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Load the ERA5 single-level reanalysis dataset from GCP
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| "## Load ERA5 single-level reanalysis from GCP cloud storage" | |
| ] | |
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| "Following the docs on\n", | |
| "* https://console.cloud.google.com/marketplace/product/bigquery-public-data/arco-era5\n", | |
| "* https://github.com/google-research/arco-era5/blob/main/docs/0-Surface-Reanalysis-Walkthrough.ipynb" | |
| ] | |
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| " pangeo-forge:inputs_hash: 5f4378143e9f42402424280b63472752da3aa79179b53b...\n", | |
| " pangeo-forge:recipe_hash: 0c3415923e347ce9dac9dc5c6d209525f4d45d799bd25b...\n", | |
| " pangeo-forge:version: 0.9.1</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-3e81a08d-f93e-4c71-91af-7448a7a24746' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3e81a08d-f93e-4c71-91af-7448a7a24746' 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>: 374016</li><li><span>values</span>: 542080</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c7b9b216-4d04-463a-adf5-a1cd23f8cad2' class='xr-section-summary-in' type='checkbox' checked><label for='section-c7b9b216-4d04-463a-adf5-a1cd23f8cad2' class='xr-section-summary' >Coordinates: <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>depthBelowLandLayer</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-5d6e7368-1476-45d7-a83f-f5ee71e26ad8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d6e7368-1476-45d7-a83f-f5ee71e26ad8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-195f1366-aa24-4478-a383-97dbbcb7395f' class='xr-var-data-in' type='checkbox'><label for='data-195f1366-aa24-4478-a383-97dbbcb7395f' 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>soil depth</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(100.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>entireAtmosphere</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-f1b9e4b6-4a6c-4e6b-9777-e357cb00b405' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1b9e4b6-4a6c-4e6b-9777-e357cb00b405' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5a76c0a-2337-4a20-afc8-633ded7aa1d3' class='xr-var-data-in' type='checkbox'><label for='data-f5a76c0a-2337-4a20-afc8-633ded7aa1d3' 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(entireAtmosphere)</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(values)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(542080,), meta=np.ndarray></div><input id='attrs-f39521ae-f5dd-4f61-87da-ce4c669ac60c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f39521ae-f5dd-4f61-87da-ce4c669ac60c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f91a274-146c-4ff3-a15f-6f9fdfbfbd78' class='xr-var-data-in' type='checkbox'><label for='data-0f91a274-146c-4ff3-a15f-6f9fdfbfbd78' 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'><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> 4.14 MiB </td>\n", | |
| " <td> 4.14 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (542080,) </td>\n", | |
| " <td> (542080,) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 1 Chunks </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Type </th>\n", | |
| " <td> float64 </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", | |
| " <!-- Horizontal lines -->\n", | |
| " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
| " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
| "\n", | |
| " <!-- Vertical lines -->\n", | |
| " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
| " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
| "\n", | |
| " <!-- Colored Rectangle -->\n", | |
| " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", | |
| "\n", | |
| " <!-- Text -->\n", | |
| " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</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></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(values)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(542080,), meta=np.ndarray></div><input id='attrs-8ad1a324-43f0-4366-8657-808175946c19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ad1a324-43f0-4366-8657-808175946c19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3e2f7f7-82ce-4efb-92ce-f5a90b7ab7ef' class='xr-var-data-in' type='checkbox'><label for='data-e3e2f7f7-82ce-4efb-92ce-f5a90b7ab7ef' 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'><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> 4.14 MiB </td>\n", | |
| " <td> 4.14 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (542080,) </td>\n", | |
| " <td> (542080,) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 1 Chunks </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Type </th>\n", | |
| " <td> float64 </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", | |
| " <!-- Horizontal lines -->\n", | |
| " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
| " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
| "\n", | |
| " <!-- Vertical lines -->\n", | |
| " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
| " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
| "\n", | |
| " <!-- Colored Rectangle -->\n", | |
| " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", | |
| "\n", | |
| " <!-- Text -->\n", | |
| " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</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></li><li class='xr-var-item'><div class='xr-var-name'><span>number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8aa5e9bb-74a8-4e91-8172-6ee16eb3347f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8aa5e9bb-74a8-4e91-8172-6ee16eb3347f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab705b2c-0b1b-4d8a-b71c-055e8ced917b' class='xr-var-data-in' type='checkbox'><label for='data-ab705b2c-0b1b-4d8a-b71c-055e8ced917b' 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>ensemble member numerical id</dd><dt><span>standard_name :</span></dt><dd>realization</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>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-4482be42-594b-4e90-87ae-bf66c011dee6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4482be42-594b-4e90-87ae-bf66c011dee6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea3d7471-8e2f-409d-80c5-fbb7d03e0d39' class='xr-var-data-in' type='checkbox'><label for='data-ea3d7471-8e2f-409d-80c5-fbb7d03e0d39' 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>array(0, 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-a290069f-f5fd-4d1c-95a2-21213dfa5607' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a290069f-f5fd-4d1c-95a2-21213dfa5607' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ddf0080-2847-434b-a503-a5677b3b48ff' class='xr-var-data-in' type='checkbox'><label for='data-2ddf0080-2847-434b-a503-a5677b3b48ff' 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>array(0.)</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'>1979-01-01 ... 2021-08-31T23:00:00</div><input id='attrs-962caa86-655c-423c-a9de-4bb71c9eb211' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-962caa86-655c-423c-a9de-4bb71c9eb211' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99f7e171-5d50-42ef-8254-717def5931d4' class='xr-var-data-in' type='checkbox'><label for='data-99f7e171-5d50-42ef-8254-717def5931d4' 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>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(['1979-01-01T00:00:00.000000000', '1979-01-01T01:00:00.000000000',\n", | |
| " '1979-01-01T02:00:00.000000000', ..., '2021-08-31T21:00:00.000000000',\n", | |
| " '2021-08-31T22:00:00.000000000', '2021-08-31T23:00:00.000000000'],\n", | |
| " dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_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'>dask.array<chunksize=(48,), meta=np.ndarray></div><input id='attrs-d46448d3-d6d7-405a-ba73-c8fb5b8b833d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d46448d3-d6d7-405a-ba73-c8fb5b8b833d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a38a7219-6ec5-4067-9c05-93dd2e3bbbd6' class='xr-var-data-in' type='checkbox'><label for='data-a38a7219-6ec5-4067-9c05-93dd2e3bbbd6' 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><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-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> 2.85 MiB </td>\n", | |
| " <td> 384 B </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016,) </td>\n", | |
| " <td> (48,) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 Chunks </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Type </th>\n", | |
| " <td> datetime64[ns] </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", | |
| " <!-- Horizontal lines -->\n", | |
| " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
| " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
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| " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
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| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c790360d-1e23-42db-b7db-76e926fab3d5' class='xr-section-summary-in' type='checkbox' ><label for='section-c790360d-1e23-42db-b7db-76e926fab3d5' class='xr-section-summary' >Data variables: <span>(38)</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>cape</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-0ea0fcb5-c767-44ca-8043-073af04801f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ea0fcb5-c767-44ca-8043-073af04801f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56a34c03-96be-43c2-9912-505678a54272' class='xr-var-data-in' type='checkbox'><label for='data-56a34c03-96be-43c2-9912-505678a54272' 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_N :</span></dt><dd>320</dd><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>cape</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Convective available potential energy</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>59</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>cape</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective available potential energy</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>J kg**-1</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>d2m</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-b16064a6-f9ba-46b6-804b-3d18f4186949' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b16064a6-f9ba-46b6-804b-3d18f4186949' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f79884a-4e35-463f-9b3c-1a9fe25ab15f' class='xr-var-data-in' type='checkbox'><label for='data-0f79884a-4e35-463f-9b3c-1a9fe25ab15f' 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_N :</span></dt><dd>320</dd><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>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</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_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>K</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", | |
| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</svg>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hcc</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-1d89b51d-7a79-4599-8352-d540d197689a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d89b51d-7a79-4599-8352-d540d197689a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9d7867b-6988-4bac-82f4-98b859fd58bf' class='xr-var-data-in' type='checkbox'><label for='data-e9d7867b-6988-4bac-82f4-98b859fd58bf' 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_N :</span></dt><dd>320</dd><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>hcc</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>High cloud cover</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>188</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 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432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>hcc</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>High cloud cover</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl1</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-f1f28d44-730d-4dfd-8e0a-7515795aa831' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1f28d44-730d-4dfd-8e0a-7515795aa831' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33882cfc-f3f8-4437-8be6-66c392de2265' class='xr-var-data-in' type='checkbox'><label for='data-33882cfc-f3f8-4437-8be6-66c392de2265' 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_N :</span></dt><dd>320</dd><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>istl1</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 1</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>35</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</svg>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl2</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-748bb386-306f-4011-90fa-995864eb6d7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-748bb386-306f-4011-90fa-995864eb6d7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3a77235-2dbd-4619-92dc-04d168b68707' class='xr-var-data-in' type='checkbox'><label for='data-f3a77235-2dbd-4619-92dc-04d168b68707' 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_N :</span></dt><dd>320</dd><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>istl2</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 2</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>36</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>istl2</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 2</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", | |
| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
| "\n", | |
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| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl3</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-8db78309-9627-4ae4-8256-c4c017e8168c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8db78309-9627-4ae4-8256-c4c017e8168c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1c2667f-94e1-4d2b-bacc-87634b2a877f' class='xr-var-data-in' type='checkbox'><label for='data-c1c2667f-94e1-4d2b-bacc-87634b2a877f' 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_N :</span></dt><dd>320</dd><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>istl3</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 3</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>37</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| " <text x=\"60.000000\" y=\"102.795750\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n", | |
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| "</svg>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl4</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-2019a6a7-b09c-49ac-a4d6-a0d7716c6aa1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2019a6a7-b09c-49ac-a4d6-a0d7716c6aa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86a2906b-0b87-409f-8515-dbb341184bb0' class='xr-var-data-in' type='checkbox'><label for='data-86a2906b-0b87-409f-8515-dbb341184bb0' 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_N :</span></dt><dd>320</dd><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>istl4</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 4</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>38</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>istl4</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 4</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", | |
| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
| "\n", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcc</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-e93d3bb8-019d-48c2-a422-cbf172068a07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e93d3bb8-019d-48c2-a422-cbf172068a07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ed17420-b225-4bda-a5a9-9a2c416ccfa7' class='xr-var-data-in' type='checkbox'><label for='data-5ed17420-b225-4bda-a5a9-9a2c416ccfa7' 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_N :</span></dt><dd>320</dd><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>lcc</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Low cloud cover</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>186</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 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432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>lcc</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Low cloud cover</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</svg>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mcc</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-2281c23c-7ac4-4a5a-8017-fb949b40cab5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2281c23c-7ac4-4a5a-8017-fb949b40cab5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa008035-ada6-4982-8c02-23d10da32c4c' class='xr-var-data-in' type='checkbox'><label for='data-fa008035-ada6-4982-8c02-23d10da32c4c' 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_N :</span></dt><dd>320</dd><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>mcc</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Medium cloud cover</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>187</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>mcc</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Medium cloud cover</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
| "\n", | |
| " <!-- Horizontal lines -->\n", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-46c463bc-1076-4c47-babd-b2bfb2c32d0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46c463bc-1076-4c47-babd-b2bfb2c32d0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a0a8184-a972-4cc6-b8c0-1c712ddd26d4' class='xr-var-data-in' type='checkbox'><label for='data-8a0a8184-a972-4cc6-b8c0-1c712ddd26d4' 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_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>GRIB_cfVarName :</span></dt><dd>msl</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>151</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 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| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p79.162</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-bbedcab8-d794-4d1b-8b68-1e0c1f2d03c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbedcab8-d794-4d1b-8b68-1e0c1f2d03c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39013596-a1a6-40f7-993d-23c88326bb80' class='xr-var-data-in' type='checkbox'><label for='data-39013596-a1a6-40f7-993d-23c88326bb80' 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_N :</span></dt><dd>320</dd><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>p79.162</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.785</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.785</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.719</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Vertical integral of divergence of cloud liquid water flux</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>162079</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 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| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p80.162</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-c1371005-3426-4809-84ad-4c03ce029a2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1371005-3426-4809-84ad-4c03ce029a2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1db8936f-14fc-458c-9008-d90dba827248' class='xr-var-data-in' type='checkbox'><label for='data-1db8936f-14fc-458c-9008-d90dba827248' 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_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName 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| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" 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", | |
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| " <line x1=\"0\" y1=\"74\" x2=\"120\" y2=\"74\" />\n", | |
| " <line x1=\"0\" y1=\"78\" x2=\"120\" y2=\"78\" />\n", | |
| " <line x1=\"0\" y1=\"82\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n", | |
| "\n", | |
| " <!-- Vertical lines -->\n", | |
| " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n", | |
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| "\n", | |
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| "\n", | |
| " <!-- Text -->\n", | |
| " <text x=\"60.000000\" y=\"102.795750\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n", | |
| " <text x=\"140.000000\" y=\"41.397875\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.397875)\">374016</text>\n", | |
| "</svg>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>siconc</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-966d9166-9925-423e-9025-e2b394f5c622' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-966d9166-9925-423e-9025-e2b394f5c622' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-138bf243-6a06-411e-95a4-c0f62af1a0e4' class='xr-var-data-in' type='checkbox'><label for='data-138bf243-6a06-411e-95a4-c0f62af1a0e4' 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_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>siconc</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>31</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>ci</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>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" 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=\"4\" x2=\"120\" y2=\"4\" />\n", | |
| " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", | |
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| " <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" />\n", | |
| " <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" />\n", | |
| " <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n", | |
| " <line x1=\"0\" y1=\"47\" x2=\"120\" y2=\"47\" />\n", | |
| " <line x1=\"0\" y1=\"52\" x2=\"120\" y2=\"52\" />\n", | |
| " <line x1=\"0\" y1=\"56\" x2=\"120\" y2=\"56\" />\n", | |
| " <line x1=\"0\" y1=\"61\" x2=\"120\" y2=\"61\" />\n", | |
| " <line x1=\"0\" y1=\"65\" x2=\"120\" y2=\"65\" />\n", | |
| " <line x1=\"0\" y1=\"69\" x2=\"120\" y2=\"69\" />\n", | |
| " <line x1=\"0\" y1=\"74\" x2=\"120\" y2=\"74\" />\n", | |
| " <line x1=\"0\" y1=\"78\" x2=\"120\" y2=\"78\" />\n", | |
| " <line x1=\"0\" y1=\"82\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n", | |
| "\n", | |
| " <!-- Vertical lines -->\n", | |
| " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n", | |
| " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n", | |
| "\n", | |
| " <!-- Colored Rectangle -->\n", | |
| " <polygon points=\"0.0,0.0 120.0,0.0 120.0,82.79574970484062 0.0,82.79574970484062\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n", | |
| "\n", | |
| " <!-- Text -->\n", | |
| " <text x=\"60.000000\" y=\"102.795750\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n", | |
| " <text x=\"140.000000\" y=\"41.397875\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.397875)\">374016</text>\n", | |
| "</svg>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-8a712d49-328e-4a4a-98f2-2d29df180bea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a712d49-328e-4a4a-98f2-2d29df180bea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c657cca6-6c5a-42c5-af3f-e204bb43c3f3' class='xr-var-data-in' type='checkbox'><label for='data-c657cca6-6c5a-42c5-af3f-e204bb43c3f3' 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_N :</span></dt><dd>320</dd><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>skt</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Skin temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>235</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>skt</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Skin 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", | |
| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-e28b4c98-379f-4487-a780-489c96789c7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e28b4c98-379f-4487-a780-489c96789c7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-254b13ff-817e-4972-b607-92c8a3b7065a' class='xr-var-data-in' type='checkbox'><label for='data-254b13ff-817e-4972-b607-92c8a3b7065a' 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_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_air_pressure</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sp</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Surface pressure</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>134</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>sp</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Surface pressure</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-cfe169b9-707b-41d7-9472-00709fe5a039' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfe169b9-707b-41d7-9472-00709fe5a039' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-527b885e-c740-4d06-bc85-2e07fa3937b4' class='xr-var-data-in' type='checkbox'><label for='data-527b885e-c740-4d06-bc85-2e07fa3937b4' 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_N :</span></dt><dd>320</dd><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>sst</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Sea surface temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>34</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 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432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>sst</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>K</dd><dt><span>long_name :</span></dt><dd>Sea surface 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", | |
| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-538a1231-ce0f-4feb-96f4-b937d893ad5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-538a1231-ce0f-4feb-96f4-b937d893ad5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb70e2b6-6e15-4cc3-828d-1d06ef6d2218' class='xr-var-data-in' type='checkbox'><label for='data-eb70e2b6-6e15-4cc3-828d-1d06ef6d2218' 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_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl1</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 1</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>139</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>stl1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 1</dd><dt><span>standard_name :</span></dt><dd>surface_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-b517e0c1-9aa6-45bb-af5e-2879678273dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b517e0c1-9aa6-45bb-af5e-2879678273dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-909b99db-7780-4cf4-8094-8d5c8525ec10' class='xr-var-data-in' type='checkbox'><label for='data-909b99db-7780-4cf4-8094-8d5c8525ec10' 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_N :</span></dt><dd>320</dd><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>stl2</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 2</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>170</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>stl2</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 2</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", | |
| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-cc6ccc45-b82d-4e12-80b7-7e6ad143debe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc6ccc45-b82d-4e12-80b7-7e6ad143debe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6939c719-a73c-49b6-9d63-fdc6267c3569' class='xr-var-data-in' type='checkbox'><label for='data-6939c719-a73c-49b6-9d63-fdc6267c3569' 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_N :</span></dt><dd>320</dd><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>stl3</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 3</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>183</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>stl3</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 3</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", | |
| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-f4ec7b00-d606-46a2-aff5-c6eb0a43857d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4ec7b00-d606-46a2-aff5-c6eb0a43857d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d553488-cb59-4f49-8467-23deac851c7e' class='xr-var-data-in' type='checkbox'><label for='data-6d553488-cb59-4f49-8467-23deac851c7e' 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_N :</span></dt><dd>320</dd><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>stl4</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 4</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>236</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-95f0fe56-f90a-4385-ae12-b2a8cfc0f416' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95f0fe56-f90a-4385-ae12-b2a8cfc0f416' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a12de288-af9b-41bd-a828-b0ec83e4ad5e' class='xr-var-data-in' type='checkbox'><label for='data-a12de288-af9b-41bd-a828-b0ec83e4ad5e' 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_N :</span></dt><dd>320</dd><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>swvl1</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>39</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
| "\n", | |
| " <!-- Horizontal lines -->\n", | |
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| " <text x=\"140.000000\" y=\"41.397875\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.397875)\">374016</text>\n", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-0803d501-3aaf-4e96-9201-88035bcbba88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0803d501-3aaf-4e96-9201-88035bcbba88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee6aef1c-3723-4bed-9bb8-65013e6abb4a' class='xr-var-data-in' type='checkbox'><label for='data-ee6aef1c-3723-4bed-9bb8-65013e6abb4a' 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_N :</span></dt><dd>320</dd><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>swvl2</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>40</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl2</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-40a9085e-6824-461f-86af-b87094601c6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40a9085e-6824-461f-86af-b87094601c6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62abeda2-c345-4f71-9b12-d97b4fb0d480' class='xr-var-data-in' type='checkbox'><label for='data-62abeda2-c345-4f71-9b12-d97b4fb0d480' 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_N :</span></dt><dd>320</dd><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>swvl3</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>41</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl3</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
| "\n", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-223fc80a-3f7d-4059-8c17-c964f1172f73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-223fc80a-3f7d-4059-8c17-c964f1172f73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29ec6007-2ff4-496f-a841-7ffe428ed581' class='xr-var-data-in' type='checkbox'><label for='data-29ec6007-2ff4-496f-a841-7ffe428ed581' 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_N :</span></dt><dd>320</dd><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>swvl4</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>42</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl4</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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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, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-ef835136-a4f1-4c63-9544-55d3d786ad84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef835136-a4f1-4c63-9544-55d3d786ad84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f045cd65-e8c9-4be7-ba07-e043508702e6' class='xr-var-data-in' type='checkbox'><label for='data-f045cd65-e8c9-4be7-ba07-e043508702e6' 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_N :</span></dt><dd>320</dd><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>t2m</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 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432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</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_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre 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", | |
| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
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| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-1881b5fb-93b1-40e1-8619-67969cccf90e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1881b5fb-93b1-40e1-8619-67969cccf90e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4fe6b7f-47bc-454e-afb1-d949dff796b7' class='xr-var-data-in' type='checkbox'><label for='data-d4fe6b7f-47bc-454e-afb1-d949dff796b7' 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_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>cloud_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcc</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total cloud cover</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>164</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>tcc</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Total cloud cover</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-4d2c8ca8-f1b7-4d35-952b-ebd7b81ea4ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d2c8ca8-f1b7-4d35-952b-ebd7b81ea4ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e9224d4-a07e-4d45-9096-52077aaf0549' class='xr-var-data-in' type='checkbox'><label for='data-6e9224d4-a07e-4d45-9096-52077aaf0549' 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_N :</span></dt><dd>320</dd><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>tciw</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud ice water</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>79</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| " <text x=\"140.000000\" y=\"41.397875\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.397875)\">374016</text>\n", | |
| "</svg>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-81002f84-1884-46cd-a71d-885dcc37b005' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81002f84-1884-46cd-a71d-885dcc37b005' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34a61570-aaad-4bc8-90fb-04f242f194f3' class='xr-var-data-in' type='checkbox'><label for='data-34a61570-aaad-4bc8-90fb-04f242f194f3' 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_N :</span></dt><dd>320</dd><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>tclw</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud liquid water</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>78</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>tclw</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud liquid water</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcrw</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-1bc30eac-e14b-4326-9045-ec569ea735fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bc30eac-e14b-4326-9045-ec569ea735fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2b0e16b-8f59-4b13-8346-a2169d0859e8' class='xr-var-data-in' type='checkbox'><label for='data-c2b0e16b-8f59-4b13-8346-a2169d0859e8' 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_N :</span></dt><dd>320</dd><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>tcrw</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column rain water</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>228089</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "\n", | |
| " <!-- Text -->\n", | |
| " <text x=\"60.000000\" y=\"102.795750\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n", | |
| " <text x=\"140.000000\" y=\"41.397875\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.397875)\">374016</text>\n", | |
| "</svg>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcsw</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-bfbaa24c-9480-4641-a637-9f99478fd450' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfbaa24c-9480-4641-a637-9f99478fd450' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbf965bc-063c-4a48-a20e-eee55a60f40d' class='xr-var-data-in' type='checkbox'><label for='data-dbf965bc-063c-4a48-a20e-eee55a60f40d' 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_N :</span></dt><dd>320</dd><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>tcsw</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column snow water</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>228090</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 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450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>tcsw</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column snow water</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
| "\n", | |
| " <!-- Horizontal lines -->\n", | |
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| " <text x=\"140.000000\" y=\"41.397875\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.397875)\">374016</text>\n", | |
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| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcw</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-bd916743-b0ab-4e10-9f1b-8c1bcfa83f0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd916743-b0ab-4e10-9f1b-8c1bcfa83f0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32cc7818-a3a2-4f98-a5c4-7ae4b5a7672e' class='xr-var-data-in' type='checkbox'><label for='data-32cc7818-a3a2-4f98-a5c4-7ae4b5a7672e' 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_N :</span></dt><dd>320</dd><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>tcw</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column water</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>136</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 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432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>tcw</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column water</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcwv</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-871ec250-2ed6-432d-ac9a-5a20674b0f79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-871ec250-2ed6-432d-ac9a-5a20674b0f79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee8c087b-2651-42fd-9780-0d986acd12c5' class='xr-var-data-in' type='checkbox'><label for='data-ee8c087b-2651-42fd-9780-0d986acd12c5' 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_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcwv</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Total column water vapour</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>137</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 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| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-73b6fab0-2737-45b9-9ede-a5b6b9704323' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73b6fab0-2737-45b9-9ede-a5b6b9704323' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f718409-734d-4bcf-819f-b944047b8d84' class='xr-var-data-in' type='checkbox'><label for='data-7f718409-734d-4bcf-819f-b944047b8d84' 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_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName 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| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 Chunks </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Type </th>\n", | |
| " <td> float32 </td>\n", | |
| " <td> numpy.ndarray </td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
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| " </td>\n", | |
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| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u10</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-f84988e3-be1e-4471-88c3-0e44dec58272' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f84988e3-be1e-4471-88c3-0e44dec58272' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11a11785-383a-4ac5-8be9-628429d9eea4' class='xr-var-data-in' type='checkbox'><label for='data-11a11785-383a-4ac5-8be9-628429d9eea4' 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_N :</span></dt><dd>320</dd><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>u10</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>10 metre U wind component</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>165</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>10u</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>standard_name :</span></dt><dd>unknown</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>\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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u100</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-980d1ed2-be29-4e77-be58-714e82ac10c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-980d1ed2-be29-4e77-be58-714e82ac10c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ed9a86a-e3a3-4ec0-9226-ab091c25cf33' class='xr-var-data-in' type='checkbox'><label for='data-5ed9a86a-e3a3-4ec0-9226-ab091c25cf33' 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_N :</span></dt><dd>320</dd><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>u100</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>100 metre U wind component</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>228246</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>100u</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>100 metre U wind component</dd><dt><span>standard_name :</span></dt><dd>unknown</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>\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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v10</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-6b8dec3b-149c-4931-b2ae-87c15958737b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b8dec3b-149c-4931-b2ae-87c15958737b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4224ef4a-c3f2-4f80-b0bf-58a22cc6a1fe' class='xr-var-data-in' type='checkbox'><label for='data-4224ef4a-c3f2-4f80-b0bf-58a22cc6a1fe' 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_N :</span></dt><dd>320</dd><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>v10</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>10 metre V wind component</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>166</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 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| " <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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v100</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-0a74e737-52ce-4825-bd11-56cb6ebcf888' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a74e737-52ce-4825-bd11-56cb6ebcf888' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a62e915f-c81f-4647-b283-3bab38927143' class='xr-var-data-in' type='checkbox'><label for='data-a62e915f-c81f-4647-b283-3bab38927143' 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_N :</span></dt><dd>320</dd><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>v100</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>100 metre V wind component</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>228247</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 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1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>100v</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>100 metre V wind component</dd><dt><span>standard_name :</span></dt><dd>unknown</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>\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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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", | |
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| " <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n", | |
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| " <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n", | |
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| " <line x1=\"0\" y1=\"56\" x2=\"120\" y2=\"56\" />\n", | |
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| " <line x1=\"0\" y1=\"65\" x2=\"120\" y2=\"65\" />\n", | |
| " <line x1=\"0\" y1=\"69\" x2=\"120\" y2=\"69\" />\n", | |
| " <line x1=\"0\" y1=\"74\" x2=\"120\" y2=\"74\" />\n", | |
| " <line x1=\"0\" y1=\"78\" x2=\"120\" y2=\"78\" />\n", | |
| " <line x1=\"0\" y1=\"82\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n", | |
| "\n", | |
| " <!-- Vertical lines -->\n", | |
| " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n", | |
| " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n", | |
| "\n", | |
| " <!-- Colored Rectangle -->\n", | |
| " <polygon points=\"0.0,0.0 120.0,0.0 120.0,82.79574970484062 0.0,82.79574970484062\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n", | |
| "\n", | |
| " <!-- Text -->\n", | |
| " <text x=\"60.000000\" y=\"102.795750\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n", | |
| " <text x=\"140.000000\" y=\"41.397875\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.397875)\">374016</text>\n", | |
| "</svg>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, values)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(48, 542080), meta=np.ndarray></div><input id='attrs-34c08c18-16e5-473f-b987-f6baf614ce0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34c08c18-16e5-473f-b987-f6baf614ce0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffd8520d-73a5-4403-8aba-2bb0c3f6fe06' class='xr-var-data-in' type='checkbox'><label for='data-ffd8520d-73a5-4403-8aba-2bb0c3f6fe06' 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_N :</span></dt><dd>320</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_dataType :</span></dt><dd>an</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_latitudeOfFirstGridPointInDegrees :</span></dt><dd>89.784</dd><dt><span>GRIB_latitudeOfLastGridPointInDegrees :</span></dt><dd>-89.784</dd><dt><span>GRIB_longitudeOfFirstGridPointInDegrees :</span></dt><dd>0.0</dd><dt><span>GRIB_longitudeOfLastGridPointInDegrees :</span></dt><dd>359.718</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>542080</dd><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_pl :</span></dt><dd>[18, 25, 36, 40, 45, 50, 60, 64, 72, 72, 75, 81, 90, 96, 100, 108, 120, 120, 125, 135, 144, 144, 150, 160, 180, 180, 180, 192, 192, 200, 216, 216, 216, 225, 240, 240, 240, 250, 256, 270, 270, 288, 288, 288, 300, 300, 320, 320, 320, 324, 360, 360, 360, 360, 360, 360, 375, 375, 384, 384, 400, 400, 405, 432, 432, 432, 432, 450, 450, 450, 480, 480, 480, 480, 480, 486, 500, 500, 500, 512, 512, 540, 540, 540, 540, 540, 576, 576, 576, 576, 576, 576, 600, 600, 600, 600, 640, 640, 640, 640, 640, 640, 640, 648, 648, 675, 675, 675, 675, 720, 720, 720, 720, 720, 720, 720, 720, 720, 729, 750, 750, 750, 750, 768, 768, 768, 768, 800, 800, 800, 800, 800, 800, 810, 810, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 900, 900, 900, 900, 900, 900, 900, 900, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 972, 972, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1024, 1024, 1024, 1024, 1024, 1024, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1200, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1152, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1125, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1080, 1024, 1024, 1024, 1024, 1024, 1024, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 972, 972, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 900, 900, 900, 900, 900, 900, 900, 900, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 864, 810, 810, 800, 800, 800, 800, 800, 800, 768, 768, 768, 768, 750, 750, 750, 750, 729, 720, 720, 720, 720, 720, 720, 720, 720, 720, 675, 675, 675, 675, 648, 648, 640, 640, 640, 640, 640, 640, 640, 600, 600, 600, 600, 576, 576, 576, 576, 576, 576, 540, 540, 540, 540, 540, 512, 512, 500, 500, 500, 486, 480, 480, 480, 480, 480, 450, 450, 450, 432, 432, 432, 432, 405, 400, 400, 384, 384, 375, 375, 360, 360, 360, 360, 360, 360, 324, 320, 320, 320, 300, 300, 288, 288, 288, 270, 270, 256, 250, 240, 240, 240, 225, 216, 216, 216, 200, 192, 192, 180, 180, 180, 160, 150, 144, 144, 135, 125, 120, 120, 108, 100, 96, 90, 81, 75, 72, 72, 64, 60, 50, 45, 40, 36, 25, 18]</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd></dl></div><div class='xr-var-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> 755.29 GiB </td>\n", | |
| " <td> 99.26 MiB </td>\n", | |
| " </tr>\n", | |
| " \n", | |
| " <tr>\n", | |
| " <th> Shape </th>\n", | |
| " <td> (374016, 542080) </td>\n", | |
| " <td> (48, 542080) </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th> Count </th>\n", | |
| " <td> 2 Graph Layers </td>\n", | |
| " <td> 7792 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=\"132\" 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=\"4\" x2=\"120\" y2=\"4\" />\n", | |
| " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", | |
| " <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n", | |
| " <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n", | |
| " <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n", | |
| " <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" />\n", | |
| " <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n", | |
| " <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" />\n", | |
| " <line x1=\"0\" y1=\"39\" x2=\"120\" y2=\"39\" />\n", | |
| " <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" />\n", | |
| " <line x1=\"0\" y1=\"47\" x2=\"120\" y2=\"47\" />\n", | |
| " <line x1=\"0\" y1=\"52\" x2=\"120\" y2=\"52\" />\n", | |
| " <line x1=\"0\" y1=\"56\" x2=\"120\" y2=\"56\" />\n", | |
| " <line x1=\"0\" y1=\"61\" x2=\"120\" y2=\"61\" />\n", | |
| " <line x1=\"0\" y1=\"65\" x2=\"120\" y2=\"65\" />\n", | |
| " <line x1=\"0\" y1=\"69\" x2=\"120\" y2=\"69\" />\n", | |
| " <line x1=\"0\" y1=\"74\" x2=\"120\" y2=\"74\" />\n", | |
| " <line x1=\"0\" y1=\"78\" x2=\"120\" y2=\"78\" />\n", | |
| " <line x1=\"0\" y1=\"82\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n", | |
| "\n", | |
| " <!-- Vertical lines -->\n", | |
| " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n", | |
| " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n", | |
| "\n", | |
| " <!-- Colored Rectangle -->\n", | |
| " <polygon points=\"0.0,0.0 120.0,0.0 120.0,82.79574970484062 0.0,82.79574970484062\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n", | |
| "\n", | |
| " <!-- Text -->\n", | |
| " <text x=\"60.000000\" y=\"102.795750\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >542080</text>\n", | |
| " <text x=\"140.000000\" y=\"41.397875\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.397875)\">374016</text>\n", | |
| "</svg>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-18df589e-17ab-45ad-85d1-ba64bda5fa55' class='xr-section-summary-in' type='checkbox' ><label for='section-18df589e-17ab-45ad-85d1-ba64bda5fa55' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>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>1</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>2022-09-23T18:56 GRIB to CDM+CF via cfgrib-0.9.10.1/ecCodes-2.20.0 with {"source": "tmp/tmp2vvg8mzi.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>pangeo-forge:inputs_hash :</span></dt><dd>5f4378143e9f42402424280b63472752da3aa79179b53b71401ee0ecf2fdc24e</dd><dt><span>pangeo-forge:recipe_hash :</span></dt><dd>0c3415923e347ce9dac9dc5c6d209525f4d45d799bd25b04bf2adf398bfc83bb</dd><dt><span>pangeo-forge:version :</span></dt><dd>0.9.1</dd></dl></div></li></ul></div></div>" | |
| ], | |
| "text/plain": [ | |
| "<xarray.Dataset>\n", | |
| "Dimensions: (time: 374016, values: 542080)\n", | |
| "Coordinates:\n", | |
| " depthBelowLandLayer float64 ...\n", | |
| " entireAtmosphere float64 ...\n", | |
| " latitude (values) float64 dask.array<chunksize=(542080,), meta=np.ndarray>\n", | |
| " longitude (values) float64 dask.array<chunksize=(542080,), meta=np.ndarray>\n", | |
| " number int64 ...\n", | |
| " step timedelta64[ns] ...\n", | |
| " surface float64 ...\n", | |
| " * time (time) datetime64[ns] 1979-01-01 ... 2021-08-31T23:0...\n", | |
| " valid_time (time) datetime64[ns] dask.array<chunksize=(48,), meta=np.ndarray>\n", | |
| "Dimensions without coordinates: values\n", | |
| "Data variables: (12/38)\n", | |
| " cape (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| " d2m (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| " hcc (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| " istl1 (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| " istl2 (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| " istl3 (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| " ... ...\n", | |
| " tsn (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| " u10 (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| " u100 (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| " v10 (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| " v100 (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| " z (time, values) float32 dask.array<chunksize=(48, 542080), meta=np.ndarray>\n", | |
| "Attributes:\n", | |
| " Conventions: CF-1.7\n", | |
| " GRIB_centre: ecmf\n", | |
| " GRIB_centreDescription: European Centre for Medium-Range Weather Forec...\n", | |
| " GRIB_edition: 1\n", | |
| " GRIB_subCentre: 0\n", | |
| " history: 2022-09-23T18:56 GRIB to CDM+CF via cfgrib-0.9...\n", | |
| " institution: European Centre for Medium-Range Weather Forec...\n", | |
| " pangeo-forge:inputs_hash: 5f4378143e9f42402424280b63472752da3aa79179b53b...\n", | |
| " pangeo-forge:recipe_hash: 0c3415923e347ce9dac9dc5c6d209525f4d45d799bd25b...\n", | |
| " pangeo-forge:version: 0.9.1" | |
| ] | |
| }, | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "import xarray\n", | |
| "\n", | |
| "single_reanalysis = xarray.open_zarr(\n", | |
| " \"gs://gcp-public-data-arco-era5/co/single-level-reanalysis.zarr\",\n", | |
| " chunks={'time': 48}, \n", | |
| " consolidated=True,\n", | |
| ")\n", | |
| "single_reanalysis" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "5c8a762d", | |
| "metadata": {}, | |
| "source": [ | |
| "## List all the data variables and their long names" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "da112b10", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "('cape', 'Convective available potential energy')\n", | |
| "('d2m', '2 metre dewpoint temperature')\n", | |
| "('hcc', 'High cloud cover')\n", | |
| "('istl1', 'Ice temperature layer 1')\n", | |
| "('istl2', 'Ice temperature layer 2')\n", | |
| "('istl3', 'Ice temperature layer 3')\n", | |
| "('istl4', 'Ice temperature layer 4')\n", | |
| "('lcc', 'Low cloud cover')\n", | |
| "('mcc', 'Medium cloud cover')\n", | |
| "('msl', 'Mean sea level pressure')\n", | |
| "('p79.162', 'Vertical integral of divergence of cloud liquid water flux')\n", | |
| "('p80.162', 'Vertical integral of divergence of cloud frozen water flux')\n", | |
| "('siconc', 'Sea ice area fraction')\n", | |
| "('skt', 'Skin temperature')\n", | |
| "('sp', 'Surface pressure')\n", | |
| "('sst', 'Sea surface temperature')\n", | |
| "('stl1', 'Soil temperature level 1')\n", | |
| "('stl2', 'Soil temperature level 2')\n", | |
| "('stl3', 'Soil temperature level 3')\n", | |
| "('stl4', 'Soil temperature level 4')\n", | |
| "('swvl1', 'Volumetric soil water layer 1')\n", | |
| "('swvl2', 'Volumetric soil water layer 2')\n", | |
| "('swvl3', 'Volumetric soil water layer 3')\n", | |
| "('swvl4', 'Volumetric soil water layer 4')\n", | |
| "('t2m', '2 metre temperature')\n", | |
| "('tcc', 'Total cloud cover')\n", | |
| "('tciw', 'Total column cloud ice water')\n", | |
| "('tclw', 'Total column cloud liquid water')\n", | |
| "('tcrw', 'Total column rain water')\n", | |
| "('tcsw', 'Total column snow water')\n", | |
| "('tcw', 'Total column water')\n", | |
| "('tcwv', 'Total column water vapour')\n", | |
| "('tsn', 'Temperature of snow layer')\n", | |
| "('u10', '10 metre U wind component')\n", | |
| "('u100', '100 metre U wind component')\n", | |
| "('v10', '10 metre V wind component')\n", | |
| "('v100', '100 metre V wind component')\n", | |
| "('z', 'Geopotential')\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for name in single_reanalysis.data_vars.keys():\n", | |
| " print((name, single_reanalysis[name].attrs[\"long_name\"]))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "b2609547", | |
| "metadata": {}, | |
| "source": [ | |
| "## Select a variable and time slice (last available)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "id": "7ae1c2d4", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "da = single_reanalysis[\"t2m\"].isel(time=-1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "0ef80dbc", | |
| "metadata": {}, | |
| "source": [ | |
| "## Plot data on Gaussian grid with scatter" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "id": "877f3991", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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