{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Precipitation as Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will test how different ICON files can be combined together. This is done with `2d_cloud` files and a total (domain-average) precipitation time series will be plotted at the end. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Python Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# system libs\n",
    "import os, sys, glob\n",
    "import datetime\n",
    "\n",
    "# array operators and netcdf datasets\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "xr.set_options(keep_attrs=True)\n",
    "\n",
    "# plotting\n",
    "import pylab as plt\n",
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "\n",
    "import matplotlib.dates as mdates\n",
    "myFmt = mdates.DateFormatter('%H:%M')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open Data with `xarray`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "exercise_path = '/work/bb1224/2024_MS-COURSE'\n",
    "data_path = f'{exercise_path}/data'\n",
    "icon_data_path = f'{data_path}/icon-lem'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is very similar to the tasks already done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bc-init', 'grids-extpar', 'experiments']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(icon_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we select the `experiment`folder in which pre-calculated experiments are stored. **Later, you need to change this directory to your ICON output directory!** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_name = 'icon_lam_1dom_lpz-base'\n",
    "exp_path = f'{icon_data_path}/experiments/{exp_name}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Base Files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dset = xr.open_mfdataset( f'{exp_path}/2d_cloud_DOM01_ML_20210516T*Z.nc', chunks={'time':1}, combine='by_coords' ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the content of the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "Dimensions:        (time: 288, plev: 1, bnds: 2, plev_2: 1, plev_3: 1,\n",
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       "  * plev_3         (plev_3) float64 800.0\n",
       "Dimensions without coordinates: bnds, ncells\n",
       "Data variables: (12/17)\n",
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       "    plev_2_bnds    (time, plev_2, bnds) float64 dask.array&lt;chunksize=(12, 1, 2), meta=np.ndarray&gt;\n",
       "    plev_3_bnds    (time, plev_3, bnds) float64 dask.array&lt;chunksize=(12, 1, 2), meta=np.ndarray&gt;\n",
       "    tqv_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;\n",
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       "    tqi_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;\n",
       "    ...             ...\n",
       "    cape           (time, ncells) float32 dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;\n",
       "    cape_ml        (time, ncells) float32 dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;\n",
       "    cin_ml         (time, ncells) float32 dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;\n",
       "    rain_con_rate  (time, ncells) float32 dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;\n",
       "    prec_con       (time, ncells) float32 dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;\n",
       "    prec_gsp       (time, ncells) float32 dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    CDI:                  Climate Data Interface version 2.2.4 (https://mpime...\n",
       "    Conventions:          CF-1.6\n",
       "    number_of_grid_used:  99\n",
       "    uuidOfHGrid:          718d4bb6-2c67-4c84-a5ab-440d28412a00\n",
       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
       "    title:                ICON simulation\n",
       "    source:               https://gitlab.dkrz.de/icon/icon-model.git@bbcca057...\n",
       "    history:              /home/k/k206107/workspace/icon-build/bin/icon at 20...\n",
       "    references:           see MPIM/DWD publications\n",
       "    comment:              v107 Workshop (k206107) on l10647 (Linux 4.18.0-425...</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-b82be572-1f5f-4729-8f7d-d92b173e32be' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b82be572-1f5f-4729-8f7d-d92b173e32be' 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>: 288</li><li><span class='xr-has-index'>plev</span>: 1</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>plev_2</span>: 1</li><li><span class='xr-has-index'>plev_3</span>: 1</li><li><span>ncells</span>: 10212</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8ffb5d37-e094-4fda-b2dc-13a2b0a1ad92' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8ffb5d37-e094-4fda-b2dc-13a2b0a1ad92' 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'>float64</div><div class='xr-var-preview xr-preview'>2.021e+07 2.021e+07 ... 2.021e+07</div><input id='attrs-d82c0627-356f-4ca6-8ee2-0a22d57f9428' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d82c0627-356f-4ca6-8ee2-0a22d57f9428' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-085aa1d8-8f3c-4465-9356-6a761747e1b1' class='xr-var-data-in' type='checkbox'><label for='data-085aa1d8-8f3c-4465-9356-6a761747e1b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>calendar :</span></dt><dd>proleptic_gregorian</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>units :</span></dt><dd>day as %Y%m%d.%f</dd></dl></div><div class='xr-var-data'><pre>array([20210516.      , 20210516.003472, 20210516.006944, ..., 20210516.989583,\n",
       "       20210516.993056, 20210516.996528])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-fc4df944-6543-4b18-9905-171f14726b1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc4df944-6543-4b18-9905-171f14726b1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dbbb8d6-e57d-404d-864f-b7239f96484d' class='xr-var-data-in' type='checkbox'><label for='data-5dbbb8d6-e57d-404d-864f-b7239f96484d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>bounds :</span></dt><dd>plev_bnds</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev_2</span></div><div class='xr-var-dims'>(plev_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>400.0</div><input id='attrs-3ad7dc44-5dd4-4225-b016-53c2b715b759' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ad7dc44-5dd4-4225-b016-53c2b715b759' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48e8d0f3-484d-4179-9ac8-7fb02458a1f5' class='xr-var-data-in' type='checkbox'><label for='data-48e8d0f3-484d-4179-9ac8-7fb02458a1f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>bounds :</span></dt><dd>plev_2_bnds</dd></dl></div><div class='xr-var-data'><pre>array([400.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev_3</span></div><div class='xr-var-dims'>(plev_3)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>800.0</div><input id='attrs-a762d96a-3c4a-49e5-b156-448a741d66fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a762d96a-3c4a-49e5-b156-448a741d66fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b13b9321-b200-43fb-a4d2-cda3649482d8' class='xr-var-data-in' type='checkbox'><label for='data-b13b9321-b200-43fb-a4d2-cda3649482d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>bounds :</span></dt><dd>plev_3_bnds</dd></dl></div><div class='xr-var-data'><pre>array([800.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b7374ab-405e-4409-91a2-48838f00213a' class='xr-section-summary-in' type='checkbox'  ><label for='section-9b7374ab-405e-4409-91a2-48838f00213a' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>plev_bnds</span></div><div class='xr-var-dims'>(time, plev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1, 2), meta=np.ndarray&gt;</div><input id='attrs-d8204bdd-f587-46cb-9fe7-fdee99504f96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8204bdd-f587-46cb-9fe7-fdee99504f96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1311e74b-2e0b-4900-926f-dfb99e9a6341' class='xr-var-data-in' type='checkbox'><label for='data-1311e74b-2e0b-4900-926f-dfb99e9a6341' title='Show/Hide data 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> 4.50 kiB </td>\n",
       "                        <td> 192 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (288, 1, 2) </td>\n",
       "                        <td> (12, 1, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 24 chunks in 73 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev_2_bnds</span></div><div class='xr-var-dims'>(time, plev_2, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1, 2), meta=np.ndarray&gt;</div><input id='attrs-9abb48a1-7bcf-4f77-9f2c-8dfacd2ceb7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9abb48a1-7bcf-4f77-9f2c-8dfacd2ceb7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-677139a0-2a95-4269-b5f7-837300d5988c' class='xr-var-data-in' type='checkbox'><label for='data-677139a0-2a95-4269-b5f7-837300d5988c' title='Show/Hide data 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",
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       "                    \n",
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       "                        <th> Bytes </th>\n",
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       "                        <td> 192 B </td>\n",
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       "                    <tr>\n",
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       "                        <td> (12, 1, 2) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;</div><input id='attrs-a3997c40-7e5f-4d59-9a21-d70bf4a416da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3997c40-7e5f-4d59-9a21-d70bf4a416da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5833217-64b3-4cf3-96e0-4b27a2b6a14f' class='xr-var-data-in' type='checkbox'><label for='data-c5833217-64b3-4cf3-96e0-4b27a2b6a14f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</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> 11.22 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (1, 10212) </td>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;</div><input id='attrs-4f8eae53-01bc-4a24-be69-3da4b15456db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f8eae53-01bc-4a24-be69-3da4b15456db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1b02f56-bad9-4779-8e92-4743d279769f' class='xr-var-data-in' type='checkbox'><label for='data-e1b02f56-bad9-4779-8e92-4743d279769f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</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>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;</div><input id='attrs-63673f56-a017-4abe-9415-2013a847e97b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63673f56-a017-4abe-9415-2013a847e97b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d745951e-e1db-4742-991a-949ea4b44ef7' class='xr-var-data-in' type='checkbox'><label for='data-d745951e-e1db-4742-991a-949ea4b44ef7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.22 MiB </td>\n",
       "                        <td> 39.89 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (288, 10212) </td>\n",
       "                        <td> (1, 10212) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 288 chunks in 49 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;</div><input id='attrs-fc7f3296-58a2-4c76-8255-83a45db0d038' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc7f3296-58a2-4c76-8255-83a45db0d038' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1ee788a-18c8-4bab-9d0c-2f7a36433398' class='xr-var-data-in' type='checkbox'><label for='data-b1ee788a-18c8-4bab-9d0c-2f7a36433398' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.22 MiB </td>\n",
       "                        <td> 39.89 kiB </td>\n",
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       "                        <td> (1, 10212) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;</div><input id='attrs-5f36d1f7-6847-485f-90cf-17fdbe2add61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f36d1f7-6847-485f-90cf-17fdbe2add61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32056408-8579-4f23-b631-19d1def68a6f' class='xr-var-data-in' type='checkbox'><label for='data-32056408-8579-4f23-b631-19d1def68a6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>rain_gsp_rate</dd><dt><span>long_name :</span></dt><dd>gridscale rain rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>param :</span></dt><dd>77.1.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.22 MiB </td>\n",
       "                        <td> 39.89 kiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (1, 10212) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clcl</span></div><div class='xr-var-dims'>(time, plev_3, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 10212), meta=np.ndarray&gt;</div><input id='attrs-b0d97b49-28a7-478c-a96c-88ecd7cdcf0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0d97b49-28a7-478c-a96c-88ecd7cdcf0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-205d7177-186a-49d5-b2d2-dd525d007650' class='xr-var-data-in' type='checkbox'><label for='data-205d7177-186a-49d5-b2d2-dd525d007650' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>clcl</dd><dt><span>long_name :</span></dt><dd>low level clouds</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>param :</span></dt><dd>22.6.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cape</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;</div><input id='attrs-c003a866-7e75-487b-a0af-c1732a62796e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c003a866-7e75-487b-a0af-c1732a62796e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-174533a8-3b34-4f3f-8d64-060ed4851571' class='xr-var-data-in' type='checkbox'><label for='data-174533a8-3b34-4f3f-8d64-060ed4851571' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>cape</dd><dt><span>long_name :</span></dt><dd>conv avail pot energy</dd><dt><span>units :</span></dt><dd>J kg-1</dd><dt><span>param :</span></dt><dd>6.7.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.22 MiB </td>\n",
       "                        <td> 39.89 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (288, 10212) </td>\n",
       "                        <td> (1, 10212) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 288 chunks in 49 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cape_ml</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;</div><input id='attrs-8fbaf8ba-7a45-4b44-926b-5ff7d40a78ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fbaf8ba-7a45-4b44-926b-5ff7d40a78ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-986de252-580c-4607-9b45-e1bd2787bdb3' class='xr-var-data-in' type='checkbox'><label for='data-986de252-580c-4607-9b45-e1bd2787bdb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>cape_ml</dd><dt><span>long_name :</span></dt><dd>cape of mean surface layer parcel</dd><dt><span>units :</span></dt><dd>J kg-1</dd><dt><span>param :</span></dt><dd>6.7.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.22 MiB </td>\n",
       "                        <td> 39.89 kiB </td>\n",
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       "                        <td> (288, 10212) </td>\n",
       "                        <td> (1, 10212) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cin_ml</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;</div><input id='attrs-c769d536-468b-433b-8b5a-7341d07c29d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c769d536-468b-433b-8b5a-7341d07c29d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2e2f879-588f-4ac6-86fa-4b8fa13dd85e' class='xr-var-data-in' type='checkbox'><label for='data-b2e2f879-588f-4ac6-86fa-4b8fa13dd85e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>cin_ml</dd><dt><span>long_name :</span></dt><dd>convective inhibition of mean surface layer parcel</dd><dt><span>units :</span></dt><dd>J kg-1</dd><dt><span>param :</span></dt><dd>7.7.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.22 MiB </td>\n",
       "                        <td> 39.89 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (288, 10212) </td>\n",
       "                        <td> (1, 10212) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 288 chunks in 49 graph layers </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;</div><input id='attrs-75f07d72-4e03-4171-a403-8855e7c4f6c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75f07d72-4e03-4171-a403-8855e7c4f6c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7caa6e6d-ee35-44c1-86ad-af4a51d0bb84' class='xr-var-data-in' type='checkbox'><label for='data-7caa6e6d-ee35-44c1-86ad-af4a51d0bb84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>rain_con_rate</dd><dt><span>long_name :</span></dt><dd>convective rain rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>param :</span></dt><dd>76.1.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</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> 11.22 MiB </td>\n",
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       "                        <td> (1, 10212) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prec_con</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;</div><input id='attrs-a0cc0212-13e4-4e84-9868-4ff9e0b449f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0cc0212-13e4-4e84-9868-4ff9e0b449f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37af7f02-2ca2-4279-b182-96b8e2d03059' class='xr-var-data-in' type='checkbox'><label for='data-37af7f02-2ca2-4279-b182-96b8e2d03059' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>prec_con</dd><dt><span>long_name :</span></dt><dd>convective precip</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>param :</span></dt><dd>37.1.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.22 MiB </td>\n",
       "                        <td> 39.89 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (288, 10212) </td>\n",
       "                        <td> (1, 10212) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 288 chunks in 49 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prec_gsp</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10212), meta=np.ndarray&gt;</div><input id='attrs-affe5b9a-33fd-4ca2-9f55-2e42d0f8113c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-affe5b9a-33fd-4ca2-9f55-2e42d0f8113c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b37cf43-6b2b-4d52-973d-579f0a8e4141' class='xr-var-data-in' type='checkbox'><label for='data-0b37cf43-6b2b-4d52-973d-579f0a8e4141' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>prec_gsp</dd><dt><span>long_name :</span></dt><dd>gridscale precip</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>param :</span></dt><dd>54.1.0</dd><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.22 MiB </td>\n",
       "                        <td> 39.89 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (288, 10212) </td>\n",
       "                        <td> (1, 10212) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 288 chunks in 49 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-be0775d1-e98f-42b6-8ecb-849b78f39598' class='xr-section-summary-in' type='checkbox'  ><label for='section-be0775d1-e98f-42b6-8ecb-849b78f39598' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7b214ef0-6921-4f0d-897f-f3cb2708555c' class='xr-index-data-in' type='checkbox'/><label for='index-7b214ef0-6921-4f0d-897f-f3cb2708555c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([        20210516.0, 20210516.003472224, 20210516.006944444,\n",
       "       20210516.010416668, 20210516.013888888, 20210516.017361112,\n",
       "       20210516.020833332, 20210516.024305556, 20210516.027777776,\n",
       "           20210516.03125,\n",
       "       ...\n",
       "       20210516.965277776,     20210516.96875, 20210516.972222224,\n",
       "       20210516.975694444, 20210516.979166668, 20210516.982638888,\n",
       "       20210516.986111112, 20210516.989583332, 20210516.993055556,\n",
       "       20210516.996527776],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>plev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-93ef50c9-85b1-4322-944e-95b9cb10d060' class='xr-index-data-in' type='checkbox'/><label for='index-93ef50c9-85b1-4322-944e-95b9cb10d060' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0], dtype=&#x27;float64&#x27;, name=&#x27;plev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>plev_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bcdfc98d-414c-46e0-80ba-f7ae2b9103a8' class='xr-index-data-in' type='checkbox'/><label for='index-bcdfc98d-414c-46e0-80ba-f7ae2b9103a8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([400.0], dtype=&#x27;float64&#x27;, name=&#x27;plev_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>plev_3</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ee9024ff-0226-4788-8140-5c1a6bc9b3af' class='xr-index-data-in' type='checkbox'/><label for='index-ee9024ff-0226-4788-8140-5c1a6bc9b3af' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([800.0], dtype=&#x27;float64&#x27;, name=&#x27;plev_3&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bfd77d09-3340-4609-b971-82dd6c50125a' class='xr-section-summary-in' type='checkbox'  ><label for='section-bfd77d09-3340-4609-b971-82dd6c50125a' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.2.4 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>number_of_grid_used :</span></dt><dd>99</dd><dt><span>uuidOfHGrid :</span></dt><dd>718d4bb6-2c67-4c84-a5ab-440d28412a00</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>source :</span></dt><dd>https://gitlab.dkrz.de/icon/icon-model.git@bbcca057a7ff3fe47b0a2ecf185d498e6948bd97</dd><dt><span>history :</span></dt><dd>/home/k/k206107/workspace/icon-build/bin/icon at 20240513 134355</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>comment :</span></dt><dd>v107 Workshop (k206107) on l10647 (Linux 4.18.0-425.10.1.el8_7.x86_64 x86_64)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:        (time: 288, plev: 1, bnds: 2, plev_2: 1, plev_3: 1,\n",
       "                    ncells: 10212)\n",
       "Coordinates:\n",
       "  * time           (time) float64 2.021e+07 2.021e+07 ... 2.021e+07 2.021e+07\n",
       "  * plev           (plev) float64 0.0\n",
       "  * plev_2         (plev_2) float64 400.0\n",
       "  * plev_3         (plev_3) float64 800.0\n",
       "Dimensions without coordinates: bnds, ncells\n",
       "Data variables: (12/17)\n",
       "    plev_bnds      (time, plev, bnds) float64 dask.array<chunksize=(12, 1, 2), meta=np.ndarray>\n",
       "    plev_2_bnds    (time, plev_2, bnds) float64 dask.array<chunksize=(12, 1, 2), meta=np.ndarray>\n",
       "    plev_3_bnds    (time, plev_3, bnds) float64 dask.array<chunksize=(12, 1, 2), meta=np.ndarray>\n",
       "    tqv_dia        (time, ncells) float32 dask.array<chunksize=(1, 10212), meta=np.ndarray>\n",
       "    tqc_dia        (time, ncells) float32 dask.array<chunksize=(1, 10212), meta=np.ndarray>\n",
       "    tqi_dia        (time, ncells) float32 dask.array<chunksize=(1, 10212), meta=np.ndarray>\n",
       "    ...             ...\n",
       "    cape           (time, ncells) float32 dask.array<chunksize=(1, 10212), meta=np.ndarray>\n",
       "    cape_ml        (time, ncells) float32 dask.array<chunksize=(1, 10212), meta=np.ndarray>\n",
       "    cin_ml         (time, ncells) float32 dask.array<chunksize=(1, 10212), meta=np.ndarray>\n",
       "    rain_con_rate  (time, ncells) float32 dask.array<chunksize=(1, 10212), meta=np.ndarray>\n",
       "    prec_con       (time, ncells) float32 dask.array<chunksize=(1, 10212), meta=np.ndarray>\n",
       "    prec_gsp       (time, ncells) float32 dask.array<chunksize=(1, 10212), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CDI:                  Climate Data Interface version 2.2.4 (https://mpime...\n",
       "    Conventions:          CF-1.6\n",
       "    number_of_grid_used:  99\n",
       "    uuidOfHGrid:          718d4bb6-2c67-4c84-a5ab-440d28412a00\n",
       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
       "    title:                ICON simulation\n",
       "    source:               https://gitlab.dkrz.de/icon/icon-model.git@bbcca057...\n",
       "    history:              /home/k/k206107/workspace/icon-build/bin/icon at 20...\n",
       "    references:           see MPIM/DWD publications\n",
       "    comment:              v107 Workshop (k206107) on l10647 (Linux 4.18.0-425..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the time dimension has a length of `288`, i.e. a full day with output each 5 minutes is accumulated. The full data are not loaded into memory so far. We use the capabilities of the `dask` module to make out-of-memory computations ... very convenient! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Grid-Scale and Sub-Gridscale Rain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain = dset['rain_con_rate'] + dset['rain_gsp_rate']\n",
    "rain.attrs['long_name'] = 'rain rate'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO for later: \n",
    "* Is it OK to neglect graupel / snow & hail?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Average Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain_mean = rain.mean( 'ncells' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is still out-of-memory... But, now we do the calculations!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain_mean = rain_mean.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple (naive) Plotting "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting is now not complicated. We just use the capabilities of `xarray`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure( figsize = (14,6))\n",
    "rain_mean.plot( lw = 3 )\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you understand this figure? Hmm, time series plotting is perhaps not so simple...\n",
    "\n",
    "We need to do two things:\n",
    "* convert the rain unit into mm per day\n",
    "* get an understandable time axis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unit Conversion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, we see the unit kg m-2 s-1. A day has 3600*24 seconds. One liter of water weighs one kilogram. This liter of water put on one square meter has a column height of 1 mm. OK, so far?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "conversion_factor = 3600 * 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain_mean = conversion_factor * rain_mean\n",
    "rain_mean.attrs['units'] = 'mm day-1'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Axis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 288)&gt;\n",
       "array([20210516.      , 20210516.003472, 20210516.006944, ..., 20210516.989583,\n",
       "       20210516.993056, 20210516.996528])\n",
       "Coordinates:\n",
       "  * time     (time) float64 2.021e+07 2.021e+07 ... 2.021e+07 2.021e+07\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    calendar:       proleptic_gregorian\n",
       "    axis:           T\n",
       "    units:          day as %Y%m%d.%f</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4ec4f444-1f13-47a5-ab51-534458f97970' class='xr-array-in' type='checkbox' checked><label for='section-4ec4f444-1f13-47a5-ab51-534458f97970' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.021e+07 2.021e+07 2.021e+07 ... 2.021e+07 2.021e+07 2.021e+07</span></div><div class='xr-array-data'><pre>array([20210516.      , 20210516.003472, 20210516.006944, ..., 20210516.989583,\n",
       "       20210516.993056, 20210516.996528])</pre></div></div></li><li class='xr-section-item'><input id='section-45418614-6c1e-45b4-afc3-b7440501e492' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45418614-6c1e-45b4-afc3-b7440501e492' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.021e+07 2.021e+07 ... 2.021e+07</div><input id='attrs-8e1995ea-4e5c-4bcc-9d57-1aa08b81b281' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e1995ea-4e5c-4bcc-9d57-1aa08b81b281' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03c09b65-7388-4484-b7fd-6cc86ce681b9' class='xr-var-data-in' type='checkbox'><label for='data-03c09b65-7388-4484-b7fd-6cc86ce681b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>calendar :</span></dt><dd>proleptic_gregorian</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>units :</span></dt><dd>day as %Y%m%d.%f</dd></dl></div><div class='xr-var-data'><pre>array([20210516.      , 20210516.003472, 20210516.006944, ..., 20210516.989583,\n",
       "       20210516.993056, 20210516.996528])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d23ca203-a14a-4db9-a229-7afeebcba6d5' class='xr-section-summary-in' type='checkbox'  ><label for='section-d23ca203-a14a-4db9-a229-7afeebcba6d5' 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-0c9d8125-febe-407d-9544-fa649a8ec8d0' class='xr-index-data-in' type='checkbox'/><label for='index-0c9d8125-febe-407d-9544-fa649a8ec8d0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([        20210516.0, 20210516.003472224, 20210516.006944444,\n",
       "       20210516.010416668, 20210516.013888888, 20210516.017361112,\n",
       "       20210516.020833332, 20210516.024305556, 20210516.027777776,\n",
       "           20210516.03125,\n",
       "       ...\n",
       "       20210516.965277776,     20210516.96875, 20210516.972222224,\n",
       "       20210516.975694444, 20210516.979166668, 20210516.982638888,\n",
       "       20210516.986111112, 20210516.989583332, 20210516.993055556,\n",
       "       20210516.996527776],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=288))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d3fdaff5-6d42-4e5c-aadc-beebd1e1896a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3fdaff5-6d42-4e5c-aadc-beebd1e1896a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>calendar :</span></dt><dd>proleptic_gregorian</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>units :</span></dt><dd>day as %Y%m%d.%f</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 288)>\n",
       "array([20210516.      , 20210516.003472, 20210516.006944, ..., 20210516.989583,\n",
       "       20210516.993056, 20210516.996528])\n",
       "Coordinates:\n",
       "  * time     (time) float64 2.021e+07 2.021e+07 ... 2.021e+07 2.021e+07\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    calendar:       proleptic_gregorian\n",
       "    axis:           T\n",
       "    units:          day as %Y%m%d.%f"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain_mean.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The time format is special: `%Y%m%d.%f`. We use the `datetime` module to convert the time vector. We will use two function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def convert_time(t, roundTo = 60.):\n",
    "\n",
    "    '''\n",
    "    Utility converts between two time formats A->B or B->A: \n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    t : float or datetime object\n",
    "        time \n",
    "        A = either float as %Y%m%d.%f where %f is fraction of the day\n",
    "        B = datetime object\n",
    "    Returns\n",
    "    -------\n",
    "    tout : datetime object or float\n",
    "        time, counterpart to t\n",
    "    '''\n",
    "\n",
    "    t0 = datetime.datetime(1970, 1, 1)\n",
    "\n",
    "    if type(t) == type(t0):\n",
    "\n",
    "        tout = np.int( t.strftime('%Y%m%d') )\n",
    "        date = datetime.datetime.strptime(str(tout), '%Y%m%d')\n",
    "\n",
    "        dt = (t - date).total_seconds()\n",
    "        frac =  dt / (24 * 3600)\n",
    "\n",
    "        tout +=  frac\n",
    "    else:\n",
    "        \n",
    "        date = np.int(t)\n",
    "        frac = t - date\n",
    "\n",
    "        tout = datetime.datetime.strptime(str(date), '%Y%m%d')\n",
    "        tout += datetime.timedelta( days = frac )\n",
    "\n",
    "    return tout \n",
    "\n",
    "\n",
    "######################################################################\n",
    "######################################################################\n",
    "\n",
    "def convert_timevec( timevec ):\n",
    "    '''\n",
    "    Utility converts between an array of two time formats A->B or B->A: \n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    timevec : list or array\n",
    "        time list\n",
    "        A = either float as %Y%m%d.%f where %f is fraction of the day\n",
    "        B = datetime object\n",
    "    Returns\n",
    "    -------\n",
    "    tout : list\n",
    "        list of datetime objects or floats\n",
    "        time, counterpart to t\n",
    "    '''\n",
    "    \n",
    "    times = []\n",
    "    for tfloat in timevec:\n",
    "        times += [ convert_time( tfloat ), ]\n",
    "    \n",
    "    times = np.array( times )\n",
    "    \n",
    "    return times\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above functions is now applied to the `time` coordinate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3197056/2192904137.py:31: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  date = np.int(t)\n"
     ]
    }
   ],
   "source": [
    "rain_mean['time'] = convert_timevec( rain_mean.time.data )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will need the function `convert_timevec` rather often. Therefore, the two function are included in an `analysis/tools.py` module which can be loaded via:\n",
    "\n",
    "```python\n",
    "sys.path.append( '/work/bb1224/2024_MS-COURSE/tools/analysis' )\n",
    "from tools import convert_timevec\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure( figsize = (14,6))\n",
    "\n",
    "plt.gca().xaxis.set_major_formatter(myFmt)\n",
    "rain_mean.plot( lw = 3 )\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use this notebook to explore the time series of other variables.\n",
    "\n",
    "* How do different cloud cover variables look like?\n",
    "\n",
    "* How does LWP evolve? (variable `tqc_dia`)\n",
    "\n",
    "* How does CAPE look like?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "1 Python 3 (based on the module python3/2023.01)",
   "language": "python",
   "name": "python3_2023_01"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}