{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7f87e8c3-2b52-447c-8279-025a4e7ea2bd",
   "metadata": {},
   "source": [
    "# Moisture Advection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "674063ab-3105-47f4-8407-1e408e653beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import metpy.calc as mcalc \n",
    "from metpy.units import units\n",
    "import glob\n",
    "import xarray as xr\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "from functools import partial\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.animation as animation\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from matplotlib.colors import Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89ea5cd6-bb2a-42a8-8561-3489e04c2aba",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_staging = \"/data/project/ARM_Summer_School_2024_Data/lasso_tutorial/cacti/lasso-cacti\"\n",
    "file_list = sorted(glob.glob(f'{path_staging}/20190129/eda09/base/les/subset_d3/corlasso_met_*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6a653c90-7cf0-442d-814d-3fa6789bd6a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(file_list[30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bbe7272f-2027-4372-8a87-18ca2cd32cfe",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 6GB\n",
       "Dimensions:               (Time: 1, south_north: 865, west_east: 750,\n",
       "                           bottom_top: 149)\n",
       "Coordinates:\n",
       "  * Time                  (Time) datetime64[ns] 8B 2019-01-29T13:30:00\n",
       "    XLONG                 (south_north, west_east) float32 3MB ...\n",
       "    XLAT                  (south_north, west_east) float32 3MB ...\n",
       "    XTIME                 (Time) float32 4B ...\n",
       "Dimensions without coordinates: south_north, west_east, bottom_top\n",
       "Data variables: (12/52)\n",
       "    ITIMESTEP             (Time) int32 4B ...\n",
       "    MUTOT                 (Time, south_north, west_east) float32 3MB ...\n",
       "    HGT                   (Time, south_north, west_east) float32 3MB ...\n",
       "    HAMSL                 (Time, bottom_top, south_north, west_east) float32 387MB ...\n",
       "    P_HYD                 (Time, bottom_top, south_north, west_east) float32 387MB ...\n",
       "    PRESSURE              (Time, bottom_top, south_north, west_east) float32 387MB ...\n",
       "    ...                    ...\n",
       "    MULFC                 (Time, south_north, west_east) float32 3MB ...\n",
       "    MULNB                 (Time, south_north, west_east) float32 3MB ...\n",
       "    MULPL                 (Time, south_north, west_east) float32 3MB ...\n",
       "    MUCAPE                (Time, south_north, west_east) float32 3MB ...\n",
       "    MUCIN                 (Time, south_north, west_east) float32 3MB ...\n",
       "    REFL_10CM_MAX         (Time, south_north, west_east) float32 3MB ...\n",
       "Attributes: (12/39)\n",
       "    DX:                          500.0\n",
       "    DY:                          500.0\n",
       "    SIMULATION_START_DATE:       2019-01-29_06:00:00\n",
       "    WEST-EAST_GRID_DIMENSION:    751\n",
       "    SOUTH-NORTH_GRID_DIMENSION:  866\n",
       "    BOTTOM-TOP_GRID_DIMENSION:   150\n",
       "    ...                          ...\n",
       "    doi_isPartOf_lasso-cacti:    https://doi.org/10.5439/1905789\n",
       "    doi_isDocumentedBy:          https://doi.org/10.2172/1905845\n",
       "    doi_thisFileType:            https://doi.org/10.5439/1905819\n",
       "    history:                     processed by user d3m088 on machine cirrus89...\n",
       "    filename_user:               corlasso_met_2019012900eda09d3_base_M1.m1.20...\n",
       "    filename_storage:            corlassomet2019012900eda09d3baseM1.m1.201901...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-53fce454-5c60-4ed8-9fa5-cf469e9e1dfc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-53fce454-5c60-4ed8-9fa5-cf469e9e1dfc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Time</span>: 1</li><li><span>south_north</span>: 865</li><li><span>west_east</span>: 750</li><li><span>bottom_top</span>: 149</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d051012b-97a2-419d-ac89-610936fb82ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d051012b-97a2-419d-ac89-610936fb82ac' 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'>Time</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-01-29T13:30:00</div><input id='attrs-92a6c6c1-6eb0-4d1f-ad31-bb067d706eff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92a6c6c1-6eb0-4d1f-ad31-bb067d706eff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10de8151-3824-49ea-9692-a20d57a16631' class='xr-var-data-in' type='checkbox'><label for='data-10de8151-3824-49ea-9692-a20d57a16631' title='Show/Hide data 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;2019-01-29T13:30:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLONG</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'>...</div><input id='attrs-c8d85282-ce08-40be-9b41-564c3307afe7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8d85282-ce08-40be-9b41-564c3307afe7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db26ac01-64cc-4a91-8bf7-c82c50838d61' class='xr-var-data-in' type='checkbox'><label for='data-db26ac01-64cc-4a91-8bf7-c82c50838d61' title='Show/Hide data 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>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLAT</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'>...</div><input id='attrs-0e953ba7-2b6d-4003-8191-9f6f866c85f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e953ba7-2b6d-4003-8191-9f6f866c85f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbe550fa-6148-4570-8922-709277367f31' class='xr-var-data-in' type='checkbox'><label for='data-cbe550fa-6148-4570-8922-709277367f31' title='Show/Hide data 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>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XTIME</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fb41a28c-a792-4091-b250-6f9b2528acdf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb41a28c-a792-4091-b250-6f9b2528acdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9973c843-4af6-4106-9156-5c139d187647' class='xr-var-data-in' type='checkbox'><label for='data-9973c843-4af6-4106-9156-5c139d187647' title='Show/Hide data 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>[1 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-001c09e4-7fde-4d8a-a44d-6da2ed8be02a' class='xr-section-summary-in' type='checkbox'  ><label for='section-001c09e4-7fde-4d8a-a44d-6da2ed8be02a' class='xr-section-summary' >Data variables: <span>(52)</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>ITIMESTEP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3d5474ef-b5b2-4eb5-883b-cc1b6b42663d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d5474ef-b5b2-4eb5-883b-cc1b6b42663d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fbd5bc2-dbb1-46bc-98bc-092369d77593' class='xr-var-data-in' type='checkbox'><label for='data-0fbd5bc2-dbb1-46bc-98bc-092369d77593' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>WRF timestep number since run start</dd><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MUTOT</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-c4f2591d-3e80-456a-9e21-5e284b0f69a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4f2591d-3e80-456a-9e21-5e284b0f69a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9388becc-a029-4a27-a22c-d0697e16bc47' class='xr-var-data-in' type='checkbox'><label for='data-9388becc-a029-4a27-a22c-d0697e16bc47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>dry air mass in column</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HGT</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-c7b6c295-92b7-4bb9-a6b0-d9ad27e3837a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7b6c295-92b7-4bb9-a6b0-d9ad27e3837a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ae5b618-d03a-46de-8fd7-cc03b6278d17' class='xr-var-data-in' type='checkbox'><label for='data-8ae5b618-d03a-46de-8fd7-cc03b6278d17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Terrain Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HAMSL</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-decd15f7-1780-44f6-aa01-51aab8484f69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-decd15f7-1780-44f6-aa01-51aab8484f69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ed1056a-4348-4a26-9e39-375928c60e27' class='xr-var-data-in' type='checkbox'><label for='data-9ed1056a-4348-4a26-9e39-375928c60e27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>model height - [MSL] (mass grid)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P_HYD</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-558d329f-dce6-428e-81ee-08622a72dfe4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-558d329f-dce6-428e-81ee-08622a72dfe4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4026b807-e720-44d5-a00c-f5948b4916c7' class='xr-var-data-in' type='checkbox'><label for='data-4026b807-e720-44d5-a00c-f5948b4916c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>hydrostatic pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PRESSURE</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-c2ae4efe-b9f5-4a38-9692-74eda5818eca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2ae4efe-b9f5-4a38-9692-74eda5818eca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a89c90a8-9978-467d-8a6d-1434657ad6be' class='xr-var-data-in' type='checkbox'><label for='data-a89c90a8-9978-467d-8a6d-1434657ad6be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALT</span></div><div class='xr-var-dims'>(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-25be04b1-c76e-494a-8f15-dc2562df879f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25be04b1-c76e-494a-8f15-dc2562df879f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ce039ab-2127-401a-a75c-174800a8e6b9' class='xr-var-data-in' type='checkbox'><label for='data-4ce039ab-2127-401a-a75c-174800a8e6b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>inverse density</dd><dt><span>units :</span></dt><dd>m3 kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TEMPERATURE</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-060f5873-7b33-46f0-80f7-b87b1f8b8337' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-060f5873-7b33-46f0-80f7-b87b1f8b8337' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-383c13e2-7c2b-479e-92c7-82caec4e2925' class='xr-var-data-in' type='checkbox'><label for='data-383c13e2-7c2b-479e-92c7-82caec4e2925' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THETA</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-9d7aef32-0013-4174-b6c8-60811c13d130' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d7aef32-0013-4174-b6c8-60811c13d130' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5de80e4d-8c49-4d03-854c-79d4613d8af1' class='xr-var-data-in' type='checkbox'><label for='data-5de80e4d-8c49-4d03-854c-79d4613d8af1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>potential temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THETA_E</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-541a2f0e-7d44-4df6-95f7-15a357c00d1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-541a2f0e-7d44-4df6-95f7-15a357c00d1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66072f34-9c20-427b-8597-515cfbdc0430' class='xr-var-data-in' type='checkbox'><label for='data-66072f34-9c20-427b-8597-515cfbdc0430' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>equivalent potential temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>QVAPOR</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-8507b86f-2792-4006-aeb9-55d60d5b4c1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8507b86f-2792-4006-aeb9-55d60d5b4c1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61a96592-a837-4ae5-b34f-6187d41f8185' class='xr-var-data-in' type='checkbox'><label for='data-61a96592-a837-4ae5-b34f-6187d41f8185' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Water vapor mixing ratio</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RH</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-5a6b7bd2-9d92-4ffc-98ec-a6e843b687d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a6b7bd2-9d92-4ffc-98ec-a6e843b687d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cddbbd6-f3df-4671-961e-5f25271acb15' class='xr-var-data-in' type='checkbox'><label for='data-9cddbbd6-f3df-4671-961e-5f25271acb15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>relative humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UA</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-dce1634a-33a7-484b-8653-dd61f11401d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dce1634a-33a7-484b-8653-dd61f11401d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-436357ab-2d4e-4284-821c-fee5aeeefbe1' class='xr-var-data-in' type='checkbox'><label for='data-436357ab-2d4e-4284-821c-fee5aeeefbe1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>destaggered u-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VA</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-34279e92-00d5-4a1a-be9a-d09703f05153' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34279e92-00d5-4a1a-be9a-d09703f05153' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae2e1de7-b1c5-4da9-99d0-d7ded88513e1' class='xr-var-data-in' type='checkbox'><label for='data-ae2e1de7-b1c5-4da9-99d0-d7ded88513e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>destaggered v-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WA</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-4d9ebc98-501a-43ae-a173-39e1874127b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d9ebc98-501a-43ae-a173-39e1874127b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-444227fc-44d0-4f4e-b487-d7250a6e8d42' class='xr-var-data-in' type='checkbox'><label for='data-444227fc-44d0-4f4e-b487-d7250a6e8d42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>destaggered w-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UMET</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-536f3cb9-2d07-4246-9f44-513b319d7eb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-536f3cb9-2d07-4246-9f44-513b319d7eb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8072462e-3f6c-4754-8863-cb66f2216524' class='xr-var-data-in' type='checkbox'><label for='data-8072462e-3f6c-4754-8863-cb66f2216524' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative u-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VMET</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-050f9eb3-db2d-4ba3-82da-2ace7107f45a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-050f9eb3-db2d-4ba3-82da-2ace7107f45a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df5eb6ec-ea58-4fb7-ab4c-8d0ae6bb8034' class='xr-var-data-in' type='checkbox'><label for='data-df5eb6ec-ea58-4fb7-ab4c-8d0ae6bb8034' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative v-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>POTVORT</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-3505dcca-4660-4152-bf72-73d7d82e198c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3505dcca-4660-4152-bf72-73d7d82e198c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29990654-cc19-4fac-a21b-2780a00a517e' class='xr-var-data-in' type='checkbox'><label for='data-29990654-cc19-4fac-a21b-2780a00a517e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>potential vorticity</dd><dt><span>units :</span></dt><dd>PVU</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[96663750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PSFC</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-8fc7a78d-2654-46ec-abef-f47a545f5107' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fc7a78d-2654-46ec-abef-f47a545f5107' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2dff5d1-7605-4c58-8049-1dcb8c6932fb' class='xr-var-data-in' type='checkbox'><label for='data-d2dff5d1-7605-4c58-8049-1dcb8c6932fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>SFC PRESSURE</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SLP</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-0c96599e-68cd-490c-8a22-7c0372b6541b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c96599e-68cd-490c-8a22-7c0372b6541b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2e5882a-abf4-450e-885d-714884a097f7' class='xr-var-data-in' type='checkbox'><label for='data-b2e5882a-abf4-450e-885d-714884a097f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>sea level pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Q2</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-7bb368b8-87c6-41c1-a9c5-39f7a221b871' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bb368b8-87c6-41c1-a9c5-39f7a221b871' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d75345b9-986c-4ec9-9b8f-89203268b379' class='xr-var-data-in' type='checkbox'><label for='data-d75345b9-986c-4ec9-9b8f-89203268b379' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>QV at 2 M</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><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-d15f8430-2535-4791-90aa-53d1d07b8ac6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d15f8430-2535-4791-90aa-53d1d07b8ac6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf28d6cd-d520-4c8f-87f2-75f07a5250cd' class='xr-var-data-in' type='checkbox'><label for='data-bf28d6cd-d520-4c8f-87f2-75f07a5250cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>TEMP at 2 M</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TH2</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-2dc2e373-5e2e-4c64-9cdf-ae49fe14d88e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2dc2e373-5e2e-4c64-9cdf-ae49fe14d88e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-775d6e69-8825-4155-b7ba-53ca2ca512c1' class='xr-var-data-in' type='checkbox'><label for='data-775d6e69-8825-4155-b7ba-53ca2ca512c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>POT TEMP at 2 M</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U10</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-6e79a7e4-3113-4c61-9e3c-1337c83a055e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e79a7e4-3113-4c61-9e3c-1337c83a055e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abbc3b1a-2ed3-4f3a-9219-f3af939de66b' class='xr-var-data-in' type='checkbox'><label for='data-abbc3b1a-2ed3-4f3a-9219-f3af939de66b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>U at 10 M</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V10</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-1a3a6b5f-4924-4d2a-afb1-b58866d6a0ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a3a6b5f-4924-4d2a-afb1-b58866d6a0ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8c0ee97-0b07-4b85-866e-654abca43b73' class='xr-var-data-in' type='checkbox'><label for='data-e8c0ee97-0b07-4b85-866e-654abca43b73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>V at 10 M</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UMET10</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-93753d8d-e11d-4e90-8186-ed0c9b189585' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93753d8d-e11d-4e90-8186-ed0c9b189585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab9153ba-4691-4d99-bd46-e047ac0a6c70' class='xr-var-data-in' type='checkbox'><label for='data-ab9153ba-4691-4d99-bd46-e047ac0a6c70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative 10-m u-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VMET10</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-0ebf8bfc-3f31-4d0f-83b4-25b40db02e4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ebf8bfc-3f31-4d0f-83b4-25b40db02e4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f1e46d6-4f94-4536-bff6-70c51e908031' class='xr-var-data-in' type='checkbox'><label for='data-1f1e46d6-4f94-4536-bff6-70c51e908031' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative 10-m v-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHEAR_MAG_SFC-TO-1KM</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-90e99170-28ed-45e4-9024-2a752dcda3a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90e99170-28ed-45e4-9024-2a752dcda3a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b26cd89-b3c9-410d-a3fc-b659bf803cd2' class='xr-var-data-in' type='checkbox'><label for='data-9b26cd89-b3c9-410d-a3fc-b659bf803cd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>description :</span></dt><dd>bulk wind-shear magnitude, sfc to 1000.0</dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHEAR_DIR_SFC-TO-1KM</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-2d9f67dc-338e-49f4-8ae4-92caa48f3e95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d9f67dc-338e-49f4-8ae4-92caa48f3e95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcceec83-5b86-4d21-9cca-9e83e204bc62' class='xr-var-data-in' type='checkbox'><label for='data-bcceec83-5b86-4d21-9cca-9e83e204bc62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>description :</span></dt><dd>bulk wind-shear direction, sfc to 1000.0</dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHEAR_MAG_SFC-TO-3KM</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-fa0dfe20-9b21-4768-9c77-826c01b725f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa0dfe20-9b21-4768-9c77-826c01b725f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dac23ffc-638c-46dc-9306-62e760b2577a' class='xr-var-data-in' type='checkbox'><label for='data-dac23ffc-638c-46dc-9306-62e760b2577a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>description :</span></dt><dd>bulk wind-shear magnitude, sfc to 3000.0</dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHEAR_DIR_SFC-TO-3KM</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-8d4199e5-9cb7-40cb-8492-ccbf10c2ad46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d4199e5-9cb7-40cb-8492-ccbf10c2ad46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0d30046-13ab-47aa-9486-311b2138f17d' class='xr-var-data-in' type='checkbox'><label for='data-c0d30046-13ab-47aa-9486-311b2138f17d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>description :</span></dt><dd>bulk wind-shear direction, sfc to 3000.0</dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHEAR_MAG_SFC-TO-6KM</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-0436796c-ac04-4738-af1e-30d39cede5fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0436796c-ac04-4738-af1e-30d39cede5fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24302791-8998-4a8e-96dd-ea5374e87ac9' class='xr-var-data-in' type='checkbox'><label for='data-24302791-8998-4a8e-96dd-ea5374e87ac9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>description :</span></dt><dd>bulk wind-shear magnitude, sfc to 6000.0</dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHEAR_DIR_SFC-TO-6KM</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-95d0a1a4-2cb9-455b-81f0-ebd6912afdc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95d0a1a4-2cb9-455b-81f0-ebd6912afdc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a4285d2-7ece-4901-9432-7a6c39497419' class='xr-var-data-in' type='checkbox'><label for='data-1a4285d2-7ece-4901-9432-7a6c39497419' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>description :</span></dt><dd>bulk wind-shear direction, sfc to 6000.0</dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHEAR_MAG_SFC-TO-9KM</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-8d52f05a-ae47-4211-9962-03523e03a267' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d52f05a-ae47-4211-9962-03523e03a267' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a87c25f-55b5-4d58-9108-1994b2bb4139' class='xr-var-data-in' type='checkbox'><label for='data-2a87c25f-55b5-4d58-9108-1994b2bb4139' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>description :</span></dt><dd>bulk wind-shear magnitude, sfc to 9000.0</dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SHEAR_DIR_SFC-TO-9KM</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-57fc4019-43cf-49b3-9ad4-470d0e287152' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57fc4019-43cf-49b3-9ad4-470d0e287152' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a7826df-3a64-4357-96f9-82a2faf61e0c' class='xr-var-data-in' type='checkbox'><label for='data-5a7826df-3a64-4357-96f9-82a2faf61e0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>description :</span></dt><dd>bulk wind-shear direction, sfc to 9000.0</dd></dl></div><div class='xr-var-data'><pre>[648750 values with 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-5a4456ee-18cf-4fb9-a238-8afffff03c84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a4456ee-18cf-4fb9-a238-8afffff03c84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdb60022-8e0f-4d50-908e-9dfbddd3719d' class='xr-var-data-in' type='checkbox'><label for='data-fdb60022-8e0f-4d50-908e-9dfbddd3719d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE PRECIPITATION</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOWNC</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-958c161c-8cdf-46da-b46c-18ddc115d187' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-958c161c-8cdf-46da-b46c-18ddc115d187' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6adad69-dec4-41ea-ad59-2aa965638c2c' class='xr-var-data-in' type='checkbox'><label for='data-f6adad69-dec4-41ea-ad59-2aa965638c2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE SNOW AND ICE</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GRAUPELNC</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-d8b17fed-e433-48ac-8556-215a8e126b69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8b17fed-e433-48ac-8556-215a8e126b69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9df0020-1ecc-4f15-98d3-67f3c63d2b28' class='xr-var-data-in' type='checkbox'><label for='data-f9df0020-1ecc-4f15-98d3-67f3c63d2b28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE GRAUPEL</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SR</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-7c4313b0-5dc4-47d2-a1ee-10d8ebe564ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c4313b0-5dc4-47d2-a1ee-10d8ebe564ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e29ddc9c-dc81-4f47-a4d5-8684be9b16ba' class='xr-var-data-in' type='checkbox'><label for='data-e29ddc9c-dc81-4f47-a4d5-8684be9b16ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>fraction of frozen precipitation</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MLLCL</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-93df0a47-ff82-4caa-a312-2dd4be614b34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93df0a47-ff82-4caa-a312-2dd4be614b34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b4eacd1-eb93-47c7-a1a2-10241b8c4a99' class='xr-var-data-in' type='checkbox'><label for='data-8b4eacd1-eb93-47c7-a1a2-10241b8c4a99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable lifting condensation level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MLLFC</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-4a0d8063-d9af-49f9-8699-9bc80d28110b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a0d8063-d9af-49f9-8699-9bc80d28110b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-748152db-da06-4250-b6a4-936f5efa48e9' class='xr-var-data-in' type='checkbox'><label for='data-748152db-da06-4250-b6a4-936f5efa48e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable level of free convection</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MLLNB</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-50799a86-7933-49eb-8c60-8ae7e39fbf10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50799a86-7933-49eb-8c60-8ae7e39fbf10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b92b0cbc-09e0-44bd-8ee4-808f68b29c5a' class='xr-var-data-in' type='checkbox'><label for='data-b92b0cbc-09e0-44bd-8ee4-808f68b29c5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable level of neutral buoyancy</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MLLPL</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-07a8b926-b6c6-4f61-919b-eb66ba166db5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07a8b926-b6c6-4f61-919b-eb66ba166db5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c18e86aa-cda6-4a5d-9a08-ab5e62987858' class='xr-var-data-in' type='checkbox'><label for='data-c18e86aa-cda6-4a5d-9a08-ab5e62987858' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable lifted parcel level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MLCAPE</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-c52ec4cd-a441-4782-a9c0-dc74814ccfcf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c52ec4cd-a441-4782-a9c0-dc74814ccfcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48a68676-2c76-4708-bfed-c8e9637e5450' class='xr-var-data-in' type='checkbox'><label for='data-48a68676-2c76-4708-bfed-c8e9637e5450' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable convective available potential energy</dd><dt><span>units :</span></dt><dd>J kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MLCIN</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-6e0ac68d-1c11-4cee-a3b7-564879ac0546' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e0ac68d-1c11-4cee-a3b7-564879ac0546' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76b69387-1f79-487e-b064-27c0cc55df77' class='xr-var-data-in' type='checkbox'><label for='data-76b69387-1f79-487e-b064-27c0cc55df77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable convective inhibition</dd><dt><span>units :</span></dt><dd>J kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MULCL</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-db060217-1ef4-4b44-aa4a-3e3a9df5e58d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db060217-1ef4-4b44-aa4a-3e3a9df5e58d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fbcf13a-a8f9-4512-93b0-483cb940a612' class='xr-var-data-in' type='checkbox'><label for='data-3fbcf13a-a8f9-4512-93b0-483cb940a612' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable lifting condensation level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MULFC</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-c01ec43f-e256-4267-a367-8711f71a8c77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c01ec43f-e256-4267-a367-8711f71a8c77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff5b541e-2029-434c-814c-d638bdc1d89f' class='xr-var-data-in' type='checkbox'><label for='data-ff5b541e-2029-434c-814c-d638bdc1d89f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable level of free convection</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MULNB</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-d9f09f3b-ade9-47f1-9e44-83872586e5e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9f09f3b-ade9-47f1-9e44-83872586e5e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9aaa3d2-da0c-421b-9a2e-3a27fd43bb9e' class='xr-var-data-in' type='checkbox'><label for='data-a9aaa3d2-da0c-421b-9a2e-3a27fd43bb9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable level of neutral buoyancy</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MULPL</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-cd087131-b07f-45fa-9c92-5035ee9c6a71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd087131-b07f-45fa-9c92-5035ee9c6a71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d322316a-02cd-4e5e-b299-644c4bd766a2' class='xr-var-data-in' type='checkbox'><label for='data-d322316a-02cd-4e5e-b299-644c4bd766a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable lifted parcel level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MUCAPE</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-7ec1b19d-1560-4d17-ba0d-ccc81e4a09a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ec1b19d-1560-4d17-ba0d-ccc81e4a09a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82ebbd5e-c234-4810-861e-4aa7c4c05993' class='xr-var-data-in' type='checkbox'><label for='data-82ebbd5e-c234-4810-861e-4aa7c4c05993' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable convective available potential energy</dd><dt><span>units :</span></dt><dd>J kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MUCIN</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-d1dec256-aadf-432d-8446-96efd0d0f48c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1dec256-aadf-432d-8446-96efd0d0f48c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c79efa4c-a82f-4397-8658-1b78c5212a96' class='xr-var-data-in' type='checkbox'><label for='data-c79efa4c-a82f-4397-8658-1b78c5212a96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>most-unstable convective inhibition</dd><dt><span>units :</span></dt><dd>J kg-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>REFL_10CM_MAX</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-cf0d8b94-c7f2-4ff2-af3d-8e19fe02b4fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf0d8b94-c7f2-4ff2-af3d-8e19fe02b4fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2046669-1025-40d9-96c2-1bb0568fc04d' class='xr-var-data-in' type='checkbox'><label for='data-a2046669-1025-40d9-96c2-1bb0568fc04d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>column-maximum radar reflectivity (lambda = 10 cm)</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>[648750 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68265b92-6b5c-4ed7-be1e-dc803f1001c6' class='xr-section-summary-in' type='checkbox'  ><label for='section-68265b92-6b5c-4ed7-be1e-dc803f1001c6' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ab73fbf0-104a-4abb-a5c8-fcde2fcd5fab' class='xr-index-data-in' type='checkbox'/><label for='index-ab73fbf0-104a-4abb-a5c8-fcde2fcd5fab' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-01-29 13:30:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ee4c93f-e316-4fc5-8cd7-e2bf269e9449' class='xr-section-summary-in' type='checkbox'  ><label for='section-7ee4c93f-e316-4fc5-8cd7-e2bf269e9449' class='xr-section-summary' >Attributes: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>DX :</span></dt><dd>500.0</dd><dt><span>DY :</span></dt><dd>500.0</dd><dt><span>SIMULATION_START_DATE :</span></dt><dd>2019-01-29_06:00:00</dd><dt><span>WEST-EAST_GRID_DIMENSION :</span></dt><dd>751</dd><dt><span>SOUTH-NORTH_GRID_DIMENSION :</span></dt><dd>866</dd><dt><span>BOTTOM-TOP_GRID_DIMENSION :</span></dt><dd>150</dd><dt><span>GRIDTYPE :</span></dt><dd>C</dd><dt><span>CEN_LAT :</span></dt><dd>-32.01111</dd><dt><span>CEN_LON :</span></dt><dd>-64.23425</dd><dt><span>TRUELAT1 :</span></dt><dd>-30.0</dd><dt><span>TRUELAT2 :</span></dt><dd>-60.0</dd><dt><span>MOAD_CEN_LAT :</span></dt><dd>-32.500015</dd><dt><span>STAND_LON :</span></dt><dd>-64.728</dd><dt><span>POLE_LAT :</span></dt><dd>90.0</dd><dt><span>POLE_LON :</span></dt><dd>0.0</dd><dt><span>MAP_PROJ :</span></dt><dd>1</dd><dt><span>MAP_PROJ_CHAR :</span></dt><dd>Lambert Conformal</dd><dt><span>run_name :</span></dt><dd>eda09_2019012900_base</dd><dt><span>model_type :</span></dt><dd>WRF</dd><dt><span>model_version :</span></dt><dd>NCAR V4.3.1 + LASSO modifications</dd><dt><span>model_source_repository :</span></dt><dd>https://code.arm.gov/lasso/lasso-cacti/lasso-wrf-cacti/</dd><dt><span>model_source_git_hash :</span></dt><dd>3e761eac</dd><dt><span>subset_source_repository :</span></dt><dd>https://code.arm.gov/lasso/lasso-cacti/subsetwrf</dd><dt><span>subset_source_git_hash :</span></dt><dd>99902f2</dd><dt><span>domain_num :</span></dt><dd>3</dd><dt><span>input_source :</span></dt><dd>/gpfs/wolf/cli120/world-shared/d3m088/cacti/staged_runs/20190129/eda09/base/les/out_d3</dd><dt><span>site_id :</span></dt><dd>cor</dd><dt><span>platform_id :</span></dt><dd>corlasso</dd><dt><span>facility_designation :</span></dt><dd>M1</dd><dt><span>location_description :</span></dt><dd>Cloud, Aerosol, and Complex Terrain Interactions (CACTI), Cordoba, Argentina</dd><dt><span>data_level :</span></dt><dd>m1</dd><dt><span>datastream :</span></dt><dd>corlasso_sub_met</dd><dt><span>contact :</span></dt><dd>lasso@arm.gov, LASSO PI: William Gustafson (PNNL, william.gustafson@pnnl.gov), LASSO Co-PI: Andrew Vogelmann (BNL)</dd><dt><span>doi_isPartOf_lasso-cacti :</span></dt><dd>https://doi.org/10.5439/1905789</dd><dt><span>doi_isDocumentedBy :</span></dt><dd>https://doi.org/10.2172/1905845</dd><dt><span>doi_thisFileType :</span></dt><dd>https://doi.org/10.5439/1905819</dd><dt><span>history :</span></dt><dd>processed by user d3m088 on machine cirrus89.ccs.ornl.gov at 23-Jan-2023 08:02 using subsetwrf.py</dd><dt><span>filename_user :</span></dt><dd>corlasso_met_2019012900eda09d3_base_M1.m1.20190129.133000.nc</dd><dt><span>filename_storage :</span></dt><dd>corlassomet2019012900eda09d3baseM1.m1.20190129.133000.nc</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 6GB\n",
       "Dimensions:               (Time: 1, south_north: 865, west_east: 750,\n",
       "                           bottom_top: 149)\n",
       "Coordinates:\n",
       "  * Time                  (Time) datetime64[ns] 8B 2019-01-29T13:30:00\n",
       "    XLONG                 (south_north, west_east) float32 3MB ...\n",
       "    XLAT                  (south_north, west_east) float32 3MB ...\n",
       "    XTIME                 (Time) float32 4B ...\n",
       "Dimensions without coordinates: south_north, west_east, bottom_top\n",
       "Data variables: (12/52)\n",
       "    ITIMESTEP             (Time) int32 4B ...\n",
       "    MUTOT                 (Time, south_north, west_east) float32 3MB ...\n",
       "    HGT                   (Time, south_north, west_east) float32 3MB ...\n",
       "    HAMSL                 (Time, bottom_top, south_north, west_east) float32 387MB ...\n",
       "    P_HYD                 (Time, bottom_top, south_north, west_east) float32 387MB ...\n",
       "    PRESSURE              (Time, bottom_top, south_north, west_east) float32 387MB ...\n",
       "    ...                    ...\n",
       "    MULFC                 (Time, south_north, west_east) float32 3MB ...\n",
       "    MULNB                 (Time, south_north, west_east) float32 3MB ...\n",
       "    MULPL                 (Time, south_north, west_east) float32 3MB ...\n",
       "    MUCAPE                (Time, south_north, west_east) float32 3MB ...\n",
       "    MUCIN                 (Time, south_north, west_east) float32 3MB ...\n",
       "    REFL_10CM_MAX         (Time, south_north, west_east) float32 3MB ...\n",
       "Attributes: (12/39)\n",
       "    DX:                          500.0\n",
       "    DY:                          500.0\n",
       "    SIMULATION_START_DATE:       2019-01-29_06:00:00\n",
       "    WEST-EAST_GRID_DIMENSION:    751\n",
       "    SOUTH-NORTH_GRID_DIMENSION:  866\n",
       "    BOTTOM-TOP_GRID_DIMENSION:   150\n",
       "    ...                          ...\n",
       "    doi_isPartOf_lasso-cacti:    https://doi.org/10.5439/1905789\n",
       "    doi_isDocumentedBy:          https://doi.org/10.2172/1905845\n",
       "    doi_thisFileType:            https://doi.org/10.5439/1905819\n",
       "    history:                     processed by user d3m088 on machine cirrus89...\n",
       "    filename_user:               corlasso_met_2019012900eda09d3_base_M1.m1.20...\n",
       "    filename_storage:            corlassomet2019012900eda09d3baseM1.m1.201901..."
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9fd6dd29-e2de-4fdc-8763-bc8557e81080",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = ds.QVAPOR[0,0,:,:]\n",
    "u = ds.UMET10[0,:,:]\n",
    "v = ds.VMET10[0,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "44598fe0-fb4c-4f8a-bdc5-3d7c0a185233",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f8eae6e0e90>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "space =100\n",
    "\n",
    "cf=plt.contourf(ds.XLONG,ds.XLAT,theta, cmap='terrain_r', levels=np.linspace(0.001,0.025, 15))\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "time_label=plt.title('Moisture Field')\n",
    "tx_time= plt.title(f\"Moisture Field {pd.to_datetime(ds['Time'])[0]:%Y-%m-%d %H:%M} UTC\")\n",
    "\n",
    "ba = plt.barbs(ds.XLONG [::space, ::space], ds.XLAT[::space, ::space], u[::space, ::space],v[::space, ::space])\n",
    "plt.colorbar(cf, label='Water Vapor mixing ratio(kg/kg)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1ea566e5-1211-4de1-ab99-a67219758393",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(file_list[50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "97570420-0fb3-4d61-a7a5-8e540101900e",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = ds.QVAPOR[0,0,:,:]\n",
    "u = ds.UMET10[0,:,:]\n",
    "v = ds.VMET10[0,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3ff5ec33-28b3-442e-9ad5-79e07dd4caec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f8eae500e90>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "space =100\n",
    "\n",
    "cf=plt.contourf(ds.XLONG,ds.XLAT,theta, cmap='terrain_r', levels=np.linspace(0.001,0.025, 15))\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "time_label=plt.title('Moisture Field')\n",
    "tx_time= plt.title(f\"Moisture Field {pd.to_datetime(ds['Time'])[0]:%Y-%m-%d %H:%M} UTC\")\n",
    "\n",
    "ba = plt.barbs(ds.XLONG [::space, ::space], ds.XLAT[::space, ::space], u[::space, ::space],v[::space, ::space])\n",
    "plt.colorbar(cf, label='Water Vapor mixing ratio(kg/kg)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6bb993a9-bdd9-4730-a58f-226f3f89dfec",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(file_list[70])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "01cede34-7e89-4780-972d-633de73a1112",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = ds.QVAPOR[0,0,:,:]\n",
    "u = ds.UMET10[0,:,:]\n",
    "v = ds.VMET10[0,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "70ab2f29-91f3-4b7e-9fde-58455a10405c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f8eae3d7710>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "space =100\n",
    "\n",
    "cf=plt.contourf(ds.XLONG,ds.XLAT,theta, cmap='terrain_r', levels=np.linspace(0.001,0.025, 15))\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "time_label=plt.title('Moisture Field')\n",
    "tx_time= plt.title(f\"Moisture Field {pd.to_datetime(ds['Time'])[0]:%Y-%m-%d %H:%M} UTC\")\n",
    "\n",
    "ba = plt.barbs(ds.XLONG [::space, ::space], ds.XLAT[::space, ::space], u[::space, ::space],v[::space, ::space])\n",
    "plt.colorbar(cf, label='Water Vapor mixing ratio(kg/kg)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b520180f-13f6-4284-a8b0-ccbe0aa1750c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6401dd92-9924-4e73-9c0e-3d20bd3d483b",
   "metadata": {},
   "source": [
    "## Cross Sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "81caa0db-a562-4e5e-b595-9c414689ca19",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_staging = \"/data/project/ARM_Summer_School_2024_Data/lasso_tutorial/cacti/lasso-cacti\"\n",
    "file_list = sorted(glob.glob(f'{path_staging}/20190129/eda09/base/les/subset_d3/corlasso_met_*'))\n",
    "\n",
    "ntimes=73\n",
    "ds= xr.open_mfdataset(file_list[:ntimes])\n",
    "\n",
    "file_list = sorted(glob.glob(f'{path_staging}/20190129/eda09/base/les/subset_d3/corlasso_cld_*'))\n",
    "ds2= xr.open_mfdataset(file_list[:ntimes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f56433ad-04f0-4d41-a822-ca64d4cebd7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1683/1194218404.py:73: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.\n",
      "  pcm= ax.pcolormesh(lonslice.values, hamsl_raw.values, plotdata_raw.isel(Time=0).values,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(750, 149)\n",
      "(15, 75)\n",
      "(15, 75)\n",
      "(15, 75)\n",
      "(15, 75)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# User settings...\n",
    "\n",
    "# Case date and times to plot:\n",
    "case_date = datetime(2019, 1, 29)\n",
    "single_time = case_date + timedelta(hours=20, minutes=15)  # time for single-time plots\n",
    "\n",
    "# Simulation selection within the case date:\n",
    "ens_name = \"eda09\"\n",
    "config_label = \"base\"\n",
    "domain = 2\n",
    "\n",
    "\n",
    "\n",
    "plot_lat= -32.1264  #S\n",
    "\n",
    "\n",
    "# Next, we have to figure out which j-index to sample for the desired latitude. WRF's coordinate name for this dimension is south_north.\n",
    "# We will ignore the map projection and pull along a single j index, which we will determine from the middle of the grid.\n",
    "lats = ds2[\"XLAT\"]\n",
    "lons = ds2[\"XLONG\"]\n",
    "nlat, nlon = lons.shape\n",
    "abslat = np.abs(lats.isel(west_east=int(nlon/2)) - plot_lat)\n",
    "jloc = np.argmin(abslat.values)\n",
    "lonslice = lons.isel(south_north=jloc)\n",
    "hamsl_raw = ds['HAMSL'].isel(Time=0, south_north=jloc)  # height of each grid cell on the raw model levels\n",
    "\n",
    "\n",
    "# ...and now the data we actually want to plot, which is clouds\n",
    "plotdata_raw = 1000.*(\n",
    "    ds2['QCLOUD'].sel(south_north=jloc) +\n",
    "    ds2['QICE'].sel(south_north=jloc)\n",
    "    )  # cloud water+ice mixing ratio converted go g/kg\n",
    "\n",
    "#pcm=plotdata_raw.isel(Time=0).plot()\n",
    "\n",
    "\n",
    "\n",
    "def plot_subsequent_frame(itime, plotdata_raw, pcm, tx_time, ba, ba_U, ba_V,ifreq):\n",
    "    \"\"\"\n",
    "    Subroutine to plot the next frame of an animation after the first frame is already defined. \n",
    "    This is to be called by animation.FuncAnimation. \n",
    "    \n",
    "    Note that some data is pulled from global scope. Variables accessed from outside the subroutine:\n",
    "        pcm = pcolormesh object from the initial frame\n",
    "        tx_time = text annotation object for the time label from the initial frame\n",
    "\n",
    "        plotdata_raw = variable holding the time series of data to plot\n",
    "        plot_lat = latitude being plotted (needed to reconstruct the title text)\n",
    "    \"\"\"\n",
    "    print(\"plotting frame \", itime)\n",
    "\n",
    "    # Overwrite the data in memory for the values displayed in the plot...\n",
    "    # NOTE: This is technically a faux paux for WRF since the model levels are not constant in time. BE CAREFUL!\n",
    "    #       However, in this situation the levels are not going to change so much as to make the plot visibly change much.\n",
    "    pcm.set_array(plotdata_raw.isel(Time=itime).values.ravel())\n",
    "\n",
    "    print(ba_U.isel(Time=itime).values[::ifreq, ::ifreq].shape)\n",
    "    print(ba_V.isel(Time=itime).values[::ifreq, ::ifreq].shape) \n",
    "    \n",
    "\n",
    "    #ba.set_UVC(ba_U.isel(Time=itime).values[::ifreq, ::ifreq].ravel(),ba_V.isel(Time=itime).values[::ifreq, ::ifreq].ravel())\n",
    "\n",
    "    # Update the time label...\n",
    "    time_label = f\"{pd.to_datetime(plotdata_raw['Time'])[itime]:%Y-%m-%d %H:%M} UTC\"\n",
    "    tx_time.set_text(f\"Cloud Condensate; \\n{time_label}\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# end plot_subsequent_frame()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=[8,3.5])\n",
    "pcm= ax.pcolormesh(lonslice.values, hamsl_raw.values, plotdata_raw.isel(Time=0).values,\n",
    "                   shading='auto',norm=Normalize(vmin=0,vmax=1.25))\n",
    "#pcm=plotdata_raw.plot()\n",
    "#theta = ds.QVAPOR[0,0,:,:]\n",
    "\n",
    "da_u = ds[\"UMET\"].isel(south_north=jloc)\n",
    "da_v = ds[\"VMET\"].isel(south_north=jloc)\n",
    "\n",
    "space =100\n",
    "\n",
    "cf=plt.contourf(ds.XLONG,ds.XLAT,theta)\n",
    "#plt.xlabel('Longitude')\n",
    "#plt.ylabel('Latitude')\n",
    "tx_time= plt.title(f\"Moisture Field {pd.to_datetime(plotdata_raw['Time'])[0]:%Y-%m-%d %H:%M} UTC\")\n",
    "\n",
    "\n",
    "lonslice2d = (lonslice+(hamsl_raw*0)).T\n",
    "print(lonslice2d.T.shape)\n",
    "#print(hamsl_raw.isel(Time=0).shape)\n",
    "#print(da_u.isel(Time=0).shape)\n",
    "da_u = ds[\"UMET\"].isel(south_north=jloc)\n",
    "da_v = ds[\"VMET\"].isel(south_north=jloc)\n",
    "ifreq = 10\n",
    "\n",
    "print(lonslice2d.values[::ifreq,::ifreq].shape)\n",
    "print(hamsl_raw.values[::ifreq,::ifreq].shape) \n",
    "print(da_u.isel(Time=0).values[::ifreq,::ifreq].shape)\n",
    "print(da_v.isel(Time=0).values[::ifreq,::ifreq].shape)\n",
    "\n",
    "#plt.barbs(lonslice2d.values[::ifreq,::ifreq], hamsl_raw.values[::ifreq,::ifreq], da_u.isel(Time=0).values[::ifreq,::ifreq], da_v.isel(Time=0).values[::ifreq,::ifreq],color='w')\n",
    "# plt.pcolormesh(lonslice.values, hamsl_raw.isel(Time=0).values, da_v.isel(Time=0).values)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.colorbar(cf, label='Water Vapor mixing ratio(kg/kg)')\n",
    "\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------------------\n",
    "# Now, update the underlying data in the plot for each subsequent frame...\n",
    "\n",
    "# NOTE: See https://matplotlib.org/stable/api/_as_gen/matplotlib.animation.FuncAnimation.html for\n",
    "#       explaination of how to use functools.partial for passing arguments to the redraw subroutine.\n",
    "ani = animation.FuncAnimation(fig, \n",
    "                              partial(plot_subsequent_frame, plotdata_raw=plotdata_raw, pcm=pcm, tx_time=tx_time,ba=ba,ba_U=da_u,ba_V=da_v,ifreq=ifreq),\n",
    "                              frames=range(ntimes))\n",
    "\n",
    "# And save the results...\n",
    "filename_mp4 = f\"./corlasso_anim_qci_{case_date:%Y%m%d}00{ens_name}d{domain}_{config_label}.mp4\"\n",
    "fps = 6 if domain < 3 else 8  # set frame rate for dt=15 vs. 5 minutes\n",
    "ani.save(filename_mp4, writer=animation.FFMpegWriter(fps=fps, bitrate=5000, codec=\"h264\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3525cec6-654c-44f3-9c1e-bef4d442ee1c",
   "metadata": {},
   "source": [
    "#### **QCloud + QIce for Domain 2, 29-Jan-2019, EDA09 Forcing:**  \n",
    "[corlasso_anim_qci_2019012900eda09d2_base.mp4](corlasso_anim_qci_2019012900eda09d2_base.mp4)  \n",
    "<video src=\"corlasso_anim_qci_2019012900eda09d2_base.mp4\" controls></video>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f165ca55-3906-46db-a7e8-818111637aeb",
   "metadata": {},
   "outputs": [
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       "Dimensions:               (Time: 10, south_north: 865, west_east: 750,\n",
       "                           bottom_top: 149)\n",
       "Coordinates:\n",
       "  * Time                  (Time) datetime64[ns] 80B 2019-01-29T06:00:00 ... 2...\n",
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       "    XTIME                 (Time) float32 40B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: south_north, west_east, bottom_top\n",
       "Data variables: (12/52)\n",
       "    ITIMESTEP             (Time) int32 40B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    MUTOT                 (Time, south_north, west_east) float32 26MB dask.array&lt;chunksize=(1, 865, 750), meta=np.ndarray&gt;\n",
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       "    HAMSL                 (Time, bottom_top, south_north, west_east) float32 4GB dask.array&lt;chunksize=(1, 50, 289, 250), meta=np.ndarray&gt;\n",
       "    P_HYD                 (Time, bottom_top, south_north, west_east) float32 4GB dask.array&lt;chunksize=(1, 50, 289, 250), meta=np.ndarray&gt;\n",
       "    PRESSURE              (Time, bottom_top, south_north, west_east) float32 4GB dask.array&lt;chunksize=(1, 50, 289, 250), meta=np.ndarray&gt;\n",
       "    ...                    ...\n",
       "    MULFC                 (Time, south_north, west_east) float32 26MB dask.array&lt;chunksize=(1, 865, 750), meta=np.ndarray&gt;\n",
       "    MULNB                 (Time, south_north, west_east) float32 26MB dask.array&lt;chunksize=(1, 865, 750), meta=np.ndarray&gt;\n",
       "    MULPL                 (Time, south_north, west_east) float32 26MB dask.array&lt;chunksize=(1, 865, 750), meta=np.ndarray&gt;\n",
       "    MUCAPE                (Time, south_north, west_east) float32 26MB dask.array&lt;chunksize=(1, 865, 750), meta=np.ndarray&gt;\n",
       "    MUCIN                 (Time, south_north, west_east) float32 26MB dask.array&lt;chunksize=(1, 865, 750), meta=np.ndarray&gt;\n",
       "    REFL_10CM_MAX         (Time, south_north, west_east) float32 26MB dask.array&lt;chunksize=(1, 865, 750), meta=np.ndarray&gt;\n",
       "Attributes: (12/39)\n",
       "    DX:                          500.0\n",
       "    DY:                          500.0\n",
       "    SIMULATION_START_DATE:       2019-01-29_06:00:00\n",
       "    WEST-EAST_GRID_DIMENSION:    751\n",
       "    SOUTH-NORTH_GRID_DIMENSION:  866\n",
       "    BOTTOM-TOP_GRID_DIMENSION:   150\n",
       "    ...                          ...\n",
       "    doi_isPartOf_lasso-cacti:    https://doi.org/10.5439/1905789\n",
       "    doi_isDocumentedBy:          https://doi.org/10.2172/1905845\n",
       "    doi_thisFileType:            https://doi.org/10.5439/1905819\n",
       "    history:                     processed by user d3m088 on machine cirrus89...\n",
       "    filename_user:               corlasso_met_2019012900eda09d3_base_M1.m1.20...\n",
       "    filename_storage:            corlassomet2019012900eda09d3baseM1.m1.201901...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9c2820de-dde2-4c1f-94d7-536b0899f982' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9c2820de-dde2-4c1f-94d7-536b0899f982' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Time</span>: 10</li><li><span>south_north</span>: 865</li><li><span>west_east</span>: 750</li><li><span>bottom_top</span>: 149</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-25a993eb-d154-4009-a84b-c8823674feb7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25a993eb-d154-4009-a84b-c8823674feb7' 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'>Time</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-01-29T06:00:00 ... 2019-01-...</div><input id='attrs-4b87c6a9-cfd8-4b1d-a61b-6aa7ff1a176c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b87c6a9-cfd8-4b1d-a61b-6aa7ff1a176c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72dd1fc0-8762-46b7-ab5c-e671f965c0f6' class='xr-var-data-in' type='checkbox'><label for='data-72dd1fc0-8762-46b7-ab5c-e671f965c0f6' title='Show/Hide data 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;2019-01-29T06:00:00.000000000&#x27;, &#x27;2019-01-29T06:15:00.000000000&#x27;,\n",
       "       &#x27;2019-01-29T06:30:00.000000000&#x27;, &#x27;2019-01-29T06:45:00.000000000&#x27;,\n",
       "       &#x27;2019-01-29T07:00:00.000000000&#x27;, &#x27;2019-01-29T07:15:00.000000000&#x27;,\n",
       "       &#x27;2019-01-29T07:30:00.000000000&#x27;, &#x27;2019-01-29T07:45:00.000000000&#x27;,\n",
       "       &#x27;2019-01-29T08:00:00.000000000&#x27;, &#x27;2019-01-29T08:15:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLONG</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'>dask.array&lt;chunksize=(865, 750), meta=np.ndarray&gt;</div><input id='attrs-a2ac2ff1-2422-4e4e-83c6-ec577c379147' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2ac2ff1-2422-4e4e-83c6-ec577c379147' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66ae6a2c-8829-4a30-9760-d6e0b726253d' class='xr-var-data-in' type='checkbox'><label for='data-66ae6a2c-8829-4a30-9760-d6e0b726253d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.47 MiB </td>\n",
       "                        <td> 2.47 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (865, 750) </td>\n",
       "                        <td> (865, 750) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 45 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"154\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLAT</span></div><div class='xr-var-dims'>(south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(865, 750), meta=np.ndarray&gt;</div><input id='attrs-b5334fcb-0a26-4d43-bbfa-0b57a3d637b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5334fcb-0a26-4d43-bbfa-0b57a3d637b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e56bc8e1-3ec9-47ef-a5f3-7a7b16bb8324' class='xr-var-data-in' type='checkbox'><label for='data-e56bc8e1-3ec9-47ef-a5f3-7a7b16bb8324' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.47 MiB </td>\n",
       "                        <td> 2.47 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (865, 750) </td>\n",
       "                        <td> (865, 750) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 45 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XTIME</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-74fa5f4f-0568-4a8a-b1b0-1bb983a14ca2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74fa5f4f-0568-4a8a-b1b0-1bb983a14ca2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a3225a8-56e9-4705-ad92-b8b5863a5d27' class='xr-var-data-in' type='checkbox'><label for='data-5a3225a8-56e9-4705-ad92-b8b5863a5d27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40 B </td>\n",
       "                        <td> 4 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (10,) </td>\n",
       "                        <td> (1,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 10 chunks in 21 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"38\" style=\"stroke-width:2\" />\n",
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       "  <text x=\"140.000000\" y=\"19.298432\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,19.298432)\">1</text>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e83b6584-a38f-4af8-8bde-6e8cf5ecb7be' class='xr-section-summary-in' type='checkbox'  ><label for='section-e83b6584-a38f-4af8-8bde-6e8cf5ecb7be' class='xr-section-summary' >Data variables: <span>(52)</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>ITIMESTEP</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-b68869fb-bca2-4cc1-9e77-63dd43e6f2da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b68869fb-bca2-4cc1-9e77-63dd43e6f2da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31738e12-0919-402b-be8a-9df114653424' class='xr-var-data-in' type='checkbox'><label for='data-31738e12-0919-402b-be8a-9df114653424' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>WRF timestep number since run start</dd><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 40 B </td>\n",
       "                        <td> 4 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (10,) </td>\n",
       "                        <td> (1,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 10 chunks in 21 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MUTOT</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'>dask.array&lt;chunksize=(1, 865, 750), meta=np.ndarray&gt;</div><input id='attrs-388eb4a5-b692-418d-9482-ec423bc0188d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-388eb4a5-b692-418d-9482-ec423bc0188d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41ee1725-b394-4ba0-9c54-665d1be76db3' class='xr-var-data-in' type='checkbox'><label for='data-41ee1725-b394-4ba0-9c54-665d1be76db3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>dry air mass in column</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 24.75 MiB </td>\n",
       "                        <td> 2.47 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (10, 865, 750) </td>\n",
       "                        <td> (1, 865, 750) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 10 chunks in 21 graph layers </td>\n",
       "                    </tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RH</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'>dask.array&lt;chunksize=(1, 50, 289, 250), meta=np.ndarray&gt;</div><input id='attrs-336cc84e-ac18-4494-a1ed-7e21ff4005a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-336cc84e-ac18-4494-a1ed-7e21ff4005a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebdd6884-e533-4f3b-8e15-0516276525f0' class='xr-var-data-in' type='checkbox'><label for='data-ebdd6884-e533-4f3b-8e15-0516276525f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>relative humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UMET</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'>dask.array&lt;chunksize=(1, 50, 289, 250), meta=np.ndarray&gt;</div><input id='attrs-99b68eed-21b5-4519-b89f-7c8dcc682df9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99b68eed-21b5-4519-b89f-7c8dcc682df9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ec6751c-0053-4712-9037-4358b4529b25' class='xr-var-data-in' type='checkbox'><label for='data-4ec6751c-0053-4712-9037-4358b4529b25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative u-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <th> Shape </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VMET</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'>dask.array&lt;chunksize=(1, 50, 289, 250), meta=np.ndarray&gt;</div><input id='attrs-4ceb64f8-ae76-4cb9-be5a-dd32e38f1b56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ceb64f8-ae76-4cb9-be5a-dd32e38f1b56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24384b80-1079-42b2-9c32-26d33cf0e782' class='xr-var-data-in' type='checkbox'><label for='data-24384b80-1079-42b2-9c32-26d33cf0e782' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>earth-relative v-wind component</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>stagger :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 24.75 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "        <td>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 24.75 MiB </td>\n",
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       "                    \n",
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       "                        <th> Shape </th>\n",
       "                        <td> (10, 865, 750) </td>\n",
       "                        <td> (1, 865, 750) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <th> Bytes </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 2.47 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1, 865, 750) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a3a9459-9616-4785-8d87-016e0913a54a' class='xr-section-summary-in' type='checkbox'  ><label for='section-9a3a9459-9616-4785-8d87-016e0913a54a' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d5323fa9-db63-4434-864d-f71f7664d76f' class='xr-index-data-in' type='checkbox'/><label for='index-d5323fa9-db63-4434-864d-f71f7664d76f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-01-29 06:00:00&#x27;, &#x27;2019-01-29 06:15:00&#x27;,\n",
       "               &#x27;2019-01-29 06:30:00&#x27;, &#x27;2019-01-29 06:45:00&#x27;,\n",
       "               &#x27;2019-01-29 07:00:00&#x27;, &#x27;2019-01-29 07:15:00&#x27;,\n",
       "               &#x27;2019-01-29 07:30:00&#x27;, &#x27;2019-01-29 07:45:00&#x27;,\n",
       "               &#x27;2019-01-29 08:00:00&#x27;, &#x27;2019-01-29 08:15:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-33c3d6a8-a227-499e-b36e-b4b359011705' class='xr-section-summary-in' type='checkbox'  ><label for='section-33c3d6a8-a227-499e-b36e-b4b359011705' class='xr-section-summary' >Attributes: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>DX :</span></dt><dd>500.0</dd><dt><span>DY :</span></dt><dd>500.0</dd><dt><span>SIMULATION_START_DATE :</span></dt><dd>2019-01-29_06:00:00</dd><dt><span>WEST-EAST_GRID_DIMENSION :</span></dt><dd>751</dd><dt><span>SOUTH-NORTH_GRID_DIMENSION :</span></dt><dd>866</dd><dt><span>BOTTOM-TOP_GRID_DIMENSION :</span></dt><dd>150</dd><dt><span>GRIDTYPE :</span></dt><dd>C</dd><dt><span>CEN_LAT :</span></dt><dd>-32.01111</dd><dt><span>CEN_LON :</span></dt><dd>-64.23425</dd><dt><span>TRUELAT1 :</span></dt><dd>-30.0</dd><dt><span>TRUELAT2 :</span></dt><dd>-60.0</dd><dt><span>MOAD_CEN_LAT :</span></dt><dd>-32.500015</dd><dt><span>STAND_LON :</span></dt><dd>-64.728</dd><dt><span>POLE_LAT :</span></dt><dd>90.0</dd><dt><span>POLE_LON :</span></dt><dd>0.0</dd><dt><span>MAP_PROJ :</span></dt><dd>1</dd><dt><span>MAP_PROJ_CHAR :</span></dt><dd>Lambert Conformal</dd><dt><span>run_name :</span></dt><dd>eda09_2019012900_base</dd><dt><span>model_type :</span></dt><dd>WRF</dd><dt><span>model_version :</span></dt><dd>NCAR V4.3.1 + LASSO modifications</dd><dt><span>model_source_repository :</span></dt><dd>https://code.arm.gov/lasso/lasso-cacti/lasso-wrf-cacti/</dd><dt><span>model_source_git_hash :</span></dt><dd>3e761eac</dd><dt><span>subset_source_repository :</span></dt><dd>https://code.arm.gov/lasso/lasso-cacti/subsetwrf</dd><dt><span>subset_source_git_hash :</span></dt><dd>99902f2</dd><dt><span>domain_num :</span></dt><dd>3</dd><dt><span>input_source :</span></dt><dd>/gpfs/wolf/cli120/world-shared/d3m088/cacti/staged_runs/20190129/eda09/base/les/out_d3</dd><dt><span>site_id :</span></dt><dd>cor</dd><dt><span>platform_id :</span></dt><dd>corlasso</dd><dt><span>facility_designation :</span></dt><dd>M1</dd><dt><span>location_description :</span></dt><dd>Cloud, Aerosol, and Complex Terrain Interactions (CACTI), Cordoba, Argentina</dd><dt><span>data_level :</span></dt><dd>m1</dd><dt><span>datastream :</span></dt><dd>corlasso_sub_met</dd><dt><span>contact :</span></dt><dd>lasso@arm.gov, LASSO PI: William Gustafson (PNNL, william.gustafson@pnnl.gov), LASSO Co-PI: Andrew Vogelmann (BNL)</dd><dt><span>doi_isPartOf_lasso-cacti :</span></dt><dd>https://doi.org/10.5439/1905789</dd><dt><span>doi_isDocumentedBy :</span></dt><dd>https://doi.org/10.2172/1905845</dd><dt><span>doi_thisFileType :</span></dt><dd>https://doi.org/10.5439/1905819</dd><dt><span>history :</span></dt><dd>processed by user d3m088 on machine cirrus89.ccs.ornl.gov at 23-Jan-2023 06:50 using subsetwrf.py</dd><dt><span>filename_user :</span></dt><dd>corlasso_met_2019012900eda09d3_base_M1.m1.20190129.060000.nc</dd><dt><span>filename_storage :</span></dt><dd>corlassomet2019012900eda09d3baseM1.m1.20190129.060000.nc</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 59GB\n",
       "Dimensions:               (Time: 10, south_north: 865, west_east: 750,\n",
       "                           bottom_top: 149)\n",
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       "  * Time                  (Time) datetime64[ns] 80B 2019-01-29T06:00:00 ... 2...\n",
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       "    PRESSURE              (Time, bottom_top, south_north, west_east) float32 4GB dask.array<chunksize=(1, 50, 289, 250), meta=np.ndarray>\n",
       "    ...                    ...\n",
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       "    MULPL                 (Time, south_north, west_east) float32 26MB dask.array<chunksize=(1, 865, 750), meta=np.ndarray>\n",
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       "    MUCIN                 (Time, south_north, west_east) float32 26MB dask.array<chunksize=(1, 865, 750), meta=np.ndarray>\n",
       "    REFL_10CM_MAX         (Time, south_north, west_east) float32 26MB dask.array<chunksize=(1, 865, 750), meta=np.ndarray>\n",
       "Attributes: (12/39)\n",
       "    DX:                          500.0\n",
       "    DY:                          500.0\n",
       "    SIMULATION_START_DATE:       2019-01-29_06:00:00\n",
       "    WEST-EAST_GRID_DIMENSION:    751\n",
       "    SOUTH-NORTH_GRID_DIMENSION:  866\n",
       "    BOTTOM-TOP_GRID_DIMENSION:   150\n",
       "    ...                          ...\n",
       "    doi_isPartOf_lasso-cacti:    https://doi.org/10.5439/1905789\n",
       "    doi_isDocumentedBy:          https://doi.org/10.2172/1905845\n",
       "    doi_thisFileType:            https://doi.org/10.5439/1905819\n",
       "    history:                     processed by user d3m088 on machine cirrus89...\n",
       "    filename_user:               corlasso_met_2019012900eda09d3_base_M1.m1.20...\n",
       "    filename_storage:            corlassomet2019012900eda09d3baseM1.m1.201901..."
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7d66bf97-8735-4c97-b165-99b99a39a090",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 39GB\n",
      "Dimensions:      (Time: 10, bottom_top: 149, south_north: 865, west_east: 750)\n",
      "Coordinates:\n",
      "  * Time         (Time) datetime64[ns] 80B 2019-01-29T06:00:00 ... 2019-01-29...\n",
      "    XLONG        (south_north, west_east) float32 3MB dask.array<chunksize=(865, 750), meta=np.ndarray>\n",
      "    XLAT         (south_north, west_east) float32 3MB dask.array<chunksize=(865, 750), meta=np.ndarray>\n",
      "    XTIME        (Time) float32 40B dask.array<chunksize=(1,), meta=np.ndarray>\n",
      "Dimensions without coordinates: bottom_top, south_north, west_east\n",
      "Data variables: (12/14)\n",
      "    ITIMESTEP    (Time) int32 40B dask.array<chunksize=(1,), meta=np.ndarray>\n",
      "    QCLOUD       (Time, bottom_top, south_north, west_east) float32 4GB dask.array<chunksize=(1, 50, 289, 250), meta=np.ndarray>\n",
      "    QRAIN        (Time, bottom_top, south_north, west_east) float32 4GB dask.array<chunksize=(1, 50, 289, 250), meta=np.ndarray>\n",
      "    QICE         (Time, bottom_top, south_north, west_east) float32 4GB dask.array<chunksize=(1, 50, 289, 250), meta=np.ndarray>\n",
      "    QSNOW        (Time, bottom_top, south_north, west_east) float32 4GB dask.array<chunksize=(1, 50, 289, 250), meta=np.ndarray>\n",
      "    QGRAUP       (Time, bottom_top, south_north, west_east) float32 4GB dask.array<chunksize=(1, 50, 289, 250), meta=np.ndarray>\n",
      "    ...           ...\n",
      "    REFL_10CM    (Time, bottom_top, south_north, west_east) float32 4GB dask.array<chunksize=(1, 50, 289, 250), meta=np.ndarray>\n",
      "    CLDFRA       (Time, bottom_top, south_north, west_east) float32 4GB dask.array<chunksize=(1, 50, 289, 250), meta=np.ndarray>\n",
      "    LWP          (Time, south_north, west_east) float32 26MB dask.array<chunksize=(1, 865, 750), meta=np.ndarray>\n",
      "    IWP          (Time, south_north, west_east) float32 26MB dask.array<chunksize=(1, 865, 750), meta=np.ndarray>\n",
      "    PRECIPWATER  (Time, south_north, west_east) float32 26MB dask.array<chunksize=(1, 865, 750), meta=np.ndarray>\n",
      "    QNCLOUD      (Time, bottom_top, south_north, west_east) float32 4GB dask.array<chunksize=(1, 50, 289, 250), meta=np.ndarray>\n",
      "Attributes: (12/39)\n",
      "    DX:                          500.0\n",
      "    DY:                          500.0\n",
      "    SIMULATION_START_DATE:       2019-01-29_06:00:00\n",
      "    WEST-EAST_GRID_DIMENSION:    751\n",
      "    SOUTH-NORTH_GRID_DIMENSION:  866\n",
      "    BOTTOM-TOP_GRID_DIMENSION:   150\n",
      "    ...                          ...\n",
      "    doi_isPartOf_lasso-cacti:    https://doi.org/10.5439/1905789\n",
      "    doi_isDocumentedBy:          https://doi.org/10.2172/1905845\n",
      "    doi_thisFileType:            https://doi.org/10.5439/1905813\n",
      "    history:                     processed by user d3m088 on machine cirrus74...\n",
      "    filename_user:               corlasso_cld_2019012900eda09d3_base_M1.m1.20...\n",
      "    filename_storage:            corlassocld2019012900eda09d3baseM1.m1.201901...\n"
     ]
    }
   ],
   "source": [
    "print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "fc3ed2d6-fc07-46f9-a76d-50a98a53425f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(865, 750)\n",
      "(10, 149, 865, 750)\n",
      "(10, 149, 865, 750)\n"
     ]
    }
   ],
   "source": [
    "print(theta.shape)\n",
    "print(ds.THETA_E.shape)\n",
    "print(ds2.QCLOUD.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "63b646a6-d517-45fb-bdf0-6f3009cfb658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(750,)\n",
      "(149, 750)\n",
      "(10, 149, 750)\n",
      "(10, 149, 750)\n"
     ]
    }
   ],
   "source": [
    "print(lonslice.values.shape)\n",
    "print(hamsl_raw.values.shape)\n",
    "print(da_u.values.shape) \n",
    "print(da_v.values.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1d471f6-1fb9-44cd-bea9-287f591b9a84",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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