daily_cycle.ipynb

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   "source": [
    "# Daily cycle\n",
    "\n",
    "Data access example to plot the daily precipitation cycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4309a36-bb0a-459e-b4ca-c65a8167da43",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cefd1fa6-4349-4bc4-8174-d1382506ff84",
   "metadata": {},
   "source": [
    "Open the virtual data set for hourly (1hr) data interpolated to the regular lon-lat grid (CSAM-4i):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd97646a-552f-41ce-9f98-5d4a364e2104",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lon: 676, lat: 451, time: 26308, bnds: 2)\n",
       "Coordinates:\n",
       "  * lon        (lon) float64 -75.0 -74.96 -74.92 -74.88 ... -48.08 -48.04 -48.0\n",
       "  * lat        (lat) float64 -35.0 -34.96 -34.92 -34.88 ... -17.08 -17.04 -17.0\n",
       "  * time       (time) datetime64[ns] 2018-06-01 ... 2021-06-01\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) float64 dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;\n",
       "    hurs       (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    huss       (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    pr         (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    rsds       (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    sfcWind    (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    tas        (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    uas        (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    vas        (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "Attributes: (12/25)\n",
       "    Conventions:                    CF-1.8\n",
       "    history:                        Thu Jul 14 15:37:45 2022: cdo remap,desti...\n",
       "    institution:                    Instituto de Fisica de Cantabria, CSIC-Un...\n",
       "    creation_date:                  2022-07-12-T16:54:19Z\n",
       "    experiment:                     Evaluation run with ECMWF ERA5 reanalysis...\n",
       "    experiment_id:                  FPS-SESA\n",
       "    ...                             ...\n",
       "    comment:                        Simulation performed at CESGA (https://ww...\n",
       "    CDO:                            Climate Data Operators version 1.9.8 (htt...\n",
       "    CORDEX_domain:                  CSAM-4i\n",
       "    contact:                        meteo@unican.es\n",
       "    size:                           2147483647\n",
       "    size_human:                     142.0 GiB</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-fe11b967-9d2d-4d55-9ab6-854b5ce3d5b0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fe11b967-9d2d-4d55-9ab6-854b5ce3d5b0' 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'>lon</span>: 676</li><li><span class='xr-has-index'>lat</span>: 451</li><li><span class='xr-has-index'>time</span>: 26308</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-79d82d44-ecc7-4f61-a9cd-d3c72a19a2e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-79d82d44-ecc7-4f61-a9cd-d3c72a19a2e8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-75.0 -74.96 ... -48.04 -48.0</div><input id='attrs-efc5e773-3ad7-4ec4-81ee-0da9020f45e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efc5e773-3ad7-4ec4-81ee-0da9020f45e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ed29f6f-b7c5-4609-9ae5-e654c5120bef' class='xr-var-data-in' type='checkbox'><label for='data-2ed29f6f-b7c5-4609-9ae5-e654c5120bef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_ChunkSizes :</span></dt><dd>676</dd></dl></div><div class='xr-var-data'><pre>array([-75.  , -74.96, -74.92, ..., -48.08, -48.04, -48.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-35.0 -34.96 ... -17.04 -17.0</div><input id='attrs-3285d50d-da46-4711-a466-ec8c8d32b344' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3285d50d-da46-4711-a466-ec8c8d32b344' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c23250b-165e-407d-b03b-ea9ef4af52b9' class='xr-var-data-in' type='checkbox'><label for='data-0c23250b-165e-407d-b03b-ea9ef4af52b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_ChunkSizes :</span></dt><dd>451</dd></dl></div><div class='xr-var-data'><pre>array([-35.  , -34.96, -34.92, ..., -17.08, -17.04, -17.  ])</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'>2018-06-01 ... 2021-06-01</div><input id='attrs-37cdceab-3c12-4a03-a2d8-ac13a5ee5e82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37cdceab-3c12-4a03-a2d8-ac13a5ee5e82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f7d08df-6517-4886-ab51-ede8826f5608' class='xr-var-data-in' type='checkbox'><label for='data-0f7d08df-6517-4886-ab51-ede8826f5608' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>_ChunkSizes :</span></dt><dd>8785</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-06-01T00:00:00.000000000&#x27;, &#x27;2018-06-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2018-06-01T02:00:00.000000000&#x27;, ..., &#x27;2021-05-31T22:00:00.000000000&#x27;,\n",
       "       &#x27;2021-05-31T23:00:00.000000000&#x27;, &#x27;2021-06-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c251dc90-f316-49c2-95ed-803959ade961' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c251dc90-f316-49c2-95ed-803959ade961' 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>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 2), meta=np.ndarray&gt;</div><input id='attrs-b9ee1ff4-2880-45f2-95e9-600a3d8d4c02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9ee1ff4-2880-45f2-95e9-600a3d8d4c02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af297fab-1a14-46d9-aea2-8fb5624111a9' class='xr-var-data-in' type='checkbox'><label for='data-af297fab-1a14-46d9-aea2-8fb5624111a9' 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>_ChunkSizes :</span></dt><dd>[8784    2]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 411.06 kiB </td>\n",
       "                        <td> 1.56 kiB </td>\n",
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       "                        <td> (26308, 2) </td>\n",
       "                        <td> (100, 2) </td>\n",
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       "                        <th> Count </th>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hurs</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-51dc7de2-bb21-4f25-a2e3-8fd9af2986e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51dc7de2-bb21-4f25-a2e3-8fd9af2986e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33b02433-de9b-4eaf-bf1e-24653e690aa6' class='xr-var-data-in' type='checkbox'><label for='data-33b02433-de9b-4eaf-bf1e-24653e690aa6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>long_name :</span></dt><dd>Near-Surface Relative Humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>_ChunkSizes :</span></dt><dd>[168  50  50]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>huss</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-d7c56371-21a1-42c6-b31e-d7d32c62d336' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7c56371-21a1-42c6-b31e-d7d32c62d336' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a22ab17-8522-4734-955a-918030b24497' class='xr-var-data-in' type='checkbox'><label for='data-9a22ab17-8522-4734-955a-918030b24497' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>long_name :</span></dt><dd>Near-Surface Specific Humidity</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>_ChunkSizes :</span></dt><dd>[168  50  50]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-cf1d5861-667e-4d98-a862-f0a8b558025a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf1d5861-667e-4d98-a862-f0a8b558025a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efbc84e8-cc84-4850-8de6-5a4a069bfd96' class='xr-var-data-in' type='checkbox'><label for='data-efbc84e8-cc84-4850-8de6-5a4a069bfd96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>long_name :</span></dt><dd>Precipitaion</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>_ChunkSizes :</span></dt><dd>[168  50  50]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 29.88 GiB </td>\n",
       "                        <td> 3.81 MiB </td>\n",
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       "                        <td> (100, 100, 100) </td>\n",
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       "Thu Jul 14 15:35:31 2022: cdo setgrid,source.grid -selname,hurs /gpfs/users/milovacj/workdir/SAM-SESA/output/post/CORDEX-FPS-SESA/output/CSAM-4/UCAN/ECMWF-ERA5/evaluation/r1i1p1f1/UCAN-WRF433/v1/1hr//hurs//hurs_CSAM-4_ECMWF-ERA5_evaluation_r1i1p1f1_UCAN-WRF433_v1_1hr_2020010100-2021010100.nc modelData_setgrid.nc</dd><dt><span>institution :</span></dt><dd>Instituto de Fisica de Cantabria, CSIC-Universidad de Cantabria, SPAIN</dd><dt><span>creation_date :</span></dt><dd>2022-07-12-T16:54:19Z</dd><dt><span>experiment :</span></dt><dd>Evaluation run with ECMWF ERA5 reanalysis forcing</dd><dt><span>experiment_id :</span></dt><dd>FPS-SESA</dd><dt><span>driving_experiment :</span></dt><dd>ECMWF ERA5, evaluation</dd><dt><span>driving_model_id :</span></dt><dd>ECMWF-ERA5</dd><dt><span>driving_model_ensemble_member :</span></dt><dd>r1i1p1f1</dd><dt><span>driving_experiment_name :</span></dt><dd>evaluation</dd><dt><span>frequency :</span></dt><dd>1hr</dd><dt><span>institute_id :</span></dt><dd>UCAN</dd><dt><span>model_id :</span></dt><dd>UCAN-WRF433</dd><dt><span>rcm_version_id :</span></dt><dd>v1</dd><dt><span>project_id :</span></dt><dd>CORDEX-FPS-SESA</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>references :</span></dt><dd>https://ifca.unican.es/en-us</dd><dt><span>tracking_id :</span></dt><dd>457c6922-0203-11ed-811d-40f2e9035d1c</dd><dt><span>title :</span></dt><dd>WRF433 RCM model simulations forced with ERA5 reanalysis</dd><dt><span>comment :</span></dt><dd>Simulation performed at CESGA (https://www.cesga.es/), data postprocessed with WRF_CMORizer.f90 v0.41</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd><dt><span>CORDEX_domain :</span></dt><dd>CSAM-4i</dd><dt><span>contact :</span></dt><dd>meteo@unican.es</dd><dt><span>size :</span></dt><dd>2147483647</dd><dt><span>size_human :</span></dt><dd>142.0 GiB</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lon: 676, lat: 451, time: 26308, bnds: 2)\n",
       "Coordinates:\n",
       "  * lon        (lon) float64 -75.0 -74.96 -74.92 -74.88 ... -48.08 -48.04 -48.0\n",
       "  * lat        (lat) float64 -35.0 -34.96 -34.92 -34.88 ... -17.08 -17.04 -17.0\n",
       "  * time       (time) datetime64[ns] 2018-06-01 ... 2021-06-01\n",
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       "    pr         (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
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       "    sfcWind    (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
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       "    vas        (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
       "Attributes: (12/25)\n",
       "    Conventions:                    CF-1.8\n",
       "    history:                        Thu Jul 14 15:37:45 2022: cdo remap,desti...\n",
       "    institution:                    Instituto de Fisica de Cantabria, CSIC-Un...\n",
       "    creation_date:                  2022-07-12-T16:54:19Z\n",
       "    experiment:                     Evaluation run with ECMWF ERA5 reanalysis...\n",
       "    experiment_id:                  FPS-SESA\n",
       "    ...                             ...\n",
       "    comment:                        Simulation performed at CESGA (https://ww...\n",
       "    CDO:                            Climate Data Operators version 1.9.8 (htt...\n",
       "    CORDEX_domain:                  CSAM-4i\n",
       "    contact:                        meteo@unican.es\n",
       "    size:                           2147483647\n",
       "    size_human:                     142.0 GiB"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://data.meteo.unican.es/thredds/dodsC/CORDEX/fps/sesa/ncml/CORDEX/output/CSAM-4i/UCAN/ECMWF-ERA5/evaluation/r1i1p1f1/UCAN-WRF433/v1/1hr/v20220727/CORDEX_output_CSAM-4i_ECMWF-ERA5_evaluation_r1i1p1f1_UCAN-WRF433_v1_1hr_v20220727'\n",
    "ds = xr.open_dataset(url, chunks = dict(time = 100, lon = 100, lat = 100))\n",
    "ds"
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  },
  {
   "cell_type": "markdown",
   "id": "52bba1be-5c16-443e-bb27-f3378bf84386",
   "metadata": {},
   "source": [
    "This loaded the full dataset (3 years and all variables) information into `ds`. Variables can be accessed as attributes of this object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a515c00c-a8c6-43ed-a4ae-12bcae25a15f",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pr&#x27; (time: 26308, lat: 451, lon: 676)&gt;\n",
       "dask.array&lt;open_dataset-2c6864230a3776c76af2e7ce635dbd09pr, shape=(26308, 451, 676), dtype=float32, chunksize=(100, 100, 100), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 -75.0 -74.96 -74.92 -74.88 ... -48.08 -48.04 -48.0\n",
       "  * lat      (lat) float64 -35.0 -34.96 -34.92 -34.88 ... -17.08 -17.04 -17.0\n",
       "  * time     (time) datetime64[ns] 2018-06-01 2018-06-01T01:00:00 ... 2021-06-01\n",
       "Attributes:\n",
       "    standard_name:  precipitation_flux\n",
       "    long_name:      Precipitaion\n",
       "    units:          kg m-2 s-1\n",
       "    cell_methods:   time: mean\n",
       "    _ChunkSizes:    [168  50  50]</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'>'pr'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 26308</li><li><span class='xr-has-index'>lat</span>: 451</li><li><span class='xr-has-index'>lon</span>: 676</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-614039f5-1676-4778-9e0b-c29c3ad58330' class='xr-array-in' type='checkbox' checked><label for='section-614039f5-1676-4778-9e0b-c29c3ad58330' 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=(100, 100, 100), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 29.88 GiB </td>\n",
       "                        <td> 3.81 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (26308, 451, 676) </td>\n",
       "                        <td> (100, 100, 100) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
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       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-dc47431a-8b86-4b0f-a7d7-ffc7a6139ef4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc47431a-8b86-4b0f-a7d7-ffc7a6139ef4' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-75.0 -74.96 ... -48.04 -48.0</div><input id='attrs-93a18ee8-73cd-4259-826b-ead66b3f6117' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93a18ee8-73cd-4259-826b-ead66b3f6117' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-901cb8bd-a0c9-4c80-8742-4fcb7b713f3c' class='xr-var-data-in' type='checkbox'><label for='data-901cb8bd-a0c9-4c80-8742-4fcb7b713f3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_ChunkSizes :</span></dt><dd>676</dd></dl></div><div class='xr-var-data'><pre>array([-75.  , -74.96, -74.92, ..., -48.08, -48.04, -48.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-35.0 -34.96 ... -17.04 -17.0</div><input id='attrs-217883cb-6595-454d-b63f-b8b59fef3f70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-217883cb-6595-454d-b63f-b8b59fef3f70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6fe7d27-85e0-4b6c-aad8-bd64e5846fde' class='xr-var-data-in' type='checkbox'><label for='data-b6fe7d27-85e0-4b6c-aad8-bd64e5846fde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_ChunkSizes :</span></dt><dd>451</dd></dl></div><div class='xr-var-data'><pre>array([-35.  , -34.96, -34.92, ..., -17.08, -17.04, -17.  ])</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'>2018-06-01 ... 2021-06-01</div><input id='attrs-259253e7-b143-4eac-a685-4f0eed66bb04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-259253e7-b143-4eac-a685-4f0eed66bb04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8798ff9c-606f-45a2-9800-08ba03f0d6e8' class='xr-var-data-in' type='checkbox'><label for='data-8798ff9c-606f-45a2-9800-08ba03f0d6e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>_ChunkSizes :</span></dt><dd>8785</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-06-01T00:00:00.000000000&#x27;, &#x27;2018-06-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2018-06-01T02:00:00.000000000&#x27;, ..., &#x27;2021-05-31T22:00:00.000000000&#x27;,\n",
       "       &#x27;2021-05-31T23:00:00.000000000&#x27;, &#x27;2021-06-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4eb08637-9b2a-4f1a-9aed-e21b4fe0119a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4eb08637-9b2a-4f1a-9aed-e21b4fe0119a' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>long_name :</span></dt><dd>Precipitaion</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>_ChunkSizes :</span></dt><dd>[168  50  50]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'pr' (time: 26308, lat: 451, lon: 676)>\n",
       "dask.array<open_dataset-2c6864230a3776c76af2e7ce635dbd09pr, shape=(26308, 451, 676), dtype=float32, chunksize=(100, 100, 100), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 -75.0 -74.96 -74.92 -74.88 ... -48.08 -48.04 -48.0\n",
       "  * lat      (lat) float64 -35.0 -34.96 -34.92 -34.88 ... -17.08 -17.04 -17.0\n",
       "  * time     (time) datetime64[ns] 2018-06-01 2018-06-01T01:00:00 ... 2021-06-01\n",
       "Attributes:\n",
       "    standard_name:  precipitation_flux\n",
       "    long_name:      Precipitaion\n",
       "    units:          kg m-2 s-1\n",
       "    cell_methods:   time: mean\n",
       "    _ChunkSizes:    [168  50  50]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.pr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e05d0be0-67d3-4762-818a-863272ac10ce",
   "metadata": {},
   "source": [
    "This is how the common spatial domain looks like (this operation takes some time since some data needs to be retrieved):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a62d235b-4498-48ae-a309-c9e496aca43e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.27 s, sys: 1.15 s, total: 5.42 s\n",
      "Wall time: 1min 27s\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfea\n",
    "from matplotlib import cm\n",
    "\n",
    "p = ds.pr.sel({'time': '2019-01-02 18:00'}).plot(\n",
    "    transform = ccrs.PlateCarree(),\n",
    "    #subplot_kws = dict(projection=ccrs.Orthographic(-60, -20)),\n",
    "    subplot_kws = dict(projection=ccrs.PlateCarree()),\n",
    "    cmap = cm.viridis_r\n",
    ")\n",
    "fa = p.axes.add_feature(cfea.COASTLINE, linewidth=1)\n",
    "fa = p.axes.add_feature(cfea.BORDERS, linewidth=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1452c079-764d-4f19-aaf9-53d25af7d5f3",
   "metadata": {},
   "source": [
    "## Extract a single time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e4974b-0dcf-4d74-91e8-6cbf9310d2a3",
   "metadata": {},
   "source": [
    "Near Buenos Aires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2e828f4-4f1a-40c3-ac56-6b344c36d14c",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_location = dict(lon=-58.48, lat=-34.63)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cf8076-704a-4281-883f-8e5ccffae43f",
   "metadata": {},
   "source": [
    "Select the nearest grid point to the target location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6dd5882e-e96d-412c-a956-c13cb0673b04",
   "metadata": {},
   "outputs": [
    {
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       "  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",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\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",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\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",
       "  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;pr&#x27; (time: 26308)&gt;\n",
       "dask.array&lt;getitem, shape=(26308,), dtype=float32, chunksize=(100,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    lon      float64 -58.48\n",
       "    lat      float64 -34.64\n",
       "  * time     (time) datetime64[ns] 2018-06-01 2018-06-01T01:00:00 ... 2021-06-01\n",
       "Attributes:\n",
       "    standard_name:  precipitation_flux\n",
       "    long_name:      Precipitaion\n",
       "    units:          kg m-2 s-1\n",
       "    cell_methods:   time: mean\n",
       "    _ChunkSizes:    [168  50  50]</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'>'pr'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 26308</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-40d83eb6-b10a-468d-ad07-e42d7bce8d70' class='xr-array-in' type='checkbox' checked><label for='section-40d83eb6-b10a-468d-ad07-e42d7bce8d70' 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=(100,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 102.77 kiB </td>\n",
       "                        <td> 400 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (26308,) </td>\n",
       "                        <td> (100,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 9505 Tasks </td>\n",
       "                        <td> 264 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
       "  <line x1=\"57\" y1=\"0\" x2=\"57\" y2=\"25\" />\n",
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       "\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >26308</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-32b58827-ee48-4ac7-b7eb-79c7fed943a4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-32b58827-ee48-4ac7-b7eb-79c7fed943a4' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-58.48</div><input id='attrs-7c8f37be-5d90-4526-8111-5f5a2fcae452' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c8f37be-5d90-4526-8111-5f5a2fcae452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c60c52e3-c59d-467b-91b2-579e880ae704' class='xr-var-data-in' type='checkbox'><label for='data-c60c52e3-c59d-467b-91b2-579e880ae704' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_ChunkSizes :</span></dt><dd>676</dd></dl></div><div class='xr-var-data'><pre>array(-58.48)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-34.64</div><input id='attrs-839c4dec-9a98-41d6-85ed-c0501ed8dc7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-839c4dec-9a98-41d6-85ed-c0501ed8dc7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a67dea2-b5aa-4ed7-8351-c15f2c4d4ed0' class='xr-var-data-in' type='checkbox'><label for='data-3a67dea2-b5aa-4ed7-8351-c15f2c4d4ed0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_ChunkSizes :</span></dt><dd>451</dd></dl></div><div class='xr-var-data'><pre>array(-34.64)</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'>2018-06-01 ... 2021-06-01</div><input id='attrs-52ebbf45-1fd5-469e-8e14-50344fc2c670' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52ebbf45-1fd5-469e-8e14-50344fc2c670' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f48178f2-4add-4319-b49f-1e5f799260b2' class='xr-var-data-in' type='checkbox'><label for='data-f48178f2-4add-4319-b49f-1e5f799260b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>_ChunkSizes :</span></dt><dd>8785</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-06-01T00:00:00.000000000&#x27;, &#x27;2018-06-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2018-06-01T02:00:00.000000000&#x27;, ..., &#x27;2021-05-31T22:00:00.000000000&#x27;,\n",
       "       &#x27;2021-05-31T23:00:00.000000000&#x27;, &#x27;2021-06-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d94f0e85-4b2d-4379-8c66-715b27613893' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d94f0e85-4b2d-4379-8c66-715b27613893' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>long_name :</span></dt><dd>Precipitaion</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>_ChunkSizes :</span></dt><dd>[168  50  50]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'pr' (time: 26308)>\n",
       "dask.array<getitem, shape=(26308,), dtype=float32, chunksize=(100,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    lon      float64 -58.48\n",
       "    lat      float64 -34.64\n",
       "  * time     (time) datetime64[ns] 2018-06-01 2018-06-01T01:00:00 ... 2021-06-01\n",
       "Attributes:\n",
       "    standard_name:  precipitation_flux\n",
       "    long_name:      Precipitaion\n",
       "    units:          kg m-2 s-1\n",
       "    cell_methods:   time: mean\n",
       "    _ChunkSizes:    [168  50  50]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tseries = ds.pr.sel(target_location, method = 'nearest')\n",
    "tseries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860da1fe-2522-4d97-92ff-103e51bc6203",
   "metadata": {},
   "source": [
    "## Compute and plot the daily cycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8211affc-311e-448e-abab-ef5da6e80dc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.95 s, sys: 2.31 s, total: 11.3 s\n",
      "Wall time: 2min 56s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "dly_cycle = tseries.sel(time=tseries.time.dt.season == 'DJF').groupby('time.hour').mean(dim='time').compute()\n",
    "p = dly_cycle.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9ac029-0e13-441c-be4c-cb4a4517a0b4",
   "metadata": {},
   "source": [
    "## Load observations\n",
    "\n",
    "Load a virtual dataset prepared for the CORDEX FPS-SESA, interpolatd to the common CSAM-4i grid and aggregating times and variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "36844326-b739-4372-ac8f-6702e91e560b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'sfcWind' has multiple fill values {1e+20, '1.e+20f'}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'tas' has multiple fill values {1e+20, '1.e+20f'}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'hurs' has multiple fill values {1e+20, '1.e+20f'}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'pr' has multiple fill values {1e+20, '1.e+20f'}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'huss' has multiple fill values {1e+20, '1.e+20f'}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'rlds' has multiple fill values {1e+20, '1.e+20f'}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'rsds' has multiple fill values {1e+20, '1.e+20f'}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ps' has multiple fill values {1e+20, '1.e+20f'}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lon: 676, lat: 451, time: 11688)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 -75.0 -74.96 -74.92 -74.88 ... -48.08 -48.04 -48.0\n",
       "  * lat      (lat) float64 -35.0 -34.96 -34.92 -34.88 ... -17.08 -17.04 -17.0\n",
       "  * time     (time) datetime64[ns] 2018-01-01 ... 2021-12-31T21:00:00\n",
       "Data variables:\n",
       "    sfcWind  (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    tas      (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    hurs     (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    pr       (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    huss     (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    rlds     (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    rsds     (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "    ps       (time, lat, lon) float32 dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;\n",
       "Attributes: (12/15)\n",
       "    Conventions:    CF-1.6\n",
       "    creation_date:  2022-08-12-T11:21:56Z\n",
       "    CDO:            Climate Data Operators version 1.9.8 (https://mpimet.mpg....\n",
       "    institution:    GloH2O, Netherlands\n",
       "    frequency:      3hr\n",
       "    institute_id:   GloH2O\n",
       "    ...             ...\n",
       "    title:          MSWX high-resolution bias-corrected meteorological product\n",
       "    comment:        Global MSWX data interpolated to CSAM-4i grid using cdo v...\n",
       "    CORDEX_domain:  CSAM-4i\n",
       "    contact:        global MSWX data: support@gloh2o.org; interpolated: meteo...\n",
       "    size:           2147483647\n",
       "    size_human:     24.0 GiB</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-af5adb3b-df89-45a0-8880-0b5a0593dc17' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af5adb3b-df89-45a0-8880-0b5a0593dc17' 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'>lon</span>: 676</li><li><span class='xr-has-index'>lat</span>: 451</li><li><span class='xr-has-index'>time</span>: 11688</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2b22d45d-a1b4-491d-9656-a3d68a08d2ff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b22d45d-a1b4-491d-9656-a3d68a08d2ff' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-75.0 -74.96 ... -48.04 -48.0</div><input id='attrs-337ae9df-fcde-4d03-bbe5-6f83c6158cf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-337ae9df-fcde-4d03-bbe5-6f83c6158cf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e189ac4-256f-4ae9-8b48-61d23651da75' class='xr-var-data-in' type='checkbox'><label for='data-1e189ac4-256f-4ae9-8b48-61d23651da75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_ChunkSizes :</span></dt><dd>676</dd></dl></div><div class='xr-var-data'><pre>array([-75.  , -74.96, -74.92, ..., -48.08, -48.04, -48.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-35.0 -34.96 ... -17.04 -17.0</div><input id='attrs-668f6980-15a2-4b6f-a7fd-831bb4d523c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-668f6980-15a2-4b6f-a7fd-831bb4d523c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1f5cf5a-b76f-431a-bb5c-91a9e44a62ed' class='xr-var-data-in' type='checkbox'><label for='data-e1f5cf5a-b76f-431a-bb5c-91a9e44a62ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_ChunkSizes :</span></dt><dd>451</dd></dl></div><div class='xr-var-data'><pre>array([-35.  , -34.96, -34.92, ..., -17.08, -17.04, -17.  ])</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'>2018-01-01 ... 2021-12-31T21:00:00</div><input id='attrs-0f8f8cf0-07c8-4d08-9651-b5d36be35db8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f8f8cf0-07c8-4d08-9651-b5d36be35db8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-235cf65f-ed09-4eaa-b2d5-4fd085eec8a8' class='xr-var-data-in' type='checkbox'><label for='data-235cf65f-ed09-4eaa-b2d5-4fd085eec8a8' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>2928</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T03:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-01T06:00:00.000000000&#x27;, ..., &#x27;2021-12-31T15:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-31T18:00:00.000000000&#x27;, &#x27;2021-12-31T21:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41e4e71e-9edf-4ded-a1bd-af2595ab9321' class='xr-section-summary-in' type='checkbox'  checked><label for='section-41e4e71e-9edf-4ded-a1bd-af2595ab9321' class='xr-section-summary' >Data variables: <span>(8)</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>sfcWind</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-76e4319b-cfb4-4e98-8664-9e0346f6bfb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76e4319b-cfb4-4e98-8664-9e0346f6bfb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4b7851e-1c7e-40f5-9250-4d85ad6b8dab' class='xr-var-data-in' type='checkbox'><label for='data-f4b7851e-1c7e-40f5-9250-4d85ad6b8dab' 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>Near-Surface Wind Speed</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>_ChunkSizes :</span></dt><dd>[56 50 50]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "            <table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 13.27 GiB </td>\n",
       "                        <td> 3.81 MiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hurs</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-79423ee6-705a-4c03-a08b-e454c9855d5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79423ee6-705a-4c03-a08b-e454c9855d5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-191ecdcc-c843-4400-bab6-1dfb85206a3b' class='xr-var-data-in' type='checkbox'><label for='data-191ecdcc-c843-4400-bab6-1dfb85206a3b' 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>Near-Surface Relative Humidity</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>_ChunkSizes :</span></dt><dd>[56 50 50]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>huss</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100, 100), meta=np.ndarray&gt;</div><input id='attrs-8e1f64d6-fee6-414d-994f-3e0038c5021b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e1f64d6-fee6-414d-994f-3e0038c5021b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9542feec-67ab-43ca-a711-061f7345f3bf' class='xr-var-data-in' type='checkbox'><label for='data-9542feec-67ab-43ca-a711-061f7345f3bf' 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>Near-Surface Specific Humidity</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>_ChunkSizes :</span></dt><dd>[56 50 50]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "\n",
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       "  <text x=\"135.978027\" y=\"87.045254\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,135.978027,87.045254)\">451</text>\n",
       "  <text x=\"35.294118\" y=\"88.208155\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,88.208155)\">11688</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1cc96d2-4a80-47ed-8ce7-18edac6bf247' class='xr-section-summary-in' type='checkbox'  ><label for='section-d1cc96d2-4a80-47ed-8ce7-18edac6bf247' class='xr-section-summary' >Attributes: <span>(15)</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-1.6</dd><dt><span>creation_date :</span></dt><dd>2022-08-12-T11:21:56Z</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)</dd><dt><span>institution :</span></dt><dd>GloH2O, Netherlands</dd><dt><span>frequency :</span></dt><dd>3hr</dd><dt><span>institute_id :</span></dt><dd>GloH2O</dd><dt><span>project_id :</span></dt><dd>MSWX</dd><dt><span>product :</span></dt><dd>observations</dd><dt><span>references :</span></dt><dd>http://www.gloh2o.org/mswx/</dd><dt><span>title :</span></dt><dd>MSWX high-resolution bias-corrected meteorological product</dd><dt><span>comment :</span></dt><dd>Global MSWX data interpolated to CSAM-4i grid using cdo v1.9.8, conservative remapping method</dd><dt><span>CORDEX_domain :</span></dt><dd>CSAM-4i</dd><dt><span>contact :</span></dt><dd>global MSWX data: support@gloh2o.org; interpolated: meteo@unican.es</dd><dt><span>size :</span></dt><dd>2147483647</dd><dt><span>size_human :</span></dt><dd>24.0 GiB</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lon: 676, lat: 451, time: 11688)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 -75.0 -74.96 -74.92 -74.88 ... -48.08 -48.04 -48.0\n",
       "  * lat      (lat) float64 -35.0 -34.96 -34.92 -34.88 ... -17.08 -17.04 -17.0\n",
       "  * time     (time) datetime64[ns] 2018-01-01 ... 2021-12-31T21:00:00\n",
       "Data variables:\n",
       "    sfcWind  (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
       "    tas      (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
       "    hurs     (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
       "    pr       (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
       "    huss     (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
       "    rlds     (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
       "    rsds     (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
       "    ps       (time, lat, lon) float32 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>\n",
       "Attributes: (12/15)\n",
       "    Conventions:    CF-1.6\n",
       "    creation_date:  2022-08-12-T11:21:56Z\n",
       "    CDO:            Climate Data Operators version 1.9.8 (https://mpimet.mpg....\n",
       "    institution:    GloH2O, Netherlands\n",
       "    frequency:      3hr\n",
       "    institute_id:   GloH2O\n",
       "    ...             ...\n",
       "    title:          MSWX high-resolution bias-corrected meteorological product\n",
       "    comment:        Global MSWX data interpolated to CSAM-4i grid using cdo v...\n",
       "    CORDEX_domain:  CSAM-4i\n",
       "    contact:        global MSWX data: support@gloh2o.org; interpolated: meteo...\n",
       "    size:           2147483647\n",
       "    size_human:     24.0 GiB"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mswx_url = 'https://data.meteo.unican.es/thredds/dodsC/CORDEX/fps/sesa/mswx/CSAM-4i_GloH2O-MSWX_3hr'\n",
    "obs = xr.open_dataset(mswx_url, chunks = dict(time = 100, lon = 100, lat = 100))\n",
    "obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1edc1e16-f697-4f39-962b-7b9f5b1aa5aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/coding/variables.py:140: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  condition |= data == fv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.03 s, sys: 1.08 s, total: 5.11 s\n",
      "Wall time: 1min 22s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "obs_tseries = obs.pr.sel(target_location, method = 'nearest')\n",
    "obs_cycle = obs_tseries.sel(time=obs_tseries.time.dt.season == 'DJF').groupby('time.hour').mean(dim='time').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79eba4da-00eb-452c-a258-3740cd770fdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f = plt.figure(figsize=(12,6))\n",
    "ax = f.gca()\n",
    "dly_cycle.plot(ax = ax)\n",
    "obs_cycle.plot(ax = ax, color = 'k', linewidth = 2)\n",
    "l = f.legend(['UCAN-WRF433', 'MSWX'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "484e2983-4ae2-49f9-ab12-07b7954b608c",
   "metadata": {},
   "source": [
    "## Load ERA5\n",
    "\n",
    "We can also access ERA5 via this opendap server (ends in 2020):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1566c4f3-9f0f-4974-8218-25035d8dde0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --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.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",
       "  display: none;\n",
       "}\n",
       "\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",
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       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "  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",
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       "\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",
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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",
       "  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",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
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       "\n",
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       ".xr-array-in:checked ~ .xr-array-preview {\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "}\n",
       "\n",
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       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (time: 26304, latitude: 721, longitude: 1440)&gt;\n",
       "dask.array&lt;concatenate, shape=(26304, 721, 1440), dtype=float32, chunksize=(1000, 200, 200), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2018-01-01 ... 2020-12-31T23:00:00\n",
       "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "Attributes:\n",
       "    units:        m\n",
       "    long_name:    Total precipitation\n",
       "    _ChunkSizes:  [2928   25   50]</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'>'tp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 26304</li><li><span class='xr-has-index'>latitude</span>: 721</li><li><span class='xr-has-index'>longitude</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5cb0cc07-912b-4348-8450-fb236a93e6b5' class='xr-array-in' type='checkbox' checked><label for='section-5cb0cc07-912b-4348-8450-fb236a93e6b5' 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=(1000, 200, 200), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 101.74 GiB </td>\n",
       "                        <td> 152.59 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (26304, 721, 1440) </td>\n",
       "                        <td> (1000, 200, 200) </td>\n",
       "                    </tr>\n",
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       "                        <td> 864 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-ca09c3b7-8aa2-4b92-babb-46d923a3d520' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca09c3b7-8aa2-4b92-babb-46d923a3d520' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>2018-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-5dff7030-233b-418c-92b9-41d4f95d055d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dff7030-233b-418c-92b9-41d4f95d055d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47bfb41e-0b9c-4fe7-8c95-071a3438b01f' class='xr-var-data-in' type='checkbox'><label for='data-47bfb41e-0b9c-4fe7-8c95-071a3438b01f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><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'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-d1afefc1-9174-4921-98d1-fa338ee2faa3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1afefc1-9174-4921-98d1-fa338ee2faa3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03c30464-e890-4332-8c57-5e2c8f04cb4d' class='xr-var-data-in' type='checkbox'><label for='data-03c30464-e890-4332-8c57-5e2c8f04cb4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-9be30f4b-79b8-4839-a2d8-8d1ed6897c75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9be30f4b-79b8-4839-a2d8-8d1ed6897c75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-818b7fc5-441d-464d-9706-0243d9244219' class='xr-var-data-in' type='checkbox'><label for='data-818b7fc5-441d-464d-9706-0243d9244219' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9737cfdd-61af-44d1-815c-7053fee1a51d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9737cfdd-61af-44d1-815c-7053fee1a51d' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>_ChunkSizes :</span></dt><dd>[2928   25   50]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tp' (time: 26304, latitude: 721, longitude: 1440)>\n",
       "dask.array<concatenate, shape=(26304, 721, 1440), dtype=float32, chunksize=(1000, 200, 200), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2018-01-01 ... 2020-12-31T23:00:00\n",
       "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "Attributes:\n",
       "    units:        m\n",
       "    long_name:    Total precipitation\n",
       "    _ChunkSizes:  [2928   25   50]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "era5_urls = [\n",
    "  'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ecmwf/era5/pr_era5_reanalysis_hourly_2018.nc',\n",
    "  'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ecmwf/era5/pr_era5_reanalysis_hourly_2019.nc',\n",
    "  'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ecmwf/era5/pr_era5_reanalysis_hourly_2020.nc'\n",
    "]\n",
    "era5 = xr.open_mfdataset(era5_urls, chunks = dict(time = 1000, longitude = 200, latitude = 200))\n",
    "era5.tp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36f7e2e-2f2e-4f55-bdc7-75518ff88dea",
   "metadata": {},
   "source": [
    "Note the different coordinate names (`longitude` instead of `lon`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9970ccc-0198-4113-bade-ee08117b1f04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14.5 s, sys: 10.9 s, total: 25.4 s\n",
      "Wall time: 2min 9s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "target_location = dict(longitude=-58.48, latitude=-34.63)\n",
    "era5_tseries = era5.tp.sel(target_location, method = 'nearest')\n",
    "era5_cycle = era5_tseries.sel(time=era5_tseries.time.dt.season == 'DJF').groupby('time.hour').mean(dim='time').compute()\n",
    "era5_cycle = era5_cycle * 1000. / 3600. # m/h -> mm/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38a1b8a8-6e32-4bb4-a06b-46cc321e22ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(12,6))\n",
    "ax = f.gca()\n",
    "dly_cycle.plot(ax = ax)\n",
    "obs_cycle.plot(ax = ax, color = 'k', linewidth = 2)\n",
    "era5_cycle.plot(ax = ax, color = 'g', linewidth = 2)\n",
    "l = f.legend(['UCAN-WRF433', 'MSWX', 'ERA5'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}