cacheb/cacheb-quickstart.ipynb

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  {
   "cell_type": "markdown",
   "id": "fb158044-8729-4464-9811-4ef8393aaded",
   "metadata": {},
   "source": [
    "# DestinE Platform CacheB Quickstart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990e6a7c-0fa0-46b9-b177-ac3f3f57d87b",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "### DestinE Platform Credentials\n",
    "\n",
    "You need to have an account on the [Destination Earth Platform](https://auth.destine.eu/realms/desp/account).\n",
    "\n",
    "#### ⚠️ Warning: Authorized Access Only\n",
    "The usage of this notebook and data access is reserved only to authorized user groups."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db2bbb1-2e10-4a64-9e84-601e3e7ee64c",
   "metadata": {},
   "source": [
    "## Access the Data\n",
    "With a DESP account you can access the dataset in the CacheB cache with Xarray / Zarr.\n",
    "\n",
    "Here below the list of available datasets, they are all reachable via a dedicated URL. In the following example we will work only with the Climate DT high resolution surface with hourly resolution, on atmosphere variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "efbaa0bb-8298-4ee9-a3b3-0fa9667968d7",
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   "outputs": [],
   "source": [
    "datasets = [\n",
    "    \"https://cacheb.dcms.destine.eu/d1-climate-dt/ScenarioMIP-SSP3-7.0-IFS-NEMO-0001-high-sfc-v0.zarr\",\n",
    "    \"https://cacheb.dcms.destine.eu/d1-climate-dt/ScenarioMIP-SSP3-7.0-IFS-NEMO-0001-high-o2d-v0.zarr\",\n",
    "    \"https://cacheb.dcms.destine.eu/d1-climate-dt/ScenarioMIP-SSP3-7.0-IFS-NEMO-0001-high-pl-v0.zarr\",\n",
    "    \"https://cacheb.dcms.destine.eu/d1-climate-dt/ScenarioMIP-SSP3-7.0-ICON-0001-high-sfc-v0.zarr\",\n",
    "    \"https://cacheb.dcms.destine.eu/d1-climate-dt/ScenarioMIP-SSP3-7.0-IFS-NEMO-0001-standard-sfc-v0.zarr\",\n",
    "    \"https://cacheb.dcms.destine.eu/era5/reanalysis-era5-land-no-antartica-v0.zarr\",\n",
    "    \"https://cacheb.dcms.destine.eu/era5/reanalysis-era5-single-levels-v0.zarr\",\n",
    "]"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e71b840",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture cap\n",
    "%run ./cacheb-authentication.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51b53ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_1 = cap.stdout.split('}\\n')\n",
    "token = output_1[-1][0:-1]\n",
    "\n",
    "from pathlib import Path\n",
    "with open(Path.home() / \".netrc\", \"a\") as fp:\n",
    "    fp.write(token)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "033a05bd",
   "metadata": {},
   "source": [
    "**NOTE**: the temporary password is valid for a limited period of time and needs to be regenerated and reconfigured periodically by running the cells above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "99aa0ac9-a9db-490d-b123-a611ff292027",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "\n",
    "data = xr.open_dataset(\n",
    "    \"https://cacheb.dcms.destine.eu/d1-climate-dt/ScenarioMIP-SSP3-7.0-IFS-NEMO-0001-high-sfc-v0.zarr\",\n",
    "    storage_options={\"client_kwargs\":{\"trust_env\":\"true\"}},\n",
    "    chunks={},\n",
    "    engine=\"zarr\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0cfb4e5-c7c2-4fed-a40a-18127fbe417c",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 188TB\n",
       "Dimensions:            (time: 175320, latitude: 4096, longitude: 8193)\n",
       "Coordinates:\n",
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       "  * longitude          (longitude) float64 66kB -180.0 -180.0 ... 180.0 180.0\n",
       "    step               timedelta64[ns] 8B ...\n",
       "    surface            float64 8B ...\n",
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       "    heightAboveGround  float64 8B ...\n",
       "    sd                 (time, latitude, longitude) float32 24TB dask.array&lt;chunksize=(48, 512, 512), meta=np.ndarray&gt;\n",
       "    ssr                (time, latitude, longitude) float32 24TB dask.array&lt;chunksize=(48, 512, 512), meta=np.ndarray&gt;\n",
       "    str                (time, latitude, longitude) float32 24TB dask.array&lt;chunksize=(48, 512, 512), meta=np.ndarray&gt;\n",
       "    t2m                (time, latitude, longitude) float32 24TB dask.array&lt;chunksize=(48, 512, 512), meta=np.ndarray&gt;\n",
       "    tprate             (time, latitude, longitude) float32 24TB dask.array&lt;chunksize=(48, 512, 512), meta=np.ndarray&gt;\n",
       "    u10                (time, latitude, longitude) float32 24TB dask.array&lt;chunksize=(48, 512, 512), meta=np.ndarray&gt;\n",
       "    v10                (time, latitude, longitude) float32 24TB dask.array&lt;chunksize=(48, 512, 512), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_edition:            2\n",
       "    GRIB_subCentre:          1003\n",
       "    history:                 2024-06-06T16:50 GRIB to CDM+CF via cfgrib-0.9.1...\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts</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-72c89400-d7f2-4ba2-9ffa-6716a19690e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-72c89400-d7f2-4ba2-9ffa-6716a19690e4' 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>: 175320</li><li><span class='xr-has-index'>latitude</span>: 4096</li><li><span class='xr-has-index'>longitude</span>: 8193</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-44a684c7-0585-433e-b05c-8449cccaf4b7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-44a684c7-0585-433e-b05c-8449cccaf4b7' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.96 -89.91 ... 89.96 90.0</div><input id='attrs-832535e7-ba69-4544-83ad-e3f189c2e8fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-832535e7-ba69-4544-83ad-e3f189c2e8fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88af9027-fdc4-47ee-9cd5-6a2153d863ab' class='xr-var-data-in' type='checkbox'><label for='data-88af9027-fdc4-47ee-9cd5-6a2153d863ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.956044, -89.912088, ...,  89.912088,  89.956044,\n",
       "        90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -180.0 ... 180.0 180.0</div><input id='attrs-80167f8a-dc96-472a-b19d-ec859098be21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80167f8a-dc96-472a-b19d-ec859098be21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-853d697c-fd97-4d25-89d6-7e5de96e1055' class='xr-var-data-in' type='checkbox'><label for='data-853d697c-fd97-4d25-89d6-7e5de96e1055' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-180.      , -179.956055, -179.912109, ...,  179.912109,  179.956055,\n",
       "        180.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-67b7c0a9-222c-45bd-81d2-1c71884b82d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67b7c0a9-222c-45bd-81d2-1c71884b82d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db30f355-1e73-4660-b89f-c2571180d3e1' class='xr-var-data-in' type='checkbox'><label for='data-db30f355-1e73-4660-b89f-c2571180d3e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-721fcb61-8faa-4133-9529-6deddb0a010f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-721fcb61-8faa-4133-9529-6deddb0a010f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4065ba3-7cd6-4fd5-86d8-05dd43d1d84c' class='xr-var-data-in' type='checkbox'><label for='data-e4065ba3-7cd6-4fd5-86d8-05dd43d1d84c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01 ... 2039-12-31T23:00:00</div><input id='attrs-c7bbab31-ff38-437f-885b-6cad627f3dd9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7bbab31-ff38-437f-885b-6cad627f3dd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e737bb82-6400-4275-a985-297315179ce6' class='xr-var-data-in' type='checkbox'><label for='data-e737bb82-6400-4275-a985-297315179ce6' 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;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T02:00:00.000000000&#x27;, ..., &#x27;2039-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2039-12-31T22:00:00.000000000&#x27;, &#x27;2039-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5827ffd8-671f-44c2-8017-d3f904a2fe8c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5827ffd8-671f-44c2-8017-d3f904a2fe8c' class='xr-section-summary' >Data variables: <span>(9)</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>d2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 512, 512), meta=np.ndarray&gt;</div><input id='attrs-87543800-ac46-4c2c-a32e-daa234fcd4e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87543800-ac46-4c2c-a32e-daa234fcd4e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09d524b1-a0a2-4abe-81bc-c5502d774f9e' class='xr-var-data-in' type='checkbox'><label for='data-09d524b1-a0a2-4abe-81bc-c5502d774f9e' 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>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>unknown</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>150</dd><dt><span>GRIB_gridType :</span></dt><dd>healpix</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>12582912</dd><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>last_restart_dim_updated :</span></dt><dd>175320</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <thead>\n",
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       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 21.40 TiB </td>\n",
       "                        <td> 48.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (175320, 4096, 8193) </td>\n",
       "                        <td> (48, 512, 512) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 496808 chunks in 2 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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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heightAboveGround</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3915adb7-38fe-4307-9d07-9eb7bc4af95b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3915adb7-38fe-4307-9d07-9eb7bc4af95b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-478e0b1d-4e7c-4848-a4ca-69067f574bc4' class='xr-var-data-in' type='checkbox'><label for='data-478e0b1d-4e7c-4848-a4ca-69067f574bc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height above the surface</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 512, 512), meta=np.ndarray&gt;</div><input id='attrs-c2c26df2-5f4b-4da1-8863-feadab1fc7d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2c26df2-5f4b-4da1-8863-feadab1fc7d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54a1e7ba-2693-4dfc-a56a-5d5110e5628c' class='xr-var-data-in' type='checkbox'><label for='data-54a1e7ba-2693-4dfc-a56a-5d5110e5628c' 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>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sd</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>150</dd><dt><span>GRIB_gridType :</span></dt><dd>healpix</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>Snow depth</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>12582912</dd><dt><span>GRIB_paramId :</span></dt><dd>141</dd><dt><span>GRIB_shortName :</span></dt><dd>sd</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>last_restart_dim_updated :</span></dt><dd>175320</dd><dt><span>long_name :</span></dt><dd>Snow depth</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m of water equivalent</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> 21.40 TiB </td>\n",
       "                        <td> 48.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (175320, 4096, 8193) </td>\n",
       "                        <td> (48, 512, 512) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 496808 chunks in 2 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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       "                <tbody>\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>t2m</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 512, 512), meta=np.ndarray&gt;</div><input id='attrs-4207b79b-a13a-445d-85b6-d46eaa994769' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4207b79b-a13a-445d-85b6-d46eaa994769' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdee62a1-89ea-4f2e-9814-f67c8cd6b051' class='xr-var-data-in' type='checkbox'><label for='data-bdee62a1-89ea-4f2e-9814-f67c8cd6b051' 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>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>150</dd><dt><span>GRIB_gridType :</span></dt><dd>healpix</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>12582912</dd><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>last_restart_dim_updated :</span></dt><dd>175320</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</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> 21.40 TiB </td>\n",
       "                        <td> 48.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (175320, 4096, 8193) </td>\n",
       "                        <td> (48, 512, 512) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 496808 chunks in 2 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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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 21.40 TiB </td>\n",
       "                        <td> 48.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
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       "                    <tr>\n",
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       "    <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> 21.40 TiB </td>\n",
       "                        <td> 48.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (175320, 4096, 8193) </td>\n",
       "                        <td> (48, 512, 512) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 496808 chunks in 2 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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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v10</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 512, 512), meta=np.ndarray&gt;</div><input id='attrs-66c1d73a-945a-4f78-8509-def7a43591af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66c1d73a-945a-4f78-8509-def7a43591af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bff52ead-8fa5-49d1-abc5-3717fefe5b47' class='xr-var-data-in' type='checkbox'><label for='data-bff52ead-8fa5-49d1-abc5-3717fefe5b47' 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>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v10</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>150</dd><dt><span>GRIB_gridType :</span></dt><dd>healpix</dd><dt><span>GRIB_missingValue :</span></dt><dd>3.4028234663852886e+38</dd><dt><span>GRIB_name :</span></dt><dd>10 metre V wind component</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>12582912</dd><dt><span>GRIB_paramId :</span></dt><dd>166</dd><dt><span>GRIB_shortName :</span></dt><dd>10v</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>last_restart_dim_updated :</span></dt><dd>175320</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</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> 21.40 TiB </td>\n",
       "                        <td> 48.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (175320, 4096, 8193) </td>\n",
       "                        <td> (48, 512, 512) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 496808 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
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       "       -89.86813186813187, -89.82417582417582, -89.78021978021978,\n",
       "       -89.73626373626374,  -89.6923076923077, -89.64835164835165,\n",
       "        -89.6043956043956,\n",
       "       ...\n",
       "        89.60439560439562,  89.64835164835165,  89.69230769230771,\n",
       "        89.73626373626374,   89.7802197802198,  89.82417582417582,\n",
       "        89.86813186813188,  89.91208791208791,  89.95604395604397,\n",
       "                     90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=4096))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cd7acbfb-b9cf-433d-a911-6373cd4a24d5' class='xr-index-data-in' type='checkbox'/><label for='index-cd7acbfb-b9cf-433d-a911-6373cd4a24d5' 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([             -180.0,     -179.9560546875, -179.91210937500003,\n",
       "           -179.8681640625,       -179.82421875,     -179.7802734375,\n",
       "            -179.736328125,     -179.6923828125,        -179.6484375,\n",
       "           -179.6044921875,\n",
       "       ...\n",
       "            179.6044921875,         179.6484375,      179.6923828125,\n",
       "             179.736328125,      179.7802734375,        179.82421875,\n",
       "            179.8681640625,  179.91210937500003,      179.9560546875,\n",
       "                     180.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=8193))</pre></div></li><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-d424d33e-67bf-41f5-a7d0-ce2c7158673b' class='xr-index-data-in' type='checkbox'/><label for='index-d424d33e-67bf-41f5-a7d0-ce2c7158673b' 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;2020-01-01 00:00:00&#x27;, &#x27;2020-01-01 01:00:00&#x27;,\n",
       "               &#x27;2020-01-01 02:00:00&#x27;, &#x27;2020-01-01 03:00:00&#x27;,\n",
       "               &#x27;2020-01-01 04:00:00&#x27;, &#x27;2020-01-01 05:00:00&#x27;,\n",
       "               &#x27;2020-01-01 06:00:00&#x27;, &#x27;2020-01-01 07:00:00&#x27;,\n",
       "               &#x27;2020-01-01 08:00:00&#x27;, &#x27;2020-01-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2039-12-31 14:00:00&#x27;, &#x27;2039-12-31 15:00:00&#x27;,\n",
       "               &#x27;2039-12-31 16:00:00&#x27;, &#x27;2039-12-31 17:00:00&#x27;,\n",
       "               &#x27;2039-12-31 18:00:00&#x27;, &#x27;2039-12-31 19:00:00&#x27;,\n",
       "               &#x27;2039-12-31 20:00:00&#x27;, &#x27;2039-12-31 21:00:00&#x27;,\n",
       "               &#x27;2039-12-31 22:00:00&#x27;, &#x27;2039-12-31 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=175320, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c8e9b6ef-c26e-4578-917c-05c43b9af6b9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8e9b6ef-c26e-4578-917c-05c43b9af6b9' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_subCentre :</span></dt><dd>1003</dd><dt><span>history :</span></dt><dd>2024-06-06T16:50 GRIB to CDM+CF via cfgrib-0.9.12.0/ecCodes-2.35.0 with {&quot;source&quot;: &quot;.xarray-ecmwf-cache/10baaccbb0bb6b0157fbe574dc7d566e.grib&quot;, &quot;filter_by_keys&quot;: {}, &quot;encode_cf&quot;: [&quot;parameter&quot;, &quot;time&quot;, &quot;geography&quot;, &quot;vertical&quot;]}</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 188TB\n",
       "Dimensions:            (time: 175320, latitude: 4096, longitude: 8193)\n",
       "Coordinates:\n",
       "  * latitude           (latitude) float64 33kB -90.0 -89.96 ... 89.96 90.0\n",
       "  * longitude          (longitude) float64 66kB -180.0 -180.0 ... 180.0 180.0\n",
       "    step               timedelta64[ns] 8B ...\n",
       "    surface            float64 8B ...\n",
       "  * time               (time) datetime64[ns] 1MB 2020-01-01 ... 2039-12-31T23...\n",
       "Data variables:\n",
       "    d2m                (time, latitude, longitude) float32 24TB dask.array<chunksize=(48, 512, 512), meta=np.ndarray>\n",
       "    heightAboveGround  float64 8B ...\n",
       "    sd                 (time, latitude, longitude) float32 24TB dask.array<chunksize=(48, 512, 512), meta=np.ndarray>\n",
       "    ssr                (time, latitude, longitude) float32 24TB dask.array<chunksize=(48, 512, 512), meta=np.ndarray>\n",
       "    str                (time, latitude, longitude) float32 24TB dask.array<chunksize=(48, 512, 512), meta=np.ndarray>\n",
       "    t2m                (time, latitude, longitude) float32 24TB dask.array<chunksize=(48, 512, 512), meta=np.ndarray>\n",
       "    tprate             (time, latitude, longitude) float32 24TB dask.array<chunksize=(48, 512, 512), meta=np.ndarray>\n",
       "    u10                (time, latitude, longitude) float32 24TB dask.array<chunksize=(48, 512, 512), meta=np.ndarray>\n",
       "    v10                (time, latitude, longitude) float32 24TB dask.array<chunksize=(48, 512, 512), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_edition:            2\n",
       "    GRIB_subCentre:          1003\n",
       "    history:                 2024-06-06T16:50 GRIB to CDM+CF via cfgrib-0.9.1...\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "markdown",
   "id": "189b34ad-bb60-4e3b-9a30-f44569745f58",
   "metadata": {},
   "source": [
    "## Working with data: example on Climate DT high resolution data\n",
    "\n",
    "Datasets on the DESP Data CacheB are typically very large and remotely hosted. Typical use imply a selection of the data followed by one or more reduction steps to be performed in a local or distributed Dask environment. \n",
    "\n",
    "The example goes through the following processing steps:\n",
    "1. data selection\n",
    "2. data reduction\n",
    "3. visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "65828ff0-618b-4f13-8cf0-ce0822497f8e",
   "metadata": {},
   "outputs": [
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       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
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       "\n",
       ".xr-sections {\n",
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       "  display: grid;\n",
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       ".xr-section-item input {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-item input + label {\n",
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       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
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       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
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       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
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       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
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       "  padding: 0 !important;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
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       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "  padding-right: 5px;\n",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
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       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m&#x27; (time: 175320, latitude: 182, longitude: 251)&gt; Size: 32GB\n",
       "dask.array&lt;getitem, shape=(175320, 182, 251), dtype=float32, chunksize=(48, 182, 251), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 1kB 47.01 47.05 47.1 ... 54.88 54.92 54.97\n",
       "  * longitude  (longitude) float64 2kB 5.01 5.054 5.098 ... 15.91 15.95 16.0\n",
       "    step       timedelta64[ns] 8B ...\n",
       "    surface    float64 8B ...\n",
       "  * time       (time) datetime64[ns] 1MB 2020-01-01 ... 2039-12-31T23:00:00\n",
       "Attributes:\n",
       "    units:    C</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'>'t2m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 175320</li><li><span class='xr-has-index'>latitude</span>: 182</li><li><span class='xr-has-index'>longitude</span>: 251</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-40ac5939-d3a6-4e74-9a06-ac97460ead3b' class='xr-array-in' type='checkbox' checked><label for='section-40ac5939-d3a6-4e74-9a06-ac97460ead3b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(48, 182, 251), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 29.84 GiB </td>\n",
       "                        <td> 8.36 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (175320, 182, 251) </td>\n",
       "                        <td> (48, 182, 251) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 3653 chunks in 4 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></div></li><li class='xr-section-item'><input id='section-fc6d220e-d0b3-4dfe-875c-58c6a390821c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc6d220e-d0b3-4dfe-875c-58c6a390821c' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.01 47.05 47.1 ... 54.92 54.97</div><input id='attrs-5bd95ee1-0dc0-4465-8794-2e555aa3ee71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bd95ee1-0dc0-4465-8794-2e555aa3ee71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d1e5af5-8097-4ae1-ad05-9ab8f7fd00b9' class='xr-var-data-in' type='checkbox'><label for='data-3d1e5af5-8097-4ae1-ad05-9ab8f7fd00b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([47.010989, 47.054945, 47.098901, 47.142857, 47.186813, 47.230769,\n",
       "       47.274725, 47.318681, 47.362637, 47.406593, 47.450549, 47.494505,\n",
       "       47.538462, 47.582418, 47.626374, 47.67033 , 47.714286, 47.758242,\n",
       "       47.802198, 47.846154, 47.89011 , 47.934066, 47.978022, 48.021978,\n",
       "       48.065934, 48.10989 , 48.153846, 48.197802, 48.241758, 48.285714,\n",
       "       48.32967 , 48.373626, 48.417582, 48.461538, 48.505495, 48.549451,\n",
       "       48.593407, 48.637363, 48.681319, 48.725275, 48.769231, 48.813187,\n",
       "       48.857143, 48.901099, 48.945055, 48.989011, 49.032967, 49.076923,\n",
       "       49.120879, 49.164835, 49.208791, 49.252747, 49.296703, 49.340659,\n",
       "       49.384615, 49.428571, 49.472527, 49.516484, 49.56044 , 49.604396,\n",
       "       49.648352, 49.692308, 49.736264, 49.78022 , 49.824176, 49.868132,\n",
       "       49.912088, 49.956044, 50.      , 50.043956, 50.087912, 50.131868,\n",
       "       50.175824, 50.21978 , 50.263736, 50.307692, 50.351648, 50.395604,\n",
       "       50.43956 , 50.483516, 50.527473, 50.571429, 50.615385, 50.659341,\n",
       "       50.703297, 50.747253, 50.791209, 50.835165, 50.879121, 50.923077,\n",
       "       50.967033, 51.010989, 51.054945, 51.098901, 51.142857, 51.186813,\n",
       "       51.230769, 51.274725, 51.318681, 51.362637, 51.406593, 51.450549,\n",
       "       51.494505, 51.538462, 51.582418, 51.626374, 51.67033 , 51.714286,\n",
       "       51.758242, 51.802198, 51.846154, 51.89011 , 51.934066, 51.978022,\n",
       "       52.021978, 52.065934, 52.10989 , 52.153846, 52.197802, 52.241758,\n",
       "       52.285714, 52.32967 , 52.373626, 52.417582, 52.461538, 52.505495,\n",
       "       52.549451, 52.593407, 52.637363, 52.681319, 52.725275, 52.769231,\n",
       "       52.813187, 52.857143, 52.901099, 52.945055, 52.989011, 53.032967,\n",
       "       53.076923, 53.120879, 53.164835, 53.208791, 53.252747, 53.296703,\n",
       "       53.340659, 53.384615, 53.428571, 53.472527, 53.516484, 53.56044 ,\n",
       "       53.604396, 53.648352, 53.692308, 53.736264, 53.78022 , 53.824176,\n",
       "       53.868132, 53.912088, 53.956044, 54.      , 54.043956, 54.087912,\n",
       "       54.131868, 54.175824, 54.21978 , 54.263736, 54.307692, 54.351648,\n",
       "       54.395604, 54.43956 , 54.483516, 54.527473, 54.571429, 54.615385,\n",
       "       54.659341, 54.703297, 54.747253, 54.791209, 54.835165, 54.879121,\n",
       "       54.923077, 54.967033])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.01 5.054 5.098 ... 15.95 16.0</div><input id='attrs-84ce4b37-4cdf-4e9b-9105-f6818c8e645c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84ce4b37-4cdf-4e9b-9105-f6818c8e645c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-564b05f3-ab5d-4a19-8fd6-ad1d11535830' class='xr-var-data-in' type='checkbox'><label for='data-564b05f3-ab5d-4a19-8fd6-ad1d11535830' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ 5.009766,  5.053711,  5.097656, ..., 15.908203, 15.952148, 15.996094])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-44b90f52-f9ea-49ec-88f6-ef6128a268b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44b90f52-f9ea-49ec-88f6-ef6128a268b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a951452d-264d-4206-9560-1aa902dd1cc4' class='xr-var-data-in' type='checkbox'><label for='data-a951452d-264d-4206-9560-1aa902dd1cc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d29a6dce-117b-421f-90cf-9f13cfb4d722' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d29a6dce-117b-421f-90cf-9f13cfb4d722' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da4b1bcb-1084-4c1d-8ad4-cd4efce173c3' class='xr-var-data-in' type='checkbox'><label for='data-da4b1bcb-1084-4c1d-8ad4-cd4efce173c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01 ... 2039-12-31T23:00:00</div><input id='attrs-a23c97b5-0f5d-418b-8637-6224698bac84' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a23c97b5-0f5d-418b-8637-6224698bac84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8fb71d6-c8f1-4030-8782-35bfe21d4cf7' class='xr-var-data-in' type='checkbox'><label for='data-c8fb71d6-c8f1-4030-8782-35bfe21d4cf7' 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;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T02:00:00.000000000&#x27;, ..., &#x27;2039-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2039-12-31T22:00:00.000000000&#x27;, &#x27;2039-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b787445a-7c34-4133-8498-7719bebc75c1' class='xr-section-summary-in' type='checkbox'  ><label for='section-b787445a-7c34-4133-8498-7719bebc75c1' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-05ea5fb9-7722-494b-bd10-6d5317a19384' class='xr-index-data-in' type='checkbox'/><label for='index-05ea5fb9-7722-494b-bd10-6d5317a19384' 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([ 47.01098901098902,  47.05494505494508,  47.09890109890111,\n",
       "        47.14285714285717,   47.1868131868132, 47.230769230769255,\n",
       "        47.27472527472529,  47.31868131868134,  47.36263736263737,\n",
       "        47.40659340659343,\n",
       "       ...\n",
       "        54.57142857142859,  54.61538461538461,  54.65934065934067,\n",
       "         54.7032967032967,  54.74725274725276,  54.79120879120879,\n",
       "       54.835164835164846, 54.879120879120904, 54.923076923076934,\n",
       "        54.96703296703299],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=182))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e678c3be-e9c7-4a45-9d38-d76c8cbfb0e8' class='xr-index-data-in' type='checkbox'/><label for='index-e678c3be-e9c7-4a45-9d38-d76c8cbfb0e8' 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([ 5.009765625000001,       5.0537109375,         5.09765625,\n",
       "             5.1416015625,        5.185546875,       5.2294921875,\n",
       "                5.2734375,       5.3173828125,        5.361328125,\n",
       "             5.4052734375,\n",
       "       ...\n",
       "            15.6005859375, 15.644531250000002,      15.6884765625,\n",
       "             15.732421875, 15.776367187499998,         15.8203125,\n",
       "       15.864257812500002,       15.908203125, 15.952148437500002,\n",
       "              15.99609375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=251))</pre></div></li><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-cb7bb786-9a20-4677-a608-f81d02354346' class='xr-index-data-in' type='checkbox'/><label for='index-cb7bb786-9a20-4677-a608-f81d02354346' 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;2020-01-01 00:00:00&#x27;, &#x27;2020-01-01 01:00:00&#x27;,\n",
       "               &#x27;2020-01-01 02:00:00&#x27;, &#x27;2020-01-01 03:00:00&#x27;,\n",
       "               &#x27;2020-01-01 04:00:00&#x27;, &#x27;2020-01-01 05:00:00&#x27;,\n",
       "               &#x27;2020-01-01 06:00:00&#x27;, &#x27;2020-01-01 07:00:00&#x27;,\n",
       "               &#x27;2020-01-01 08:00:00&#x27;, &#x27;2020-01-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2039-12-31 14:00:00&#x27;, &#x27;2039-12-31 15:00:00&#x27;,\n",
       "               &#x27;2039-12-31 16:00:00&#x27;, &#x27;2039-12-31 17:00:00&#x27;,\n",
       "               &#x27;2039-12-31 18:00:00&#x27;, &#x27;2039-12-31 19:00:00&#x27;,\n",
       "               &#x27;2039-12-31 20:00:00&#x27;, &#x27;2039-12-31 21:00:00&#x27;,\n",
       "               &#x27;2039-12-31 22:00:00&#x27;, &#x27;2039-12-31 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=175320, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b0f75ed-5652-4479-bbec-5e7e10245a6f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6b0f75ed-5652-4479-bbec-5e7e10245a6f' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>C</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 't2m' (time: 175320, latitude: 182, longitude: 251)> Size: 32GB\n",
       "dask.array<getitem, shape=(175320, 182, 251), dtype=float32, chunksize=(48, 182, 251), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 1kB 47.01 47.05 47.1 ... 54.88 54.92 54.97\n",
       "  * longitude  (longitude) float64 2kB 5.01 5.054 5.098 ... 15.91 15.95 16.0\n",
       "    step       timedelta64[ns] 8B ...\n",
       "    surface    float64 8B ...\n",
       "  * time       (time) datetime64[ns] 1MB 2020-01-01 ... 2039-12-31T23:00:00\n",
       "Attributes:\n",
       "    units:    C"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2m = data.t2m.astype(\"float32\") - 273.15\n",
    "t2m.attrs[\"units\"] = \"C\"\n",
    "t2m_germany_area = t2m.sel(**{\"latitude\": slice(47, 55), \"longitude\": slice(5, 16)})\n",
    "t2m_germany_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33bf3367-d8db-4b52-aa47-028921afa6bd",
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
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       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
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       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m&#x27; (time: 744, latitude: 182, longitude: 251)&gt; Size: 136MB\n",
       "dask.array&lt;getitem, shape=(744, 182, 251), dtype=float32, chunksize=(48, 182, 251), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 1kB 47.01 47.05 47.1 ... 54.88 54.92 54.97\n",
       "  * longitude  (longitude) float64 2kB 5.01 5.054 5.098 ... 15.91 15.95 16.0\n",
       "    step       timedelta64[ns] 8B ...\n",
       "    surface    float64 8B ...\n",
       "  * time       (time) datetime64[ns] 6kB 2028-01-01 ... 2028-01-31T23:00:00\n",
       "Attributes:\n",
       "    units:    C</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'>'t2m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 744</li><li><span class='xr-has-index'>latitude</span>: 182</li><li><span class='xr-has-index'>longitude</span>: 251</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2dce7f12-829b-4442-9258-f38d141913a4' class='xr-array-in' type='checkbox' checked><label for='section-2dce7f12-829b-4442-9258-f38d141913a4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(48, 182, 251), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 129.65 MiB </td>\n",
       "                        <td> 8.36 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (744, 182, 251) </td>\n",
       "                        <td> (48, 182, 251) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 16 chunks in 5 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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       "</table></div></div></li><li class='xr-section-item'><input id='section-a1fa7ac3-7eb2-4ebb-acab-7fde5d53b0f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a1fa7ac3-7eb2-4ebb-acab-7fde5d53b0f4' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.01 47.05 47.1 ... 54.92 54.97</div><input id='attrs-a5573638-a7f6-434d-aded-61bedc6e5ad9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5573638-a7f6-434d-aded-61bedc6e5ad9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f26c3a93-16f7-49b8-a982-e4dcd85f76ba' class='xr-var-data-in' type='checkbox'><label for='data-f26c3a93-16f7-49b8-a982-e4dcd85f76ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([47.010989, 47.054945, 47.098901, 47.142857, 47.186813, 47.230769,\n",
       "       47.274725, 47.318681, 47.362637, 47.406593, 47.450549, 47.494505,\n",
       "       47.538462, 47.582418, 47.626374, 47.67033 , 47.714286, 47.758242,\n",
       "       47.802198, 47.846154, 47.89011 , 47.934066, 47.978022, 48.021978,\n",
       "       48.065934, 48.10989 , 48.153846, 48.197802, 48.241758, 48.285714,\n",
       "       48.32967 , 48.373626, 48.417582, 48.461538, 48.505495, 48.549451,\n",
       "       48.593407, 48.637363, 48.681319, 48.725275, 48.769231, 48.813187,\n",
       "       48.857143, 48.901099, 48.945055, 48.989011, 49.032967, 49.076923,\n",
       "       49.120879, 49.164835, 49.208791, 49.252747, 49.296703, 49.340659,\n",
       "       49.384615, 49.428571, 49.472527, 49.516484, 49.56044 , 49.604396,\n",
       "       49.648352, 49.692308, 49.736264, 49.78022 , 49.824176, 49.868132,\n",
       "       49.912088, 49.956044, 50.      , 50.043956, 50.087912, 50.131868,\n",
       "       50.175824, 50.21978 , 50.263736, 50.307692, 50.351648, 50.395604,\n",
       "       50.43956 , 50.483516, 50.527473, 50.571429, 50.615385, 50.659341,\n",
       "       50.703297, 50.747253, 50.791209, 50.835165, 50.879121, 50.923077,\n",
       "       50.967033, 51.010989, 51.054945, 51.098901, 51.142857, 51.186813,\n",
       "       51.230769, 51.274725, 51.318681, 51.362637, 51.406593, 51.450549,\n",
       "       51.494505, 51.538462, 51.582418, 51.626374, 51.67033 , 51.714286,\n",
       "       51.758242, 51.802198, 51.846154, 51.89011 , 51.934066, 51.978022,\n",
       "       52.021978, 52.065934, 52.10989 , 52.153846, 52.197802, 52.241758,\n",
       "       52.285714, 52.32967 , 52.373626, 52.417582, 52.461538, 52.505495,\n",
       "       52.549451, 52.593407, 52.637363, 52.681319, 52.725275, 52.769231,\n",
       "       52.813187, 52.857143, 52.901099, 52.945055, 52.989011, 53.032967,\n",
       "       53.076923, 53.120879, 53.164835, 53.208791, 53.252747, 53.296703,\n",
       "       53.340659, 53.384615, 53.428571, 53.472527, 53.516484, 53.56044 ,\n",
       "       53.604396, 53.648352, 53.692308, 53.736264, 53.78022 , 53.824176,\n",
       "       53.868132, 53.912088, 53.956044, 54.      , 54.043956, 54.087912,\n",
       "       54.131868, 54.175824, 54.21978 , 54.263736, 54.307692, 54.351648,\n",
       "       54.395604, 54.43956 , 54.483516, 54.527473, 54.571429, 54.615385,\n",
       "       54.659341, 54.703297, 54.747253, 54.791209, 54.835165, 54.879121,\n",
       "       54.923077, 54.967033])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.01 5.054 5.098 ... 15.95 16.0</div><input id='attrs-a13ae1dc-4632-4b64-813b-31073293ff77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a13ae1dc-4632-4b64-813b-31073293ff77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71b70410-62c8-43c5-aec4-f5abc4d5600d' class='xr-var-data-in' type='checkbox'><label for='data-71b70410-62c8-43c5-aec4-f5abc4d5600d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ 5.009766,  5.053711,  5.097656, ..., 15.908203, 15.952148, 15.996094])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e29d3580-6831-4f4f-a09c-4c735775a14f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e29d3580-6831-4f4f-a09c-4c735775a14f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f8d6105-eacf-473d-ac83-0f87bf23673f' class='xr-var-data-in' type='checkbox'><label for='data-4f8d6105-eacf-473d-ac83-0f87bf23673f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b2d71ece-abd3-4d2d-a740-f686a4daf545' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2d71ece-abd3-4d2d-a740-f686a4daf545' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae9ff9ae-724f-496b-9f09-186c7b9e280e' class='xr-var-data-in' type='checkbox'><label for='data-ae9ff9ae-724f-496b-9f09-186c7b9e280e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2028-01-01 ... 2028-01-31T23:00:00</div><input id='attrs-3b82a391-f738-4552-ba29-e2b457cd5133' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b82a391-f738-4552-ba29-e2b457cd5133' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-508ca92e-8c5f-4c8c-b303-922e0e3920a5' class='xr-var-data-in' type='checkbox'><label for='data-508ca92e-8c5f-4c8c-b303-922e0e3920a5' 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;2028-01-01T00:00:00.000000000&#x27;, &#x27;2028-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2028-01-01T02:00:00.000000000&#x27;, ..., &#x27;2028-01-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2028-01-31T22:00:00.000000000&#x27;, &#x27;2028-01-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff0eaf73-63f3-456c-b1b8-3b6a86c6fcee' class='xr-section-summary-in' type='checkbox'  ><label for='section-ff0eaf73-63f3-456c-b1b8-3b6a86c6fcee' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-12f9c087-2311-4281-99d1-5ae430d46f47' class='xr-index-data-in' type='checkbox'/><label for='index-12f9c087-2311-4281-99d1-5ae430d46f47' 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([ 47.01098901098902,  47.05494505494508,  47.09890109890111,\n",
       "        47.14285714285717,   47.1868131868132, 47.230769230769255,\n",
       "        47.27472527472529,  47.31868131868134,  47.36263736263737,\n",
       "        47.40659340659343,\n",
       "       ...\n",
       "        54.57142857142859,  54.61538461538461,  54.65934065934067,\n",
       "         54.7032967032967,  54.74725274725276,  54.79120879120879,\n",
       "       54.835164835164846, 54.879120879120904, 54.923076923076934,\n",
       "        54.96703296703299],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=182))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-339f2727-22b5-4f45-a44c-64ac41e0b468' class='xr-index-data-in' type='checkbox'/><label for='index-339f2727-22b5-4f45-a44c-64ac41e0b468' 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([ 5.009765625000001,       5.0537109375,         5.09765625,\n",
       "             5.1416015625,        5.185546875,       5.2294921875,\n",
       "                5.2734375,       5.3173828125,        5.361328125,\n",
       "             5.4052734375,\n",
       "       ...\n",
       "            15.6005859375, 15.644531250000002,      15.6884765625,\n",
       "             15.732421875, 15.776367187499998,         15.8203125,\n",
       "       15.864257812500002,       15.908203125, 15.952148437500002,\n",
       "              15.99609375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=251))</pre></div></li><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-e01f7007-f9a9-4bff-b85a-59aea23e6087' class='xr-index-data-in' type='checkbox'/><label for='index-e01f7007-f9a9-4bff-b85a-59aea23e6087' 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;2028-01-01 00:00:00&#x27;, &#x27;2028-01-01 01:00:00&#x27;,\n",
       "               &#x27;2028-01-01 02:00:00&#x27;, &#x27;2028-01-01 03:00:00&#x27;,\n",
       "               &#x27;2028-01-01 04:00:00&#x27;, &#x27;2028-01-01 05:00:00&#x27;,\n",
       "               &#x27;2028-01-01 06:00:00&#x27;, &#x27;2028-01-01 07:00:00&#x27;,\n",
       "               &#x27;2028-01-01 08:00:00&#x27;, &#x27;2028-01-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2028-01-31 14:00:00&#x27;, &#x27;2028-01-31 15:00:00&#x27;,\n",
       "               &#x27;2028-01-31 16:00:00&#x27;, &#x27;2028-01-31 17:00:00&#x27;,\n",
       "               &#x27;2028-01-31 18:00:00&#x27;, &#x27;2028-01-31 19:00:00&#x27;,\n",
       "               &#x27;2028-01-31 20:00:00&#x27;, &#x27;2028-01-31 21:00:00&#x27;,\n",
       "               &#x27;2028-01-31 22:00:00&#x27;, &#x27;2028-01-31 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=744, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80e447dc-7e49-4bd5-8f47-dfe15202261d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80e447dc-7e49-4bd5-8f47-dfe15202261d' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>C</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 't2m' (time: 744, latitude: 182, longitude: 251)> Size: 136MB\n",
       "dask.array<getitem, shape=(744, 182, 251), dtype=float32, chunksize=(48, 182, 251), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 1kB 47.01 47.05 47.1 ... 54.88 54.92 54.97\n",
       "  * longitude  (longitude) float64 2kB 5.01 5.054 5.098 ... 15.91 15.95 16.0\n",
       "    step       timedelta64[ns] 8B ...\n",
       "    surface    float64 8B ...\n",
       "  * time       (time) datetime64[ns] 6kB 2028-01-01 ... 2028-01-31T23:00:00\n",
       "Attributes:\n",
       "    units:    C"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2m_germany_area_selection = t2m_germany_area.sel(time=\"2028-01\")\n",
    "t2m_germany_area_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe496d38-862b-4184-8557-0d000f9c4aab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.73 s, sys: 781 ms, total: 2.51 s\n",
      "Wall time: 5.96 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "t2m_germany_area_selection = t2m_germany_area_selection.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aefa0482-bb68-4854-b966-f1c75dae25b3",
   "metadata": {},
   "source": [
    "### 2. Data reduction\n",
    "\n",
    "Now that the data is loaded in memory, we can easily compute the monthly mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e7e58eb5-eb1a-458a-ba52-b0aaebab2ba1",
   "metadata": {},
   "outputs": [
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       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m&#x27; (latitude: 182, longitude: 251)&gt; Size: 183kB\n",
       "dask.array&lt;mean_agg-aggregate, shape=(182, 251), dtype=float32, chunksize=(182, 251), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 1kB 47.01 47.05 47.1 ... 54.88 54.92 54.97\n",
       "  * longitude  (longitude) float64 2kB 5.01 5.054 5.098 ... 15.91 15.95 16.0\n",
       "    step       timedelta64[ns] 8B ...\n",
       "    surface    float64 8B ...</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'>'t2m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 182</li><li><span class='xr-has-index'>longitude</span>: 251</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-24fad48d-ab29-4453-a27a-4db3e3c0171d' class='xr-array-in' type='checkbox' checked><label for='section-24fad48d-ab29-4453-a27a-4db3e3c0171d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(182, 251), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 178.45 kiB </td>\n",
       "                        <td> 178.45 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (182, 251) </td>\n",
       "                        <td> (182, 251) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 8 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=\"170\" height=\"137\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"87\" x2=\"120\" y2=\"87\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"87\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"87\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,87.01195219123507 0.0,87.01195219123507\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"107.011952\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >251</text>\n",
       "  <text x=\"140.000000\" y=\"43.505976\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,43.505976)\">182</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-f01f09b6-a78a-4cba-9fd7-be211bdac75e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f01f09b6-a78a-4cba-9fd7-be211bdac75e' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.01 47.05 47.1 ... 54.92 54.97</div><input id='attrs-ffb558d4-faca-4cc4-a786-caaa8c16b91d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffb558d4-faca-4cc4-a786-caaa8c16b91d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dc5d371-8787-4f50-ae73-7334a074ab25' class='xr-var-data-in' type='checkbox'><label for='data-6dc5d371-8787-4f50-ae73-7334a074ab25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([47.010989, 47.054945, 47.098901, 47.142857, 47.186813, 47.230769,\n",
       "       47.274725, 47.318681, 47.362637, 47.406593, 47.450549, 47.494505,\n",
       "       47.538462, 47.582418, 47.626374, 47.67033 , 47.714286, 47.758242,\n",
       "       47.802198, 47.846154, 47.89011 , 47.934066, 47.978022, 48.021978,\n",
       "       48.065934, 48.10989 , 48.153846, 48.197802, 48.241758, 48.285714,\n",
       "       48.32967 , 48.373626, 48.417582, 48.461538, 48.505495, 48.549451,\n",
       "       48.593407, 48.637363, 48.681319, 48.725275, 48.769231, 48.813187,\n",
       "       48.857143, 48.901099, 48.945055, 48.989011, 49.032967, 49.076923,\n",
       "       49.120879, 49.164835, 49.208791, 49.252747, 49.296703, 49.340659,\n",
       "       49.384615, 49.428571, 49.472527, 49.516484, 49.56044 , 49.604396,\n",
       "       49.648352, 49.692308, 49.736264, 49.78022 , 49.824176, 49.868132,\n",
       "       49.912088, 49.956044, 50.      , 50.043956, 50.087912, 50.131868,\n",
       "       50.175824, 50.21978 , 50.263736, 50.307692, 50.351648, 50.395604,\n",
       "       50.43956 , 50.483516, 50.527473, 50.571429, 50.615385, 50.659341,\n",
       "       50.703297, 50.747253, 50.791209, 50.835165, 50.879121, 50.923077,\n",
       "       50.967033, 51.010989, 51.054945, 51.098901, 51.142857, 51.186813,\n",
       "       51.230769, 51.274725, 51.318681, 51.362637, 51.406593, 51.450549,\n",
       "       51.494505, 51.538462, 51.582418, 51.626374, 51.67033 , 51.714286,\n",
       "       51.758242, 51.802198, 51.846154, 51.89011 , 51.934066, 51.978022,\n",
       "       52.021978, 52.065934, 52.10989 , 52.153846, 52.197802, 52.241758,\n",
       "       52.285714, 52.32967 , 52.373626, 52.417582, 52.461538, 52.505495,\n",
       "       52.549451, 52.593407, 52.637363, 52.681319, 52.725275, 52.769231,\n",
       "       52.813187, 52.857143, 52.901099, 52.945055, 52.989011, 53.032967,\n",
       "       53.076923, 53.120879, 53.164835, 53.208791, 53.252747, 53.296703,\n",
       "       53.340659, 53.384615, 53.428571, 53.472527, 53.516484, 53.56044 ,\n",
       "       53.604396, 53.648352, 53.692308, 53.736264, 53.78022 , 53.824176,\n",
       "       53.868132, 53.912088, 53.956044, 54.      , 54.043956, 54.087912,\n",
       "       54.131868, 54.175824, 54.21978 , 54.263736, 54.307692, 54.351648,\n",
       "       54.395604, 54.43956 , 54.483516, 54.527473, 54.571429, 54.615385,\n",
       "       54.659341, 54.703297, 54.747253, 54.791209, 54.835165, 54.879121,\n",
       "       54.923077, 54.967033])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.01 5.054 5.098 ... 15.95 16.0</div><input id='attrs-f849996b-5966-4c08-afb4-a92f773ca4ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f849996b-5966-4c08-afb4-a92f773ca4ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f8438f2-1149-4c40-83e4-eae8ab4d6c7e' class='xr-var-data-in' type='checkbox'><label for='data-6f8438f2-1149-4c40-83e4-eae8ab4d6c7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ 5.009766,  5.053711,  5.097656, ..., 15.908203, 15.952148, 15.996094])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-34ea76e3-954d-49eb-965b-ed1975225bd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34ea76e3-954d-49eb-965b-ed1975225bd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb07cf7d-726b-4a66-9c7b-7906199bdf71' class='xr-var-data-in' type='checkbox'><label for='data-bb07cf7d-726b-4a66-9c7b-7906199bdf71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=timedelta64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b3ae317-d0f9-4a2c-a4f1-c76c18cbee0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b3ae317-d0f9-4a2c-a4f1-c76c18cbee0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b06afc1-9380-462f-80d3-b592f4cb2a3d' class='xr-var-data-in' type='checkbox'><label for='data-7b06afc1-9380-462f-80d3-b592f4cb2a3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>original GRIB coordinate for key: level(surface)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-65bc1883-85a1-4779-bc60-2da4e908193b' class='xr-section-summary-in' type='checkbox'  ><label for='section-65bc1883-85a1-4779-bc60-2da4e908193b' class='xr-section-summary' >Indexes: <span>(2)</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>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a6632e67-7b33-4a54-979a-baca10bd11e7' class='xr-index-data-in' type='checkbox'/><label for='index-a6632e67-7b33-4a54-979a-baca10bd11e7' 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([ 47.01098901098902,  47.05494505494508,  47.09890109890111,\n",
       "        47.14285714285717,   47.1868131868132, 47.230769230769255,\n",
       "        47.27472527472529,  47.31868131868134,  47.36263736263737,\n",
       "        47.40659340659343,\n",
       "       ...\n",
       "        54.57142857142859,  54.61538461538461,  54.65934065934067,\n",
       "         54.7032967032967,  54.74725274725276,  54.79120879120879,\n",
       "       54.835164835164846, 54.879120879120904, 54.923076923076934,\n",
       "        54.96703296703299],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=182))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a13d0b68-0b32-41e9-ba62-119769a2c13c' class='xr-index-data-in' type='checkbox'/><label for='index-a13d0b68-0b32-41e9-ba62-119769a2c13c' 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([ 5.009765625000001,       5.0537109375,         5.09765625,\n",
       "             5.1416015625,        5.185546875,       5.2294921875,\n",
       "                5.2734375,       5.3173828125,        5.361328125,\n",
       "             5.4052734375,\n",
       "       ...\n",
       "            15.6005859375, 15.644531250000002,      15.6884765625,\n",
       "             15.732421875, 15.776367187499998,         15.8203125,\n",
       "       15.864257812500002,       15.908203125, 15.952148437500002,\n",
       "              15.99609375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=251))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62d80416-2c10-473d-ac1a-1506abc23f28' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-62d80416-2c10-473d-ac1a-1506abc23f28' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 't2m' (latitude: 182, longitude: 251)> Size: 183kB\n",
       "dask.array<mean_agg-aggregate, shape=(182, 251), dtype=float32, chunksize=(182, 251), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 1kB 47.01 47.05 47.1 ... 54.88 54.92 54.97\n",
       "  * longitude  (longitude) float64 2kB 5.01 5.054 5.098 ... 15.91 15.95 16.0\n",
       "    step       timedelta64[ns] 8B ...\n",
       "    surface    float64 8B ..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2m_germany_area_selection_monthly_mean = t2m_germany_area_selection.mean(dim=\"time\")\n",
    "t2m_germany_area_selection_monthly_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7a43c9-9a20-4e79-835e-710124326487",
   "metadata": {},
   "source": [
    "### 3. Visualization\n",
    "Finally, we can plot the monthly mean on a map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0cae4185-312a-474d-8e71-faa72706751e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cf\n",
    "\n",
    "ax = plt.axes(projection=ccrs.Mercator())\n",
    "t2m_germany_area_selection_monthly_mean.plot(ax=ax,transform=ccrs.PlateCarree(),cmap=\"YlOrRd\",vmax=None)  \n",
    "plt.title(\"Mean Surface Temperature, Jan 2028\")\n",
    "ax.coastlines()\n",
    "ax.add_feature(cf.BORDERS)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb4f00e1-b4f2-410a-9aa3-300e1a03ac6b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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