "         2.92265341e-08, 2.97279357e-08, 3.04919823e-08],\n",
       "        [4.35281251e-08, 4.40442989e-08, 4.46532873e-08, ...,\n",
       "         4.22211599e-08, 4.24766817e-08, 4.29772768e-08],\n",
       "        [4.03609626e-08, 4.03609661e-08, 4.03609661e-08, ...,\n",
       "         4.03609626e-08, 4.03609626e-08, 4.03609661e-08]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTC_hr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 3.0 6.0 9.0 ... 15.0 18.0 21.0</div><input id='attrs-c2305f33-a2b6-4961-9e42-cbe6337f394b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2305f33-a2b6-4961-9e42-cbe6337f394b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6c07b33-3469-4727-9aa8-75b6ab6656bb' class='xr-var-data-in' type='checkbox'><label for='data-a6c07b33-3469-4727-9aa8-75b6ab6656bb' title='Show/Hide data repr'><svg 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>Hour in UTC</dd><dt><span>calendar :</span></dt><dd>noleap</dd></dl></div><div class='xr-var-data'><pre>array([ 0.,  3.,  6., ..., 15., 18., 21.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTC_day</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 31.0 31.0 31.0</div><input id='attrs-d086f79a-1878-408f-95ad-9ff992661a3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d086f79a-1878-408f-95ad-9ff992661a3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14765030-b34e-4628-9763-a66e7ce3a6ae' class='xr-var-data-in' type='checkbox'><label for='data-14765030-b34e-4628-9763-a66e7ce3a6ae' title='Show/Hide data repr'><svg 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>Day in UTC</dd><dt><span>calendar :</span></dt><dd>noleap</dd></dl></div><div class='xr-var-data'><pre>array([ 1.,  1.,  1., ..., 31., 31., 31.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTC_mon</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 12.0 12.0 12.0</div><input id='attrs-8cea4b24-680f-4ae1-91c9-bbd7531f1c50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cea4b24-680f-4ae1-91c9-bbd7531f1c50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c45d0540-71d7-431a-817b-814a0eea71d5' class='xr-var-data-in' type='checkbox'><label for='data-c45d0540-71d7-431a-817b-814a0eea71d5' title='Show/Hide data repr'><svg 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>Mon in UTC</dd><dt><span>calendar :</span></dt><dd>noleap</dd></dl></div><div class='xr-var-data'><pre>array([ 1.,  1.,  1., ..., 12., 12., 12.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UTC_yr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1979.0 1979.0 ... 1981.0 1981.0</div><input id='attrs-2b4fb9b9-9a67-464e-bcd9-4cafc3dde728' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b4fb9b9-9a67-464e-bcd9-4cafc3dde728' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6edc1931-46e4-43fe-801e-251e14777034' class='xr-var-data-in' type='checkbox'><label for='data-6edc1931-46e4-43fe-801e-251e14777034' title='Show/Hide data repr'><svg 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>Yr in UTC</dd><dt><span>calendar :</span></dt><dd>noleap</dd></dl></div><div class='xr-var-data'><pre>array([1979., 1979., 1979., ..., 1981., 1981., 1981.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-61d1b146-79e6-4cd0-b22e-eb5f067f2be1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-61d1b146-79e6-4cd0-b22e-eb5f067f2be1' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>creation_date :</span></dt><dd>Tue Sep  8 13:22:28 MDT 2020</dd><dt><span>Conventions :</span></dt><dd>None</dd><dt><span>source_file :</span></dt><dd>/glade/scratch/mdfowler/processedData/f.e21.FHIST_BGC.f09_f09_mg17.hourlyOutput.001.cam.h1.1979_PRECT.nc</dd><dt><span>title :</span></dt><dd>NCL used to get UTC and compute 3-hr sum of PRECT.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 192, lon: 288, time: 8760)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
       "  * time     (time) object 1979-01-01 00:00:00 ... 1981-12-31 21:00:00\n",
       "Data variables:\n",
       "    P_3hr    (time, lat, lon) float32 6.000612e-19 ... 4.0360966e-08\n",
       "    UTC_hr   (time) float32 0.0 3.0 6.0 9.0 12.0 ... 9.0 12.0 15.0 18.0 21.0\n",
       "    UTC_day  (time) float32 1.0 1.0 1.0 1.0 1.0 1.0 ... 31.0 31.0 31.0 31.0 31.0\n",
       "    UTC_mon  (time) float32 1.0 1.0 1.0 1.0 1.0 1.0 ... 12.0 12.0 12.0 12.0 12.0\n",
       "    UTC_yr   (time) float32 1979.0 1979.0 1979.0 1979.0 ... 1981.0 1981.0 1981.0\n",
       "Attributes:\n",
       "    creation_date:  Tue Sep  8 13:22:28 MDT 2020\n",
       "    Conventions:    None\n",
       "    source_file:    /glade/scratch/mdfowler/processedData/f.e21.FHIST_BGC.f09...\n",
       "    title:          NCL used to get UTC and compute 3-hr sum of PRECT."
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pr_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T19:41:23.221663Z",
     "start_time": "2020-09-08T19:41:23.217661Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8760, 192, 288)\n"
     ]
    }
   ],
   "source": [
    "# Get individual arrays \n",
    "P_3hr = Pr_full.P_3hr.values\n",
    "pr_Hr = Pr_full.UTC_hr.values\n",
    "lat   = Pr_full.lat.values\n",
    "lon   = Pr_full.lon.values\n",
    "\n",
    "print(np.shape(P_3hr))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T19:41:25.102070Z",
     "start_time": "2020-09-08T19:41:23.726450Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8760, 43, 65)\n"
     ]
    }
   ],
   "source": [
    "# Limit to range of US \n",
    "iLat = np.where((lat>=np.min(ds_utc12.lat.values)) & (lat<=np.max(ds_utc12.lat.values)))[0]\n",
    "iLon = np.where((lon>=np.min(ds_utc12.lon.values)) & (lon<=np.max(ds_utc12.lon.values)))[0]\n",
    "\n",
    "lat = lat[iLat]\n",
    "lon = lon[iLon]\n",
    "P_3hr = P_3hr[:,iLat,:]\n",
    "P_3hr = P_3hr[:,:,iLon]\n",
    "\n",
    "print(np.shape(P_3hr))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T19:41:32.803206Z",
     "start_time": "2020-09-08T19:41:32.799671Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([220.  , 221.25, 222.5 , 223.75, 225.  , 226.25, 227.5 , 228.75,\n",
       "       230.  , 231.25, 232.5 , 233.75, 235.  , 236.25, 237.5 , 238.75,\n",
       "       240.  , 241.25, 242.5 , 243.75, 245.  , 246.25, 247.5 , 248.75,\n",
       "       250.  , 251.25, 252.5 , 253.75, 255.  , 256.25, 257.5 , 258.75,\n",
       "       260.  , 261.25, 262.5 , 263.75, 265.  , 266.25, 267.5 , 268.75,\n",
       "       270.  , 271.25, 272.5 , 273.75, 275.  , 276.25, 277.5 , 278.75,\n",
       "       280.  , 281.25, 282.5 , 283.75, 285.  , 286.25, 287.5 , 288.75,\n",
       "       290.  , 291.25, 292.5 , 293.75, 295.  , 296.25, 297.5 , 298.75,\n",
       "       300.  ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T19:44:26.944104Z",
     "start_time": "2020-09-08T19:44:26.940106Z"
    }
   },
   "outputs": [],
   "source": [
    "# Let's look at the four points used in Ferguson and Wood (2011)\n",
    "latPoint = np.asarray([36.6, 27.7, 35.0, 39.1])\n",
    "lonPoint = np.asarray([-116.0, -82.4, -106.6, -95.6])\n",
    "\n",
    "lonPoint = (lonPoint % 360.0)     # Convert to be 0-360 based \n",
    "\n",
    "# Find gridcells containing points\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-08T19:44:30.599674Z",
     "start_time": "2020-09-08T19:44:30.596678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([244. , 277.6, 253.4, 264.4])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lonPoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I think we *have* to try the classification scheme of Ferguson and Wood (2011)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-04T17:42:59.486420Z",
     "start_time": "2020-09-04T17:42:59.482901Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude range:  20.261780104712045 59.84293193717278\n"
     ]
    }
   ],
   "source": [
    "print('Latitude range: ', min(ds_utc12.lat.values), max(ds_utc12.lat.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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