{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WRFChem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WRFChem is numerical weather prediction model coupled with chemistry.\n",
    "\n",
    "For more information on running WRFChem, see [WRFotron](https://wrfchem-leeds.github.io/WRFotron)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import salem\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import cartopy.crs as ccrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:       (bottom_top: 27, south_north: 150, time: 3, west_east: 150)\n",
       "Coordinates:\n",
       "    lon           (south_north, west_east) float32 70.723145 ... 117.81842\n",
       "    lat           (south_north, west_east) float32 7.78907 ... 46.457573\n",
       "  * time          (time) datetime64[ns] 2008-10-26T12:00:00 ... 2008-10-26T18...\n",
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       "  * south_north   (south_north) float64 -2.235e+06 -2.205e+06 ... 2.235e+06\n",
       "Dimensions without coordinates: bottom_top\n",
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       "    RAINC         (time, south_north, west_east) float32 0.0 0.0 0.0 ... 0.0 0.0\n",
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       "    V             (time, bottom_top, south_north, west_east) float32 ...\n",
       "    PH            (time, bottom_top, south_north, west_east) float32 ...\n",
       "    PHB           (time, bottom_top, south_north, west_east) float32 ...\n",
       "    T2C           (time, south_north, west_east) float32 28.826569 ... -5.259369\n",
       "    PRCP          (time, south_north, west_east) float32 nan nan nan ... 0.0 0.0\n",
       "    WS            (time, bottom_top, south_north, west_east) float32 ...\n",
       "    GEOPOTENTIAL  (time, bottom_top, south_north, west_east) float32 ...\n",
       "    Z             (time, bottom_top, south_north, west_east) float32 ...\n",
       "    PRCP_NC       (time, south_north, west_east) float32 nan nan nan ... 0.0 0.0\n",
       "    PRCP_C        (time, south_north, west_east) float32 nan nan nan ... 0.0 0.0\n",
       "Attributes:\n",
       "    TITLE:                            OUTPUT FROM WRF V3.1.1 MODEL\n",
       "    START_DATE:                      2008-10-26_12:00:00\n",
       "    SIMULATION_START_DATE:           2008-10-26_12:00:00\n",
       "    WEST-EAST_GRID_DIMENSION:        151\n",
       "    SOUTH-NORTH_GRID_DIMENSION:      151\n",
       "    BOTTOM-TOP_GRID_DIMENSION:       28\n",
       "    DX:                              30000.0\n",
       "    DY:                              30000.0\n",
       "    GRIDTYPE:                        C\n",
       "    DIFF_OPT:                        1\n",
       "    KM_OPT:                          4\n",
       "    DAMP_OPT:                        0\n",
       "    DAMPCOEF:                        0.01\n",
       "    KHDIF:                           0.0\n",
       "    KVDIF:                           0.0\n",
       "    MP_PHYSICS:                      8\n",
       "    RA_LW_PHYSICS:                   1\n",
       "    RA_SW_PHYSICS:                   1\n",
       "    SF_SFCLAY_PHYSICS:               2\n",
       "    SF_SURFACE_PHYSICS:              2\n",
       "    BL_PBL_PHYSICS:                  2\n",
       "    CU_PHYSICS:                      1\n",
       "    SURFACE_INPUT_SOURCE:            1\n",
       "    SST_UPDATE:                      0\n",
       "    GRID_FDDA:                       0\n",
       "    GFDDA_INTERVAL_M:                0\n",
       "    GFDDA_END_H:                     0\n",
       "    GRID_SFDDA:                      0\n",
       "    SGFDDA_INTERVAL_M:               0\n",
       "    SGFDDA_END_H:                    0\n",
       "    SF_URBAN_PHYSICS:                0\n",
       "    FEEDBACK:                        1\n",
       "    SMOOTH_OPTION:                   0\n",
       "    SWRAD_SCAT:                      1.0\n",
       "    W_DAMPING:                       1\n",
       "    MOIST_ADV_OPT:                   1\n",
       "    SCALAR_ADV_OPT:                  1\n",
       "    TKE_ADV_OPT:                     1\n",
       "    DIFF_6TH_OPT:                    0\n",
       "    DIFF_6TH_FACTOR:                 0.12\n",
       "    OBS_NUDGE_OPT:                   0\n",
       "    WEST-EAST_PATCH_START_UNSTAG:    1\n",
       "    WEST-EAST_PATCH_END_UNSTAG:      150\n",
       "    WEST-EAST_PATCH_START_STAG:      1\n",
       "    WEST-EAST_PATCH_END_STAG:        151\n",
       "    SOUTH-NORTH_PATCH_START_UNSTAG:  1\n",
       "    SOUTH-NORTH_PATCH_END_UNSTAG:    150\n",
       "    SOUTH-NORTH_PATCH_START_STAG:    1\n",
       "    SOUTH-NORTH_PATCH_END_STAG:      151\n",
       "    BOTTOM-TOP_PATCH_START_UNSTAG:   1\n",
       "    BOTTOM-TOP_PATCH_END_UNSTAG:     27\n",
       "    BOTTOM-TOP_PATCH_START_STAG:     1\n",
       "    BOTTOM-TOP_PATCH_END_STAG:       28\n",
       "    GRID_ID:                         1\n",
       "    PARENT_ID:                       1\n",
       "    I_PARENT_START:                  1\n",
       "    J_PARENT_START:                  1\n",
       "    PARENT_GRID_RATIO:               1\n",
       "    DT:                              120.0\n",
       "    CEN_LAT:                         29.039997\n",
       "    CEN_LON:                         89.79999\n",
       "    TRUELAT1:                        29.04\n",
       "    TRUELAT2:                        29.04\n",
       "    MOAD_CEN_LAT:                    29.039997\n",
       "    STAND_LON:                       89.8\n",
       "    POLE_LAT:                        90.0\n",
       "    POLE_LON:                        0.0\n",
       "    GMT:                             12.0\n",
       "    JULYR:                           2008\n",
       "    JULDAY:                          300\n",
       "    MAP_PROJ:                        1\n",
       "    MMINLU:                          USGS\n",
       "    NUM_LAND_CAT:                    24\n",
       "    ISWATER:                         16\n",
       "    ISLAKE:                          -1\n",
       "    ISICE:                           24\n",
       "    ISURBAN:                         1\n",
       "    ISOILWATER:                      14\n",
       "    pyproj_srs:                      +proj=lcc +lat_0=29.0399971008301 +lon_0...</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-5a881ada-a30a-4682-8576-a4ebb3c17121' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5a881ada-a30a-4682-8576-a4ebb3c17121' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bottom_top</span>: 27</li><li><span class='xr-has-index'>south_north</span>: 150</li><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>west_east</span>: 150</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7c2e480d-1ae8-41e4-a937-aac7db8aa3c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c2e480d-1ae8-41e4-a937-aac7db8aa3c2' class='xr-section-summary' >Coordinates: <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>lon</span></div><div class='xr-var-dims'>(south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>70.723145 70.97485 ... 117.81842</div><input id='attrs-5df500c6-81a7-4503-b59b-24fd147846f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5df500c6-81a7-4503-b59b-24fd147846f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60d186cb-d00e-497c-aacd-4e170ad2ee40' class='xr-var-data-in' type='checkbox'><label for='data-60d186cb-d00e-497c-aacd-4e170ad2ee40' title='Show/Hide data 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([[ 70.723145,  70.97485 ,  71.226746, ..., 108.37326 , 108.62515 ,\n",
       "        108.87689 ],\n",
       "       [ 70.68201 ,  70.934265,  71.186676, ..., 108.41333 , 108.66574 ,\n",
       "        108.91797 ],\n",
       "       [ 70.64075 ,  70.89349 ,  71.14642 , ..., 108.45355 , 108.70651 ,\n",
       "        108.95929 ],\n",
       "       ...,\n",
       "       [ 61.95575 ,  62.31604 ,  62.67685 , ..., 116.92316 , 117.283966,\n",
       "        117.64429 ],\n",
       "       [ 61.868927,  62.230286,  62.592133, ..., 117.00787 , 117.36975 ,\n",
       "        117.73108 ],\n",
       "       [ 61.781586,  62.143982,  62.506897, ..., 117.09311 , 117.456024,\n",
       "        117.81842 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.78907 7.8294373 ... 46.457573</div><input id='attrs-72ccb7ad-95ac-4bdd-99de-d0d3cf780cdf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72ccb7ad-95ac-4bdd-99de-d0d3cf780cdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-badf79b2-7397-4420-ab51-89aa900a6798' class='xr-var-data-in' type='checkbox'><label for='data-badf79b2-7397-4420-ab51-89aa900a6798' title='Show/Hide data 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([[ 7.78907  ,  7.8294373,  7.8693237, ...,  7.8693237,  7.8294373,\n",
       "         7.78907  ],\n",
       "       [ 8.038551 ,  8.079079 ,  8.11908  , ...,  8.11908  ,  8.079079 ,\n",
       "         8.038551 ],\n",
       "       [ 8.288406 ,  8.329086 ,  8.369247 , ...,  8.369247 ,  8.329086 ,\n",
       "         8.288406 ],\n",
       "       ...,\n",
       "       [45.95782  , 46.017597 , 46.076576 , ..., 46.076576 , 46.017597 ,\n",
       "        45.95782  ],\n",
       "       [46.2079   , 46.267773 , 46.32685  , ..., 46.32685  , 46.267773 ,\n",
       "        46.2079   ],\n",
       "       [46.457573 , 46.51753  , 46.576714 , ..., 46.576714 , 46.51753  ,\n",
       "        46.457573 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2008-10-26T12:00:00 ... 2008-10-...</div><input id='attrs-32cd0460-c690-453d-8146-61031936760d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32cd0460-c690-453d-8146-61031936760d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-972195f6-99e1-442a-ab36-ffa256717aab' class='xr-var-data-in' type='checkbox'><label for='data-972195f6-99e1-442a-ab36-ffa256717aab' title='Show/Hide data 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([&#x27;2008-10-26T12:00:00.000000000&#x27;, &#x27;2008-10-26T15:00:00.000000000&#x27;,\n",
       "       &#x27;2008-10-26T18:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>west_east</span></div><div class='xr-var-dims'>(west_east)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.235e+06 -2.205e+06 ... 2.235e+06</div><input id='attrs-3b641972-4dfa-487e-a9ec-2616c639466f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b641972-4dfa-487e-a9ec-2616c639466f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-265758c6-33ab-4ba8-9dbe-11dd8ba421eb' class='xr-var-data-in' type='checkbox'><label for='data-265758c6-33ab-4ba8-9dbe-11dd8ba421eb' title='Show/Hide data 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([-2235001.48316, -2205001.48316, -2175001.48316, -2145001.48316,\n",
       "       -2115001.48316, -2085001.48316, -2055001.48316, -2025001.48316,\n",
       "       -1995001.48316, -1965001.48316, -1935001.48316, -1905001.48316,\n",
       "       -1875001.48316, -1845001.48316, -1815001.48316, -1785001.48316,\n",
       "       -1755001.48316, -1725001.48316, -1695001.48316, -1665001.48316,\n",
       "       -1635001.48316, -1605001.48316, -1575001.48316, -1545001.48316,\n",
       "       -1515001.48316, -1485001.48316, -1455001.48316, -1425001.48316,\n",
       "       -1395001.48316, -1365001.48316, -1335001.48316, -1305001.48316,\n",
       "       -1275001.48316, -1245001.48316, -1215001.48316, -1185001.48316,\n",
       "       -1155001.48316, -1125001.48316, -1095001.48316, -1065001.48316,\n",
       "       -1035001.48316, -1005001.48316,  -975001.48316,  -945001.48316,\n",
       "        -915001.48316,  -885001.48316,  -855001.48316,  -825001.48316,\n",
       "        -795001.48316,  -765001.48316,  -735001.48316,  -705001.48316,\n",
       "        -675001.48316,  -645001.48316,  -615001.48316,  -585001.48316,\n",
       "        -555001.48316,  -525001.48316,  -495001.48316,  -465001.48316,\n",
       "        -435001.48316,  -405001.48316,  -375001.48316,  -345001.48316,\n",
       "        -315001.48316,  -285001.48316,  -255001.48316,  -225001.48316,\n",
       "        -195001.48316,  -165001.48316,  -135001.48316,  -105001.48316,\n",
       "         -75001.48316,   -45001.48316,   -15001.48316,    14998.51684,\n",
       "          44998.51684,    74998.51684,   104998.51684,   134998.51684,\n",
       "         164998.51684,   194998.51684,   224998.51684,   254998.51684,\n",
       "         284998.51684,   314998.51684,   344998.51684,   374998.51684,\n",
       "         404998.51684,   434998.51684,   464998.51684,   494998.51684,\n",
       "         524998.51684,   554998.51684,   584998.51684,   614998.51684,\n",
       "         644998.51684,   674998.51684,   704998.51684,   734998.51684,\n",
       "         764998.51684,   794998.51684,   824998.51684,   854998.51684,\n",
       "         884998.51684,   914998.51684,   944998.51684,   974998.51684,\n",
       "        1004998.51684,  1034998.51684,  1064998.51684,  1094998.51684,\n",
       "        1124998.51684,  1154998.51684,  1184998.51684,  1214998.51684,\n",
       "        1244998.51684,  1274998.51684,  1304998.51684,  1334998.51684,\n",
       "        1364998.51684,  1394998.51684,  1424998.51684,  1454998.51684,\n",
       "        1484998.51684,  1514998.51684,  1544998.51684,  1574998.51684,\n",
       "        1604998.51684,  1634998.51684,  1664998.51684,  1694998.51684,\n",
       "        1724998.51684,  1754998.51684,  1784998.51684,  1814998.51684,\n",
       "        1844998.51684,  1874998.51684,  1904998.51684,  1934998.51684,\n",
       "        1964998.51684,  1994998.51684,  2024998.51684,  2054998.51684,\n",
       "        2084998.51684,  2114998.51684,  2144998.51684,  2174998.51684,\n",
       "        2204998.51684,  2234998.51684])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>south_north</span></div><div class='xr-var-dims'>(south_north)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.235e+06 -2.205e+06 ... 2.235e+06</div><input id='attrs-55ff42be-1e47-495c-802d-ce1a25a7a2c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55ff42be-1e47-495c-802d-ce1a25a7a2c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35731121-6f3d-4db5-990b-c9eb4baf629e' class='xr-var-data-in' type='checkbox'><label for='data-35731121-6f3d-4db5-990b-c9eb4baf629e' title='Show/Hide data 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([-2235000., -2205000., -2175000., -2145000., -2115000., -2085000.,\n",
       "       -2055000., -2025000., -1995000., -1965000., -1935000., -1905000.,\n",
       "       -1875000., -1845000., -1815000., -1785000., -1755000., -1725000.,\n",
       "       -1695000., -1665000., -1635000., -1605000., -1575000., -1545000.,\n",
       "       -1515000., -1485000., -1455000., -1425000., -1395000., -1365000.,\n",
       "       -1335000., -1305000., -1275000., -1245000., -1215000., -1185000.,\n",
       "       -1155000., -1125000., -1095000., -1065000., -1035000., -1005000.,\n",
       "        -975000.,  -945000.,  -915000.,  -885000.,  -855000.,  -825000.,\n",
       "        -795000.,  -765000.,  -735000.,  -705000.,  -675000.,  -645000.,\n",
       "        -615000.,  -585000.,  -555000.,  -525000.,  -495000.,  -465000.,\n",
       "        -435000.,  -405000.,  -375000.,  -345000.,  -315000.,  -285000.,\n",
       "        -255000.,  -225000.,  -195000.,  -165000.,  -135000.,  -105000.,\n",
       "         -75000.,   -45000.,   -15000.,    15000.,    45000.,    75000.,\n",
       "         105000.,   135000.,   165000.,   195000.,   225000.,   255000.,\n",
       "         285000.,   315000.,   345000.,   375000.,   405000.,   435000.,\n",
       "         465000.,   495000.,   525000.,   555000.,   585000.,   615000.,\n",
       "         645000.,   675000.,   705000.,   735000.,   765000.,   795000.,\n",
       "         825000.,   855000.,   885000.,   915000.,   945000.,   975000.,\n",
       "        1005000.,  1035000.,  1065000.,  1095000.,  1125000.,  1155000.,\n",
       "        1185000.,  1215000.,  1245000.,  1275000.,  1305000.,  1335000.,\n",
       "        1365000.,  1395000.,  1425000.,  1455000.,  1485000.,  1515000.,\n",
       "        1545000.,  1575000.,  1605000.,  1635000.,  1665000.,  1695000.,\n",
       "        1725000.,  1755000.,  1785000.,  1815000.,  1845000.,  1875000.,\n",
       "        1905000.,  1935000.,  1965000.,  1995000.,  2025000.,  2055000.,\n",
       "        2085000.,  2115000.,  2145000.,  2175000.,  2205000.,  2235000.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd20bb52-d4b9-4788-820b-dbbe97a12781' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bd20bb52-d4b9-4788-820b-dbbe97a12781' class='xr-section-summary' >Data variables: <span>(14)</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>T2</span></div><div class='xr-var-dims'>(time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b5dae39-c583-4282-9ce9-6a1de5f6248c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b5dae39-c583-4282-9ce9-6a1de5f6248c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32adbb15-96f7-4fdc-81f5-18596c6c8d50' class='xr-var-data-in' type='checkbox'><label for='data-32adbb15-96f7-4fdc-81f5-18596c6c8d50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>TEMP at 2 M</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>array([[[301.97656, 302.0078 , ..., 301.14062, 301.21875],\n",
       "        [301.96094, 301.98438, ..., 301.3203 , 301.39062],\n",
       "        ...,\n",
       "        [281.4375 , 280.73438, ..., 269.07812, 269.625  ],\n",
       "        [281.51562, 280.8672 , ..., 269.3828 , 269.8203 ]],\n",
       "\n",
       "       [[301.96875, 301.98438, ..., 301.46875, 301.41406],\n",
       "        [301.96875, 302.0625 , ..., 301.5    , 301.4375 ],\n",
       "        ...,\n",
       "        [279.48438, 279.25   , ..., 268.26562, 268.5547 ],\n",
       "        [279.2422 , 278.9453 , ..., 268.65625, 268.71875]],\n",
       "\n",
       "       [[301.8672 , 301.875  , ..., 301.40625, 301.28125],\n",
       "        [301.875  , 301.95312, ..., 301.375  , 301.2578 ],\n",
       "        ...,\n",
       "        [278.1875 , 277.625  , ..., 267.6172 , 267.6875 ],\n",
       "        [278.14844, 277.60938, ..., 268.07812, 267.89062]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAINC</span></div><div class='xr-var-dims'>(time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c7a51685-d225-410a-84ed-f30c83b4d06a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7a51685-d225-410a-84ed-f30c83b4d06a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cceb8be5-57d1-4e80-8bca-c70f40f9f348' class='xr-var-data-in' type='checkbox'><label for='data-cceb8be5-57d1-4e80-8bca-c70f40f9f348' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL CUMULUS PRECIPITATION</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., ..., 0., 0.],\n",
       "        [0., 0., ..., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., ..., 0., 0.],\n",
       "        [0., 0., ..., 0., 0.]],\n",
       "\n",
       "       [[0., 0., ..., 0., 0.],\n",
       "        [0., 0., ..., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., ..., 0., 0.],\n",
       "        [0., 0., ..., 0., 0.]],\n",
       "\n",
       "       [[0., 0., ..., 0., 0.],\n",
       "        [0., 0., ..., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., ..., 0., 0.],\n",
       "        [0., 0., ..., 0., 0.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAINNC</span></div><div class='xr-var-dims'>(time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-581a18c8-717c-4fa6-98bc-1a22134fae8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-581a18c8-717c-4fa6-98bc-1a22134fae8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f30f05d-1f88-4132-af60-cdc3d04f0d81' class='xr-var-data-in' type='checkbox'><label for='data-4f30f05d-1f88-4132-af60-cdc3d04f0d81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE PRECIPITATION</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>array([[[0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        ...,\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ]],\n",
       "\n",
       "       [[0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        ...,\n",
       "        [0.      , 0.328125, ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ]],\n",
       "\n",
       "       [[0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        ...,\n",
       "        [0.      , 0.695312, ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d98a78d-9fa7-4c8c-8bf6-abe1a1d325e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d98a78d-9fa7-4c8c-8bf6-abe1a1d325e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-140b568e-bba2-43a3-881b-28fbc51e4b69' class='xr-var-data-in' type='checkbox'><label for='data-140b568e-bba2-43a3-881b-28fbc51e4b69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>x-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd>X</dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>[1822500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V</span></div><div class='xr-var-dims'>(time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2096228c-573f-4849-98dd-411007b867bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2096228c-573f-4849-98dd-411007b867bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f72316b-88dc-4175-bed8-cac7a11f118c' class='xr-var-data-in' type='checkbox'><label for='data-7f72316b-88dc-4175-bed8-cac7a11f118c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>y-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd>Y</dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>[1822500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PH</span></div><div class='xr-var-dims'>(time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7b72254f-1b20-456b-a312-4612e961c72b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b72254f-1b20-456b-a312-4612e961c72b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6ec9e79-938c-46a6-8d6c-3c8dfdbd39cc' class='xr-var-data-in' type='checkbox'><label for='data-c6ec9e79-938c-46a6-8d6c-3c8dfdbd39cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>perturbation geopotential</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd>Z</dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>[1822500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHB</span></div><div class='xr-var-dims'>(time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-daac13e9-46a8-4ea5-a7bd-985cf5bff1a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-daac13e9-46a8-4ea5-a7bd-985cf5bff1a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d008c4c0-c76b-459b-813f-586107725b2f' class='xr-var-data-in' type='checkbox'><label for='data-d008c4c0-c76b-459b-813f-586107725b2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>base-state geopotential</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd>Z</dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>[1822500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T2C</span></div><div class='xr-var-dims'>(time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ecf4344-ef28-4ea3-8aea-1865848d6831' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ecf4344-ef28-4ea3-8aea-1865848d6831' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2320698a-5fed-4507-9fc9-7eaa3601e98f' class='xr-var-data-in' type='checkbox'><label for='data-2320698a-5fed-4507-9fc9-7eaa3601e98f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>2m Temperature</dd><dt><span>units :</span></dt><dd>C</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>array([[[28.826569, 28.857819, ..., 27.990631, 28.068756],\n",
       "        [28.810944, 28.834381, ..., 28.170319, 28.240631],\n",
       "        ...,\n",
       "        [ 8.287506,  7.584381, ..., -4.071869, -3.524994],\n",
       "        [ 8.365631,  7.717194, ..., -3.767181, -3.329681]],\n",
       "\n",
       "       [[28.818756, 28.834381, ..., 28.318756, 28.264069],\n",
       "        [28.818756, 28.912506, ..., 28.350006, 28.287506],\n",
       "        ...,\n",
       "        [ 6.334381,  6.100006, ..., -4.884369, -4.595306],\n",
       "        [ 6.092194,  5.795319, ..., -4.493744, -4.431244]],\n",
       "\n",
       "       [[28.717194, 28.725006, ..., 28.256256, 28.131256],\n",
       "        [28.725006, 28.803131, ..., 28.225006, 28.107819],\n",
       "        ...,\n",
       "        [ 5.037506,  4.475006, ..., -5.532806, -5.462494],\n",
       "        [ 4.998444,  4.459381, ..., -5.071869, -5.259369]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRCP</span></div><div class='xr-var-dims'>(time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dfe3f1ab-65c0-4f15-b27a-ea639db80212' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfe3f1ab-65c0-4f15-b27a-ea639db80212' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b08a91ba-ae13-4adb-a286-6603e829d80b' class='xr-var-data-in' type='checkbox'><label for='data-b08a91ba-ae13-4adb-a286-6603e829d80b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Total precipitation rate</dd><dt><span>units :</span></dt><dd>mm h-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>array([[[     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ...,      nan,      nan],\n",
       "        ...,\n",
       "        [     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ...,      nan,      nan]],\n",
       "\n",
       "       [[0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        ...,\n",
       "        [0.      , 0.109375, ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ]],\n",
       "\n",
       "       [[0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        ...,\n",
       "        [0.      , 0.122396, ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WS</span></div><div class='xr-var-dims'>(time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b71d36b2-38ac-420b-9d44-0dc8918e2f07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b71d36b2-38ac-420b-9d44-0dc8918e2f07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d27e2af5-a32e-4b0a-9aa7-a47fe69038c1' class='xr-var-data-in' type='checkbox'><label for='data-d27e2af5-a32e-4b0a-9aa7-a47fe69038c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Horizontal wind speed</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd>X</dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>[1822500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GEOPOTENTIAL</span></div><div class='xr-var-dims'>(time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b155a1d1-6083-476b-b052-e85d5e85ec24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b155a1d1-6083-476b-b052-e85d5e85ec24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7a16af9-0ba7-44ab-8c10-d67f6e57fed3' class='xr-var-data-in' type='checkbox'><label for='data-d7a16af9-0ba7-44ab-8c10-d67f6e57fed3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Full model geopotential</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>stagger :</span></dt><dd>Z</dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>[1822500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z</span></div><div class='xr-var-dims'>(time, bottom_top, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eefa9880-2363-452c-a17c-20125bc04f36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eefa9880-2363-452c-a17c-20125bc04f36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e79af3d-b792-461a-85e1-2417c7f862bf' class='xr-var-data-in' type='checkbox'><label for='data-8e79af3d-b792-461a-85e1-2417c7f862bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XYZ</dd><dt><span>description :</span></dt><dd>Full model height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd>Z</dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>[1822500 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRCP_NC</span></div><div class='xr-var-dims'>(time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-29b60a86-20ad-43be-8833-cd9778b91a0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29b60a86-20ad-43be-8833-cd9778b91a0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03f7603e-d2d6-45e5-9383-c896a83f93ab' class='xr-var-data-in' type='checkbox'><label for='data-03f7603e-d2d6-45e5-9383-c896a83f93ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Precipitation rate from grid scale physics</dd><dt><span>units :</span></dt><dd>mm h-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>array([[[     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ...,      nan,      nan],\n",
       "        ...,\n",
       "        [     nan,      nan, ...,      nan,      nan],\n",
       "        [     nan,      nan, ...,      nan,      nan]],\n",
       "\n",
       "       [[0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        ...,\n",
       "        [0.      , 0.109375, ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ]],\n",
       "\n",
       "       [[0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ],\n",
       "        ...,\n",
       "        [0.      , 0.122396, ..., 0.      , 0.      ],\n",
       "        [0.      , 0.      , ..., 0.      , 0.      ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRCP_C</span></div><div class='xr-var-dims'>(time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ef1ae8b7-3649-4d0b-8b41-508e6850ae6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef1ae8b7-3649-4d0b-8b41-508e6850ae6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43b735cc-93f3-4fae-b572-00ae61b1c331' class='xr-var-data-in' type='checkbox'><label for='data-43b735cc-93f3-4fae-b572-00ae61b1c331' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>description :</span></dt><dd>Precipitation rate from cumulus physics</dd><dt><span>units :</span></dt><dd>mm h-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>pyproj_srs :</span></dt><dd>+proj=lcc +lat_0=29.0399971008301 +lon_0=89.8000030517578 +lat_1=29.0400009155273 +lat_2=29.0400009155273 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, ..., nan, nan],\n",
       "        [nan, nan, ..., nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, ..., nan, nan],\n",
       "        [nan, nan, ..., nan, nan]],\n",
       "\n",
       "       [[ 0.,  0., ...,  0.,  0.],\n",
       "        [ 0.,  0., ...,  0.,  0.],\n",
       "        ...,\n",
       "        [ 0.,  0., ...,  0.,  0.],\n",
       "        [ 0.,  0., ...,  0.,  0.]],\n",
       "\n",
       "       [[ 0.,  0., ...,  0.,  0.],\n",
       "        [ 0.,  0., ...,  0.,  0.],\n",
       "        ...,\n",
       "        [ 0.,  0., ...,  0.,  0.],\n",
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      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (bottom_top: 27, south_north: 150, time: 3, west_east: 150)\n",
       "Coordinates:\n",
       "    lon           (south_north, west_east) float32 70.723145 ... 117.81842\n",
       "    lat           (south_north, west_east) float32 7.78907 ... 46.457573\n",
       "  * time          (time) datetime64[ns] 2008-10-26T12:00:00 ... 2008-10-26T18...\n",
       "  * west_east     (west_east) float64 -2.235e+06 -2.205e+06 ... 2.235e+06\n",
       "  * south_north   (south_north) float64 -2.235e+06 -2.205e+06 ... 2.235e+06\n",
       "Dimensions without coordinates: bottom_top\n",
       "Data variables:\n",
       "    T2            (time, south_north, west_east) float32 ...\n",
       "    RAINC         (time, south_north, west_east) float32 ...\n",
       "    RAINNC        (time, south_north, west_east) float32 ...\n",
       "    U             (time, bottom_top, south_north, west_east) float32 ...\n",
       "    V             (time, bottom_top, south_north, west_east) float32 ...\n",
       "    PH            (time, bottom_top, south_north, west_east) float32 ...\n",
       "    PHB           (time, bottom_top, south_north, west_east) float32 ...\n",
       "    T2C           (time, south_north, west_east) float32 ...\n",
       "    PRCP          (time, south_north, west_east) float32 ...\n",
       "    WS            (time, bottom_top, south_north, west_east) float32 ...\n",
       "    GEOPOTENTIAL  (time, bottom_top, south_north, west_east) float32 ...\n",
       "    Z             (time, bottom_top, south_north, west_east) float32 ...\n",
       "    PRCP_NC       (time, south_north, west_east) float32 ...\n",
       "    PRCP_C        (time, south_north, west_east) float32 ...\n",
       "Attributes:\n",
       "    TITLE:                            OUTPUT FROM WRF V3.1.1 MODEL\n",
       "    START_DATE:                      2008-10-26_12:00:00\n",
       "    SIMULATION_START_DATE:           2008-10-26_12:00:00\n",
       "    WEST-EAST_GRID_DIMENSION:        151\n",
       "    SOUTH-NORTH_GRID_DIMENSION:      151\n",
       "    BOTTOM-TOP_GRID_DIMENSION:       28\n",
       "    DX:                              30000.0\n",
       "    DY:                              30000.0\n",
       "    GRIDTYPE:                        C\n",
       "    DIFF_OPT:                        1\n",
       "    KM_OPT:                          4\n",
       "    DAMP_OPT:                        0\n",
       "    DAMPCOEF:                        0.01\n",
       "    KHDIF:                           0.0\n",
       "    KVDIF:                           0.0\n",
       "    MP_PHYSICS:                      8\n",
       "    RA_LW_PHYSICS:                   1\n",
       "    RA_SW_PHYSICS:                   1\n",
       "    SF_SFCLAY_PHYSICS:               2\n",
       "    SF_SURFACE_PHYSICS:              2\n",
       "    BL_PBL_PHYSICS:                  2\n",
       "    CU_PHYSICS:                      1\n",
       "    SURFACE_INPUT_SOURCE:            1\n",
       "    SST_UPDATE:                      0\n",
       "    GRID_FDDA:                       0\n",
       "    GFDDA_INTERVAL_M:                0\n",
       "    GFDDA_END_H:                     0\n",
       "    GRID_SFDDA:                      0\n",
       "    SGFDDA_INTERVAL_M:               0\n",
       "    SGFDDA_END_H:                    0\n",
       "    SF_URBAN_PHYSICS:                0\n",
       "    FEEDBACK:                        1\n",
       "    SMOOTH_OPTION:                   0\n",
       "    SWRAD_SCAT:                      1.0\n",
       "    W_DAMPING:                       1\n",
       "    MOIST_ADV_OPT:                   1\n",
       "    SCALAR_ADV_OPT:                  1\n",
       "    TKE_ADV_OPT:                     1\n",
       "    DIFF_6TH_OPT:                    0\n",
       "    DIFF_6TH_FACTOR:                 0.12\n",
       "    OBS_NUDGE_OPT:                   0\n",
       "    WEST-EAST_PATCH_START_UNSTAG:    1\n",
       "    WEST-EAST_PATCH_END_UNSTAG:      150\n",
       "    WEST-EAST_PATCH_START_STAG:      1\n",
       "    WEST-EAST_PATCH_END_STAG:        151\n",
       "    SOUTH-NORTH_PATCH_START_UNSTAG:  1\n",
       "    SOUTH-NORTH_PATCH_END_UNSTAG:    150\n",
       "    SOUTH-NORTH_PATCH_START_STAG:    1\n",
       "    SOUTH-NORTH_PATCH_END_STAG:      151\n",
       "    BOTTOM-TOP_PATCH_START_UNSTAG:   1\n",
       "    BOTTOM-TOP_PATCH_END_UNSTAG:     27\n",
       "    BOTTOM-TOP_PATCH_START_STAG:     1\n",
       "    BOTTOM-TOP_PATCH_END_STAG:       28\n",
       "    GRID_ID:                         1\n",
       "    PARENT_ID:                       1\n",
       "    I_PARENT_START:                  1\n",
       "    J_PARENT_START:                  1\n",
       "    PARENT_GRID_RATIO:               1\n",
       "    DT:                              120.0\n",
       "    CEN_LAT:                         29.039997\n",
       "    CEN_LON:                         89.79999\n",
       "    TRUELAT1:                        29.04\n",
       "    TRUELAT2:                        29.04\n",
       "    MOAD_CEN_LAT:                    29.039997\n",
       "    STAND_LON:                       89.8\n",
       "    POLE_LAT:                        90.0\n",
       "    POLE_LON:                        0.0\n",
       "    GMT:                             12.0\n",
       "    JULYR:                           2008\n",
       "    JULDAY:                          300\n",
       "    MAP_PROJ:                        1\n",
       "    MMINLU:                          USGS\n",
       "    NUM_LAND_CAT:                    24\n",
       "    ISWATER:                         16\n",
       "    ISLAKE:                          -1\n",
       "    ISICE:                           24\n",
       "    ISURBAN:                         1\n",
       "    ISOILWATER:                      14\n",
       "    pyproj_srs:                      +proj=lcc +lat_0=29.0399971008301 +lon_0..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = salem.open_wrf_dataset(salem.utils.get_demo_file('wrfout_d01.nc'))\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;T2&#x27; (south_north: 150, west_east: 150)&gt;\n",
       "array([[301.9375 , 301.95572, 302.00522, ..., 301.41406, 301.33853,\n",
       "        301.3047 ],\n",
       "       [301.9349 , 302.     , 302.04688, ..., 301.47134, 301.39844,\n",
       "        301.36197],\n",
       "       [301.9349 , 302.0026 , 302.04166, ..., 301.50522, 301.45572,\n",
       "        301.4297 ],\n",
       "       ...,\n",
       "       [279.84634, 279.70053, 279.58594, ..., 267.8047 , 268.3151 ,\n",
       "        268.58072],\n",
       "       [279.70312, 279.20312, 279.33853, ..., 268.08072, 268.3203 ,\n",
       "        268.6224 ],\n",
       "       [279.6354 , 279.14062, 278.6901 , ..., 268.61978, 268.70572,\n",
       "        268.8099 ]], dtype=float32)\n",
       "Coordinates:\n",
       "    lon          (south_north, west_east) float32 70.723145 ... 117.81842\n",
       "    lat          (south_north, west_east) float32 7.78907 ... 46.457573\n",
       "  * west_east    (west_east) float64 -2.235e+06 -2.205e+06 ... 2.235e+06\n",
       "  * south_north  (south_north) float64 -2.235e+06 -2.205e+06 ... 2.235e+06</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'T2'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>south_north</span>: 150</li><li><span class='xr-has-index'>west_east</span>: 150</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8756d973-185a-4595-bcff-2c4718002fbd' class='xr-array-in' type='checkbox' checked><label for='section-8756d973-185a-4595-bcff-2c4718002fbd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>301.9375 301.95572 302.00522 ... 268.61978 268.70572 268.8099</span></div><div class='xr-array-data'><pre>array([[301.9375 , 301.95572, 302.00522, ..., 301.41406, 301.33853,\n",
       "        301.3047 ],\n",
       "       [301.9349 , 302.     , 302.04688, ..., 301.47134, 301.39844,\n",
       "        301.36197],\n",
       "       [301.9349 , 302.0026 , 302.04166, ..., 301.50522, 301.45572,\n",
       "        301.4297 ],\n",
       "       ...,\n",
       "       [279.84634, 279.70053, 279.58594, ..., 267.8047 , 268.3151 ,\n",
       "        268.58072],\n",
       "       [279.70312, 279.20312, 279.33853, ..., 268.08072, 268.3203 ,\n",
       "        268.6224 ],\n",
       "       [279.6354 , 279.14062, 278.6901 , ..., 268.61978, 268.70572,\n",
       "        268.8099 ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-91692e99-27fa-4127-82d8-cf3f00ef03a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91692e99-27fa-4127-82d8-cf3f00ef03a6' 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>lon</span></div><div class='xr-var-dims'>(south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>70.723145 70.97485 ... 117.81842</div><input id='attrs-07c37b6a-b6e8-4323-be08-2cc74a627ac7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07c37b6a-b6e8-4323-be08-2cc74a627ac7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac1e116c-7369-4980-9fb3-5496555e7fea' class='xr-var-data-in' type='checkbox'><label for='data-ac1e116c-7369-4980-9fb3-5496555e7fea' title='Show/Hide data 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([[ 70.723145,  70.97485 ,  71.226746, ..., 108.37326 , 108.62515 ,\n",
       "        108.87689 ],\n",
       "       [ 70.68201 ,  70.934265,  71.186676, ..., 108.41333 , 108.66574 ,\n",
       "        108.91797 ],\n",
       "       [ 70.64075 ,  70.89349 ,  71.14642 , ..., 108.45355 , 108.70651 ,\n",
       "        108.95929 ],\n",
       "       ...,\n",
       "       [ 61.95575 ,  62.31604 ,  62.67685 , ..., 116.92316 , 117.283966,\n",
       "        117.64429 ],\n",
       "       [ 61.868927,  62.230286,  62.592133, ..., 117.00787 , 117.36975 ,\n",
       "        117.73108 ],\n",
       "       [ 61.781586,  62.143982,  62.506897, ..., 117.09311 , 117.456024,\n",
       "        117.81842 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.78907 7.8294373 ... 46.457573</div><input id='attrs-5bf2c05f-d866-43fa-ae77-ccf66effd78c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bf2c05f-d866-43fa-ae77-ccf66effd78c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65e5ebf6-bb0e-4f20-a4d4-6179b4767d26' class='xr-var-data-in' type='checkbox'><label for='data-65e5ebf6-bb0e-4f20-a4d4-6179b4767d26' title='Show/Hide data 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([[ 7.78907  ,  7.8294373,  7.8693237, ...,  7.8693237,  7.8294373,\n",
       "         7.78907  ],\n",
       "       [ 8.038551 ,  8.079079 ,  8.11908  , ...,  8.11908  ,  8.079079 ,\n",
       "         8.038551 ],\n",
       "       [ 8.288406 ,  8.329086 ,  8.369247 , ...,  8.369247 ,  8.329086 ,\n",
       "         8.288406 ],\n",
       "       ...,\n",
       "       [45.95782  , 46.017597 , 46.076576 , ..., 46.076576 , 46.017597 ,\n",
       "        45.95782  ],\n",
       "       [46.2079   , 46.267773 , 46.32685  , ..., 46.32685  , 46.267773 ,\n",
       "        46.2079   ],\n",
       "       [46.457573 , 46.51753  , 46.576714 , ..., 46.576714 , 46.51753  ,\n",
       "        46.457573 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>west_east</span></div><div class='xr-var-dims'>(west_east)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.235e+06 -2.205e+06 ... 2.235e+06</div><input id='attrs-eae288ea-f598-449a-867b-18928ef2d4b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eae288ea-f598-449a-867b-18928ef2d4b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56cfda96-573e-49c0-a911-8cfd9e732c58' class='xr-var-data-in' type='checkbox'><label for='data-56cfda96-573e-49c0-a911-8cfd9e732c58' title='Show/Hide data 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([-2235001.48316, -2205001.48316, -2175001.48316, -2145001.48316,\n",
       "       -2115001.48316, -2085001.48316, -2055001.48316, -2025001.48316,\n",
       "       -1995001.48316, -1965001.48316, -1935001.48316, -1905001.48316,\n",
       "       -1875001.48316, -1845001.48316, -1815001.48316, -1785001.48316,\n",
       "       -1755001.48316, -1725001.48316, -1695001.48316, -1665001.48316,\n",
       "       -1635001.48316, -1605001.48316, -1575001.48316, -1545001.48316,\n",
       "       -1515001.48316, -1485001.48316, -1455001.48316, -1425001.48316,\n",
       "       -1395001.48316, -1365001.48316, -1335001.48316, -1305001.48316,\n",
       "       -1275001.48316, -1245001.48316, -1215001.48316, -1185001.48316,\n",
       "       -1155001.48316, -1125001.48316, -1095001.48316, -1065001.48316,\n",
       "       -1035001.48316, -1005001.48316,  -975001.48316,  -945001.48316,\n",
       "        -915001.48316,  -885001.48316,  -855001.48316,  -825001.48316,\n",
       "        -795001.48316,  -765001.48316,  -735001.48316,  -705001.48316,\n",
       "        -675001.48316,  -645001.48316,  -615001.48316,  -585001.48316,\n",
       "        -555001.48316,  -525001.48316,  -495001.48316,  -465001.48316,\n",
       "        -435001.48316,  -405001.48316,  -375001.48316,  -345001.48316,\n",
       "        -315001.48316,  -285001.48316,  -255001.48316,  -225001.48316,\n",
       "        -195001.48316,  -165001.48316,  -135001.48316,  -105001.48316,\n",
       "         -75001.48316,   -45001.48316,   -15001.48316,    14998.51684,\n",
       "          44998.51684,    74998.51684,   104998.51684,   134998.51684,\n",
       "         164998.51684,   194998.51684,   224998.51684,   254998.51684,\n",
       "         284998.51684,   314998.51684,   344998.51684,   374998.51684,\n",
       "         404998.51684,   434998.51684,   464998.51684,   494998.51684,\n",
       "         524998.51684,   554998.51684,   584998.51684,   614998.51684,\n",
       "         644998.51684,   674998.51684,   704998.51684,   734998.51684,\n",
       "         764998.51684,   794998.51684,   824998.51684,   854998.51684,\n",
       "         884998.51684,   914998.51684,   944998.51684,   974998.51684,\n",
       "        1004998.51684,  1034998.51684,  1064998.51684,  1094998.51684,\n",
       "        1124998.51684,  1154998.51684,  1184998.51684,  1214998.51684,\n",
       "        1244998.51684,  1274998.51684,  1304998.51684,  1334998.51684,\n",
       "        1364998.51684,  1394998.51684,  1424998.51684,  1454998.51684,\n",
       "        1484998.51684,  1514998.51684,  1544998.51684,  1574998.51684,\n",
       "        1604998.51684,  1634998.51684,  1664998.51684,  1694998.51684,\n",
       "        1724998.51684,  1754998.51684,  1784998.51684,  1814998.51684,\n",
       "        1844998.51684,  1874998.51684,  1904998.51684,  1934998.51684,\n",
       "        1964998.51684,  1994998.51684,  2024998.51684,  2054998.51684,\n",
       "        2084998.51684,  2114998.51684,  2144998.51684,  2174998.51684,\n",
       "        2204998.51684,  2234998.51684])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>south_north</span></div><div class='xr-var-dims'>(south_north)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.235e+06 -2.205e+06 ... 2.235e+06</div><input id='attrs-e2b77d20-87db-4a14-94e5-bea7a635f75f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2b77d20-87db-4a14-94e5-bea7a635f75f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d59ca48-377b-4c5e-a3b0-2b48fbac208a' class='xr-var-data-in' type='checkbox'><label for='data-3d59ca48-377b-4c5e-a3b0-2b48fbac208a' title='Show/Hide data 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([-2235000., -2205000., -2175000., -2145000., -2115000., -2085000.,\n",
       "       -2055000., -2025000., -1995000., -1965000., -1935000., -1905000.,\n",
       "       -1875000., -1845000., -1815000., -1785000., -1755000., -1725000.,\n",
       "       -1695000., -1665000., -1635000., -1605000., -1575000., -1545000.,\n",
       "       -1515000., -1485000., -1455000., -1425000., -1395000., -1365000.,\n",
       "       -1335000., -1305000., -1275000., -1245000., -1215000., -1185000.,\n",
       "       -1155000., -1125000., -1095000., -1065000., -1035000., -1005000.,\n",
       "        -975000.,  -945000.,  -915000.,  -885000.,  -855000.,  -825000.,\n",
       "        -795000.,  -765000.,  -735000.,  -705000.,  -675000.,  -645000.,\n",
       "        -615000.,  -585000.,  -555000.,  -525000.,  -495000.,  -465000.,\n",
       "        -435000.,  -405000.,  -375000.,  -345000.,  -315000.,  -285000.,\n",
       "        -255000.,  -225000.,  -195000.,  -165000.,  -135000.,  -105000.,\n",
       "         -75000.,   -45000.,   -15000.,    15000.,    45000.,    75000.,\n",
       "         105000.,   135000.,   165000.,   195000.,   225000.,   255000.,\n",
       "         285000.,   315000.,   345000.,   375000.,   405000.,   435000.,\n",
       "         465000.,   495000.,   525000.,   555000.,   585000.,   615000.,\n",
       "         645000.,   675000.,   705000.,   735000.,   765000.,   795000.,\n",
       "         825000.,   855000.,   885000.,   915000.,   945000.,   975000.,\n",
       "        1005000.,  1035000.,  1065000.,  1095000.,  1125000.,  1155000.,\n",
       "        1185000.,  1215000.,  1245000.,  1275000.,  1305000.,  1335000.,\n",
       "        1365000.,  1395000.,  1425000.,  1455000.,  1485000.,  1515000.,\n",
       "        1545000.,  1575000.,  1605000.,  1635000.,  1665000.,  1695000.,\n",
       "        1725000.,  1755000.,  1785000.,  1815000.,  1845000.,  1875000.,\n",
       "        1905000.,  1935000.,  1965000.,  1995000.,  2025000.,  2055000.,\n",
       "        2085000.,  2115000.,  2145000.,  2175000.,  2205000.,  2235000.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e9bd1bf-1cdb-474c-868a-84a890dfa72a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8e9bd1bf-1cdb-474c-868a-84a890dfa72a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'T2' (south_north: 150, west_east: 150)>\n",
       "array([[301.9375 , 301.95572, 302.00522, ..., 301.41406, 301.33853,\n",
       "        301.3047 ],\n",
       "       [301.9349 , 302.     , 302.04688, ..., 301.47134, 301.39844,\n",
       "        301.36197],\n",
       "       [301.9349 , 302.0026 , 302.04166, ..., 301.50522, 301.45572,\n",
       "        301.4297 ],\n",
       "       ...,\n",
       "       [279.84634, 279.70053, 279.58594, ..., 267.8047 , 268.3151 ,\n",
       "        268.58072],\n",
       "       [279.70312, 279.20312, 279.33853, ..., 268.08072, 268.3203 ,\n",
       "        268.6224 ],\n",
       "       [279.6354 , 279.14062, 278.6901 , ..., 268.61978, 268.70572,\n",
       "        268.8099 ]], dtype=float32)\n",
       "Coordinates:\n",
       "    lon          (south_north, west_east) float32 70.723145 ... 117.81842\n",
       "    lat          (south_north, west_east) float32 7.78907 ... 46.457573\n",
       "  * west_east    (west_east) float64 -2.235e+06 -2.205e+06 ... 2.235e+06\n",
       "  * south_north  (south_north) float64 -2.235e+06 -2.205e+06 ... 2.235e+06"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_mean = ds['T2'].mean(dim='time')\n",
    "temp_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "lon = ds.lon.values\n",
    "lat = ds.lat.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize=(8, 8))\n",
    "gs = gridspec.GridSpec(1, 1)\n",
    "\n",
    "ax = fig.add_subplot(gs[0], projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "im = ax.contourf(lon, lat, temp_mean, transform=ccrs.PlateCarree())\n",
    "fig.colorbar(im, label='Mean temperature ($K$)', shrink=0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem: Crop arrays to shapefiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solution 1\n",
    "- Customisable cropping\n",
    "- Destaggered/rectilinear grid e.g. after using `pp_concat_regrid.py` on WRFChem output with Salem to destagger and XEMSF to regrid to rectilinear grid\n",
    "  - For conservative regridding, consider [xgcm](https://xgcm.readthedocs.io/en/latest/).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import geopandas as gpd\n",
    "from rasterio import features\n",
    "from affine import Affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_from_latlon(lat, lon):\n",
    "    \"\"\" input 1D array of lat / lon and output an Affine transformation \"\"\"\n",
    "    lat = np.asarray(lat)\n",
    "    lon = np.asarray(lon)\n",
    "    trans = Affine.translation(lon[0], lat[0])\n",
    "    scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\n",
    "    return trans * scale\n",
    "\n",
    "\n",
    "def rasterize(shapes, coords, latitude='latitude', longitude='longitude', fill=np.nan, **kwargs):\n",
    "    \"\"\"\n",
    "    Description:    \n",
    "        Rasterize a list of (geometry, fill_value) tuples onto the given\n",
    "        xarray coordinates. This only works for 1D latitude and longitude\n",
    "        arrays.\n",
    "    Usage:\n",
    "        1. read shapefile to geopandas.GeoDataFrame\n",
    "               `states = gpd.read_file(shp_dir+shp_file)`\n",
    "        2. encode the different shapefiles that capture those lat-lons as different\n",
    "           numbers i.e. 0.0, 1.0 ... and otherwise np.nan\n",
    "              `shapes = (zip(states.geometry, range(len(states))))`\n",
    "        3. Assign this to a new coord in your original xarray.DataArray\n",
    "              `ds['states'] = rasterize(shapes, ds.coords, longitude='X', latitude='Y')`\n",
    "    Arguments:\n",
    "        **kwargs (dict): passed to `rasterio.rasterize` function.\n",
    "    Attributes:\n",
    "        transform (affine.Affine): how to translate from latlon to ...?\n",
    "        raster (numpy.ndarray): use rasterio.features.rasterize fill the values\n",
    "                                outside the .shp file with np.nan\n",
    "        spatial_coords (dict): dictionary of {\"X\":xr.DataArray, \"Y\":xr.DataArray()}\n",
    "                               with \"X\", \"Y\" as keys, and xr.DataArray as values\n",
    "    Returns:\n",
    "        (xr.DataArray): DataArray with `values` of nan for points outside shapefile\n",
    "                        and coords `Y` = latitude, 'X' = longitude.\n",
    "    \"\"\"\n",
    "    transform = transform_from_latlon(coords[latitude], coords[longitude])\n",
    "    out_shape = (len(coords[latitude]), len(coords[longitude]))\n",
    "    raster = features.rasterize(\n",
    "        shapes, \n",
    "        out_shape=out_shape,\n",
    "        fill=fill,\n",
    "        transform=transform,\n",
    "        dtype=float, \n",
    "        **kwargs\n",
    "    )\n",
    "    spatial_coords = {latitude: coords[latitude], longitude: coords[longitude]}\n",
    "    return xr.DataArray(raster, coords=spatial_coords, dims=(latitude, longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# single multipolygon\n",
    "shapefile = gpd.read_file('/nfs/a68/earlacoa/shapefiles/china/CHN_adm0.shp')\n",
    "shapes = [(shape, index) for index, shape in enumerate(shapefile.geometry)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f3bb67d42b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using regridded file (xemsf)\n",
    "ds = xr.open_dataset(\n",
    "    '/nfs/b0122/Users/earlacoa/shared/nadia/wrfout_d01_global_0.25deg_2015-06_PM2_5_DRY_nadia.nc'\n",
    ")['PM2_5_DRY'].mean(dim='time')\n",
    "\n",
    "# apply shapefile to geometry, default: inside shapefile == 0, outside shapefile == np.nan\n",
    "ds['shapefile'] = rasterize(shapes, ds.coords, longitude='lon', latitude='lat') \n",
    "\n",
    "# change to more intuitive labelling of 1 for inside shapefile and np.nan for outside shapefile\n",
    "# if condition preserve (outside shapefile, as inside defaults to 0), otherwise (1, to mark in shapefile)\n",
    "ds['shapefile'] = ds.shapefile.where(cond=ds.shapefile!=0, other=1) \n",
    "\n",
    "# example: crop to shapefile\n",
    "# if condition (inside shapefile) preserve, otherwise (outside shapefile) remove\n",
    "ds = ds.where(cond=ds.shapefile==1, other=np.nan) # could scale instead with other=ds*scale\n",
    "\n",
    "ds.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solution 2\n",
    "- Cropping only\n",
    "- Destaggered/rectilinear grid e.g. after using `pp_concat_regrid.py` on WRFChem output with Salem to destagger and XEMSF to regrid to rectilinear grid\n",
    "  - For conservative regridding, consider [xgcm](https://xgcm.readthedocs.io/en/latest/).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import geopandas as gpd\n",
    "import rioxarray\n",
    "from shapely.geometry import mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "shapefile = gpd.read_file('/nfs/a68/earlacoa/shapefiles/china/CHN_adm0.shp', crs=\"epsg:4326\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(\n",
    "    '/nfs/b0122/Users/earlacoa/shared/nadia/wrfout_d01_global_0.25deg_2015-06_PM2_5_DRY_nadia.nc'\n",
    ")['PM2_5_DRY'].mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.rio.set_spatial_dims(x_dim=\"lon\", y_dim=\"lat\", inplace=True)\n",
    "ds.rio.write_crs(\"epsg:4326\", inplace=True)\n",
    "ds_clipped = ds.rio.clip(shapefile.geometry.apply(mapping), shapefile.crs, drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f3bbc9ee280>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_clipped.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solution 3\n",
    "- Staggered grid\n",
    "  - [WRFChem projection](https://fabienmaussion.info/2018/01/06/wrf-projection/) is normally on a [Arakawa-C Grid](https://xgcm.readthedocs.io/en/latest/grids.html)\n",
    "  - e.g. intermediate WRFChem files that need to be reused (wrfiobiochemi)\n",
    "  - e.g. raw wrfout file (after postprocessing) still on Arakawa-C Grid (2D lat/lon coordinates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import geopandas as gpd # ensure version > 0.8.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WARNING: the double for loop of geometry creation and checking is very slow\n",
    "# the mask in the cell below has already been calculated\n",
    "ds = xr.open_dataset('/nfs/b0122/Users/earlacoa/shared/nadia/wrfbiochemi')['MSEBIO_ISOP']\n",
    "shapefile = gpd.read_file('/nfs/a68/earlacoa/shapefiles/china/CHN_adm0.shp')\n",
    "mask = np.empty([ds.south_north.shape[0], ds.west_east.shape[0]])\n",
    "mask[:] = np.nan\n",
    "\n",
    "for index_lat in range(ds.south_north.shape[0]):\n",
    "    for index_lon in range(ds.west_east.shape[0]):\n",
    "        lat = ds.isel(south_north=index_lat).isel(west_east=index_lon).XLAT.values[0]\n",
    "        lon = ds.isel(south_north=index_lat).isel(west_east=index_lon).XLONG.values[0]\n",
    "\n",
    "        point_df = pd.DataFrame({'longitude': [lon], 'latitude': [lat]})\n",
    "        point_geometry = gpd.points_from_xy(point_df.longitude, point_df.latitude, crs=\"EPSG:4326\")\n",
    "        point_gdf = gpd.GeoDataFrame(point_df, geometry=point_geometry)\n",
    "\n",
    "        point_within_shapefile = point_gdf.within(shapefile)[0]\n",
    "        \n",
    "        if point_within_shapefile:\n",
    "            mask[index_lat][index_lon] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bring in mask which computed earlier\n",
    "ds = xr.open_dataset('/nfs/b0122/Users/earlacoa/shared/nadia/wrfbiochemi')\n",
    "mask = np.load('/nfs/b0122/Users/earlacoa/shared/nadia/mask_china.npz')['mask']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f3bb4be2790>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# demo - removing values in mask\n",
    "demo = ds['MSEBIO_ISOP'].where(cond=mask!=True, other=np.nan)\n",
    "demo.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f3bbcd0bbe0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# example - doubling isoprene emissions within mask\n",
    "ds['MSEBIO_ISOP'] = ds['MSEBIO_ISOP'].where(cond=mask!=True, other=2*ds['MSEBIO_ISOP'])\n",
    "ds['MSEBIO_ISOP'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving back into dataset\n",
    "ds.to_netcdf('/nfs/b0122/Users/earlacoa/shared/nadia/wrfbiochemi_double_isoprene_china')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check that doubling persisted\n",
    "ds_original = xr.open_dataset('/nfs/b0122/Users/earlacoa/shared/nadia/wrfbiochemi')['MSEBIO_ISOP']\n",
    "ds_double = xr.open_dataset('/nfs/b0122/Users/earlacoa/shared/nadia/wrfbiochemi_double_isoprene_china')['MSEBIO_ISOP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "fraction = ds_double / ds_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n",
      "1.0\n",
      "1.4608158\n"
     ]
    }
   ],
   "source": [
    "print(fraction.max().values)\n",
    "print(fraction.min().values)\n",
    "print(fraction.mean().values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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