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willirath/2023-11-16_subsampling_NEMO_along_altimetry_tracks.ipynb

Created November 16, 2023 15:13
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Along Track Sampling NEMO
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2023-11-16_subsampling_NEMO_along_altimetry_tracks.ipynb
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"# %pip install xoak"
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"import xarray as xr"
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"Downloaded along-track data from CMEMS:\n",
"https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_L3_MY_008_062/description"
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (time: 632980)\n",
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" * time (time) datetime64[ns] 2002-04-30T23:58:22.856968704 ... 2...\n",
" longitude (time) float64 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" latitude (time) float64 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
"Data variables:\n",
" cycle (time) int16 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" track (time) int16 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
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" sla_filtered (time) float32 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" dac (time) float32 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" ocean_tide (time) float32 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" internal_tide (time) float32 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" lwe (time) float32 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" mdt (time) float32 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" tpa_correction (time) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
"Attributes: (12/44)\n",
" Conventions: CF-1.6\n",
" Metadata_Conventions: Unidata Dataset Discovery v1.0\n",
" cdm_data_type: Swath\n",
" comment: Sea surface height measured by altimeter...\n",
" contact: [email protected]\n",
" creator_email: [email protected]\n",
" ... ...\n",
" summary: SSALTO/DUACS Delayed-Time Level-3 sea su...\n",
" time_coverage_duration: P23H14M47.18179S\n",
" time_coverage_end: 2002-05-01T23:13:09Z\n",
" time_coverage_resolution: P1S\n",
" time_coverage_start: 2002-04-30T23:58:22Z\n",
" title: DT ERS-2 Global Ocean Along track SSALTO...</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-4a0c67e2-bd50-4cf6-9a20-1ae91879bd7d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4a0c67e2-bd50-4cf6-9a20-1ae91879bd7d' 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>: 632980</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5df4e298-ae15-4733-bc3b-6c9e37695809' class='xr-section-summary-in' type='checkbox' checked><label for='section-5df4e298-ae15-4733-bc3b-6c9e37695809' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2002-04-30T23:58:22.856968704 .....</div><input id='attrs-018e7373-fff2-4172-8c31-ccf219e82107' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-018e7373-fff2-4172-8c31-ccf219e82107' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2565f48-171e-4e8a-bef7-b73adb882bd1' class='xr-var-data-in' type='checkbox'><label for='data-c2565f48-171e-4e8a-bef7-b73adb882bd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Time of measurement</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2002-04-30T23:58:22.856968704&#x27;, &#x27;2002-04-30T23:58:27.756968448&#x27;,\n",
" &#x27;2002-04-30T23:58:28.736968704&#x27;, ..., &#x27;2002-05-14T18:04:51.273067776&#x27;,\n",
" &#x27;2002-05-14T18:04:52.253067776&#x27;, &#x27;2002-05-14T18:04:55.193067520&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-817223c5-1088-4337-b42f-c106fe4e8e43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-817223c5-1088-4337-b42f-c106fe4e8e43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63d2ae01-7857-4cfb-888b-c034507508f6' class='xr-var-data-in' type='checkbox'><label for='data-63d2ae01-7857-4cfb-888b-c034507508f6' 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 of measurement</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",
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" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-41163524-993c-4ce9-9fbe-1d6b2fc86036' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41163524-993c-4ce9-9fbe-1d6b2fc86036' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78973fa3-dd7d-41c7-b3ac-9797c7a647b0' class='xr-var-data-in' type='checkbox'><label for='data-78973fa3-dd7d-41c7-b3ac-9797c7a647b0' 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 of measurement</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 style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 4.83 MiB </td>\n",
" <td> 377.74 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
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" </td>\n",
" </tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7d24dae-97d2-40d3-a22d-0ade5d76e351' class='xr-section-summary-in' type='checkbox' checked><label for='section-a7d24dae-97d2-40d3-a22d-0ade5d76e351' class='xr-section-summary' >Data variables: <span>(10)</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>cycle</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-6dda20f4-f275-4a0c-9f6d-d2fe708e33d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6dda20f4-f275-4a0c-9f6d-d2fe708e33d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa02ae95-ca1a-48c8-ae30-7c5b6007a32b' class='xr-var-data-in' type='checkbox'><label for='data-fa02ae95-ca1a-48c8-ae30-7c5b6007a32b' 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>Cycle the measurement belongs to</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.21 MiB </td>\n",
" <td> 94.44 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> int16 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>track</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-cf999ff3-1037-4bc0-8f8d-6b581c9fcf57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf999ff3-1037-4bc0-8f8d-6b581c9fcf57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47702242-c4ba-4404-b86d-8d7c2256752c' class='xr-var-data-in' type='checkbox'><label for='data-47702242-c4ba-4404-b86d-8d7c2256752c' 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>Track in cycle the measurement belongs to</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.21 MiB </td>\n",
" <td> 94.44 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> int16 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
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"\n",
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"\n",
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"</svg>\n",
" </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sla_unfiltered</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-8d089eb1-2566-4327-b7c7-f76cc93d8081' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d089eb1-2566-4327-b7c7-f76cc93d8081' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b15004b7-8403-4799-9686-f2210777514a' class='xr-var-data-in' type='checkbox'><label for='data-b15004b7-8403-4799-9686-f2210777514a' 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>comment :</span></dt><dd>The sea level anomaly is the sea surface height above mean sea surface height; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[dac]+[ocean_tide]+[internal_tide]-[lwe]; see the product user manual for details</dd><dt><span>long_name :</span></dt><dd>Sea level anomaly not-filtered not-subsampled with dac, ocean_tide and lwe correction applied</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 2.41 MiB </td>\n",
" <td> 188.87 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
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"</svg>\n",
" </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sla_filtered</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-1de7d1c3-4209-4502-9633-ede54aabd3d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1de7d1c3-4209-4502-9633-ede54aabd3d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07e7d01a-ef41-433d-a893-daafce3e70b0' class='xr-var-data-in' type='checkbox'><label for='data-07e7d01a-ef41-433d-a893-daafce3e70b0' 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>comment :</span></dt><dd>The sea level anomaly is the sea surface height above mean sea surface height; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[dac]+[ocean_tide]+[internal_tide]-[lwe]; see the product user manual for details</dd><dt><span>long_name :</span></dt><dd>Sea level anomaly filtered not-subsampled with dac, ocean_tide and lwe correction applied</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
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" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 2.41 MiB </td>\n",
" <td> 188.87 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"170\" height=\"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",
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" <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
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" <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"25\" />\n",
" <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"25\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\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\" >632980</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>dac</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-9e8071f2-9d3a-4c28-991a-bbfc0c359059' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e8071f2-9d3a-4c28-991a-bbfc0c359059' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66a16b97-304d-4ae4-9222-98db64053127' class='xr-var-data-in' type='checkbox'><label for='data-66a16b97-304d-4ae4-9222-98db64053127' 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>comment :</span></dt><dd>The sla in this file is already corrected for the dac; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[dac]; see the product user manual for details</dd><dt><span>long_name :</span></dt><dd>Dynamic Atmospheric Correction</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 2.41 MiB </td>\n",
" <td> 188.87 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"170\" height=\"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=\"8\" y1=\"0\" x2=\"8\" y2=\"25\" />\n",
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" <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
" <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
" <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"25\" />\n",
" <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"25\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\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\" >632980</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>ocean_tide</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-e15c47c7-92b7-41bb-b9fb-d15ae7bdfcad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e15c47c7-92b7-41bb-b9fb-d15ae7bdfcad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3af7cc7-f979-4808-b0fe-cfe845914d47' class='xr-var-data-in' type='checkbox'><label for='data-f3af7cc7-f979-4808-b0fe-cfe845914d47' 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>comment :</span></dt><dd>The sla in this file is already corrected for the ocean_tide; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[ocean_tide]; see the product user manual for details</dd><dt><span>long_name :</span></dt><dd>Ocean tide model</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 2.41 MiB </td>\n",
" <td> 188.87 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"170\" height=\"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=\"8\" y1=\"0\" x2=\"8\" y2=\"25\" />\n",
" <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"25\" />\n",
" <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
" <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"25\" />\n",
" <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
" <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
" <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"25\" />\n",
" <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"25\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\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\" >632980</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>internal_tide</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-17f9cd80-fc46-4be0-9a2f-16937f57bf9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17f9cd80-fc46-4be0-9a2f-16937f57bf9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b136482-54e5-4d70-bf47-3cd22a63d689' class='xr-var-data-in' type='checkbox'><label for='data-1b136482-54e5-4d70-bf47-3cd22a63d689' 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>comment :</span></dt><dd>The sla in this file is already corrected for the internal_tide; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[internal_tide]; see the product user manual for details</dd><dt><span>long_name :</span></dt><dd>Internal tide correction</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 2.41 MiB </td>\n",
" <td> 188.87 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"170\" height=\"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=\"8\" y1=\"0\" x2=\"8\" y2=\"25\" />\n",
" <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"25\" />\n",
" <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
" <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"25\" />\n",
" <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
" <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
" <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"25\" />\n",
" <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"25\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\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\" >632980</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>lwe</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-18873d2c-17d1-4cdc-b24b-cd4682613e50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18873d2c-17d1-4cdc-b24b-cd4682613e50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aeced60b-a8ad-4f3d-aba6-d53139554492' class='xr-var-data-in' type='checkbox'><label for='data-aeced60b-a8ad-4f3d-aba6-d53139554492' 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>comment :</span></dt><dd>The sla in this file is already corrected for the lwe; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]-[lwe]; see the product user manual for details</dd><dt><span>long_name :</span></dt><dd>Long wavelength error</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 2.41 MiB </td>\n",
" <td> 188.87 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"170\" height=\"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=\"8\" y1=\"0\" x2=\"8\" y2=\"25\" />\n",
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" <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
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" <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
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" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"25\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
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"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >632980</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>mdt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-2bab8c00-3742-4a7c-90bb-2d075047fb44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bab8c00-3742-4a7c-90bb-2d075047fb44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb6a41c2-8bf4-48bf-86a7-a246baa2d930' class='xr-var-data-in' type='checkbox'><label for='data-eb6a41c2-8bf4-48bf-86a7-a246baa2d930' 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>comment :</span></dt><dd>The mean dynamic topography is the sea surface height above geoid; it is used to compute the absolute dynamic tyopography adt=sla+mdt</dd><dt><span>long_name :</span></dt><dd>Mean dynamic topography</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tpa_correction</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-44ce1ddf-64a1-40ca-9d76-b045c728b143' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44ce1ddf-64a1-40ca-9d76-b045c728b143' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff154641-56ec-4060-a16c-1cf1530cf3c9' class='xr-var-data-in' type='checkbox'><label for='data-ff154641-56ec-4060-a16c-1cf1530cf3c9' 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>comment :</span></dt><dd>This variable can be added to the SLA to correct for the observed instrumental drift during the lifetime of the TOPEX-A mission (the correction is null after this period). This is a global correction to be added a posteriori (and not before) on the global mean sea level estimate derived from the along-track product. It can be applied at regional or local scale as a best estimate (better than no correction, since the regional variation of the instrumental drift is unknown). See product manual for more details.</dd><dt><span>long_name :</span></dt><dd>TOPEX-A instrumental drift correction derived from altimetry and tide gauges global comparisons (WCRP Sea Level Budget Group, 2018)</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-023bd6f3-e0f2-4698-9169-17fad519e25a' class='xr-section-summary-in' type='checkbox' ><label for='section-023bd6f3-e0f2-4698-9169-17fad519e25a' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4defd8ff-a0da-4fe0-93e9-f25ff899c4c8' class='xr-index-data-in' type='checkbox'/><label for='index-4defd8ff-a0da-4fe0-93e9-f25ff899c4c8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2002-04-30 23:58:22.856968704&#x27;,\n",
" &#x27;2002-04-30 23:58:27.756968448&#x27;,\n",
" &#x27;2002-04-30 23:58:28.736968704&#x27;,\n",
" &#x27;2002-04-30 23:58:29.716968704&#x27;,\n",
" &#x27;2002-04-30 23:58:30.696968704&#x27;,\n",
" &#x27;2002-04-30 23:58:31.676968704&#x27;,\n",
" &#x27;2002-04-30 23:58:32.656968704&#x27;,\n",
" &#x27;2002-04-30 23:58:33.636968448&#x27;,\n",
" &#x27;2002-04-30 23:58:34.616968448&#x27;,\n",
" &#x27;2002-04-30 23:58:35.596968448&#x27;,\n",
" ...\n",
" &#x27;2002-05-14 18:04:44.413067776&#x27;,\n",
" &#x27;2002-05-14 18:04:45.393067776&#x27;,\n",
" &#x27;2002-05-14 18:04:46.373067520&#x27;,\n",
" &#x27;2002-05-14 18:04:47.353067520&#x27;,\n",
" &#x27;2002-05-14 18:04:48.333067520&#x27;,\n",
" &#x27;2002-05-14 18:04:49.313067520&#x27;,\n",
" &#x27;2002-05-14 18:04:50.293067520&#x27;,\n",
" &#x27;2002-05-14 18:04:51.273067776&#x27;,\n",
" &#x27;2002-05-14 18:04:52.253067776&#x27;,\n",
" &#x27;2002-05-14 18:04:55.193067520&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=632980, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f89722c7-7451-4b26-b2df-207714297e2c' class='xr-section-summary-in' type='checkbox' ><label for='section-f89722c7-7451-4b26-b2df-207714297e2c' class='xr-section-summary' >Attributes: <span>(44)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>cdm_data_type :</span></dt><dd>Swath</dd><dt><span>comment :</span></dt><dd>Sea surface height measured by altimeters referenced to the [1993, 2012] period; with additional corrections; the proposed sla is already corrected for dac, ocean_tide and lwe; [uncorrected sla]=[sla from product]+[dac]+[ocean_tide]-[lwe]</dd><dt><span>contact :</span></dt><dd>[email protected]</dd><dt><span>creator_email :</span></dt><dd>[email protected]</dd><dt><span>creator_name :</span></dt><dd>CMEMS - Sea Level Thematic Assembly Center</dd><dt><span>creator_url :</span></dt><dd>http://marine.copernicus.eu</dd><dt><span>date_created :</span></dt><dd>2021-09-09T04:59:55Z</dd><dt><span>date_issued :</span></dt><dd>2021-09-09T04:59:55Z</dd><dt><span>date_modified :</span></dt><dd>2021-09-09T04:59:55Z</dd><dt><span>geospatial_lat_max :</span></dt><dd>80.415607</dd><dt><span>geospatial_lat_min :</span></dt><dd>-69.307493</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.0476875000000021</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.994941</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.012109</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.016302999999993517</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>down</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>point</dd><dt><span>geospatial_vertical_units :</span></dt><dd>m</dd><dt><span>history :</span></dt><dd>2021-09-09T04:59:55Z: Creation</dd><dt><span>institution :</span></dt><dd>CLS, CNES</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Topography &gt; Sea Surface Height</dd><dt><span>keywords_vocabulary :</span></dt><dd>NetCDF COARDS Climate and Forecast Standard Names</dd><dt><span>license :</span></dt><dd>http://marine.copernicus.eu/web/27-service-commitments-and-licence.php</dd><dt><span>platform :</span></dt><dd>ERS-2</dd><dt><span>processing_level :</span></dt><dd>L3</dd><dt><span>product_version :</span></dt><dd>vDec2021</dd><dt><span>project :</span></dt><dd>COPERNICUS MARINE ENVIRONMENT MONITORING SERVICE (CMEMS)</dd><dt><span>references :</span></dt><dd>http://marine.copernicus.eu</dd><dt><span>software_version :</span></dt><dd>7.0_DUACS_DT2021_baseline</dd><dt><span>source :</span></dt><dd>ERS-2 measurements</dd><dt><span>ssalto_duacs_comment :</span></dt><dd>The reference mission used for the altimeter inter-calibration processing is Topex/Poseidon between 1993-01-01 and 2002-04-23, Jason-1 between 2002-04-24 and 2008-10-18, OSTM/Jason-2 between 2008-10-19 and 2016-06-25, Jason-3 since 2016-06-25.</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table v37</dd><dt><span>summary :</span></dt><dd>SSALTO/DUACS Delayed-Time Level-3 sea surface height measured by ERS-2 altimetry observations over Global Ocean.</dd><dt><span>time_coverage_duration :</span></dt><dd>P23H14M47.18179S</dd><dt><span>time_coverage_end :</span></dt><dd>2002-05-01T23:13:09Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1S</dd><dt><span>time_coverage_start :</span></dt><dd>2002-04-30T23:58:22Z</dd><dt><span>title :</span></dt><dd>DT ERS-2 Global Ocean Along track SSALTO/DUACS Sea Surface Height L3 product</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (time: 632980)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2002-04-30T23:58:22.856968704 ... 2...\n",
" longitude (time) float64 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" latitude (time) float64 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
"Data variables:\n",
" cycle (time) int16 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" track (time) int16 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" sla_unfiltered (time) float32 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" sla_filtered (time) float32 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" dac (time) float32 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" ocean_tide (time) float32 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" internal_tide (time) float32 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" lwe (time) float32 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" mdt (time) float32 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" tpa_correction (time) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
"Attributes: (12/44)\n",
" Conventions: CF-1.6\n",
" Metadata_Conventions: Unidata Dataset Discovery v1.0\n",
" cdm_data_type: Swath\n",
" comment: Sea surface height measured by altimeter...\n",
" contact: [email protected]\n",
" creator_email: [email protected]\n",
" ... ...\n",
" summary: SSALTO/DUACS Delayed-Time Level-3 sea su...\n",
" time_coverage_duration: P23H14M47.18179S\n",
" time_coverage_end: 2002-05-01T23:13:09Z\n",
" time_coverage_resolution: P1S\n",
" time_coverage_start: 2002-04-30T23:58:22Z\n",
" title: DT ERS-2 Global Ocean Along track SSALTO..."
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_track = xr.open_mfdataset(\"dt_global_e2_phy_l3_*_*.nc\")\n",
"ds_track"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "ae9790eb-c176-45a5-9bf2-a4d168760f95",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='longitude', ylabel='latitude'>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds_track.to_dataframe().iloc[::20].plot.scatter(\n",
" x=\"longitude\",\n",
" y=\"latitude\",\n",
" s=1,\n",
" alpha=0.6,\n",
" figsize=(12, 6),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d935cae2-f683-4998-9b4b-828cf5ce77ab",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "52635193-5fa1-4038-9e87-d6fcdcf7db1e",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (axis_nbounds: 2, time_counter: 73, y: 1021, x: 1442)\n",
"Coordinates:\n",
" nav_lat (y, x) float32 -77.01 -77.01 -77.01 ... 50.0 50.0\n",
" nav_lon (y, x) float32 72.75 73.0 73.25 ... 73.01 73.0 73.0\n",
" time_centered (time_counter) datetime64[ns] 2018-01-03T12:00:00 ....\n",
" * time_counter (time_counter) datetime64[ns] 2018-01-03T12:00:00 ....\n",
" * x (x) int64 0 1 2 3 4 5 ... 1437 1438 1439 1440 1441\n",
" * y (y) int64 0 1 2 3 4 5 ... 1016 1017 1018 1019 1020\n",
"Dimensions without coordinates: axis_nbounds\n",
"Data variables: (12/14)\n",
" deptht_bounds (axis_nbounds, time_counter, y, x) float32 nan ... nan\n",
" time_centered_bounds (time_counter, axis_nbounds, y, x) datetime64[ns] N...\n",
" time_counter_bounds (time_counter, axis_nbounds, y, x) datetime64[ns] N...\n",
" votemper (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" vosaline (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" sowaflup (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" ... ...\n",
" somxl010 (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" somixhgt (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" sowindsp (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" sohefldp (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" sowafldp (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" sobowlin (time_counter, y, x) float32 nan nan nan ... nan nan\n",
"Attributes:\n",
" name: ./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T\n",
" description: ocean T grid variables\n",
" title: ocean T grid variables\n",
" Conventions: CF-1.6\n",
" timeStamp: 2020-Jan-04 09:32:22 GMT\n",
" uuid: c1428d50-e458-41e7-83f6-b2813d89831b</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-eeca5dfc-87e8-4481-aae1-3c2231a01076' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eeca5dfc-87e8-4481-aae1-3c2231a01076' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>axis_nbounds</span>: 2</li><li><span class='xr-has-index'>time_counter</span>: 73</li><li><span class='xr-has-index'>y</span>: 1021</li><li><span class='xr-has-index'>x</span>: 1442</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-aa5f2d24-f64c-442c-b995-101edc4c7a03' class='xr-section-summary-in' type='checkbox' checked><label for='section-aa5f2d24-f64c-442c-b995-101edc4c7a03' class='xr-section-summary' >Coordinates: <span>(6)</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>nav_lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-77.01 -77.01 -77.01 ... 50.0 50.0</div><input id='attrs-893e67e5-557b-4c79-99fb-8274c430e723' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-893e67e5-557b-4c79-99fb-8274c430e723' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5237746a-2e22-4005-9f74-f052055b64e4' class='xr-var-data-in' type='checkbox'><label for='data-5237746a-2e22-4005-9f74-f052055b64e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([[-77.010475, -77.010475, -77.010475, ..., -77.010475, -77.010475,\n",
" -77.010475],\n",
" [-76.95416 , -76.95416 , -76.95416 , ..., -76.95416 , -76.95416 ,\n",
" -76.95416 ],\n",
" [-76.89761 , -76.89761 , -76.89761 , ..., -76.89761 , -76.89761 ,\n",
" -76.89761 ],\n",
" ...,\n",
" [ 49.995502, 49.995502, 49.995502, ..., 50.018894, 49.995502,\n",
" 49.995502],\n",
" [ 50. , 50. , 50. , ..., 50.019047, 50. ,\n",
" 50. ],\n",
" [ 49.995502, 49.995502, 49.995502, ..., 50.018894, 49.995502,\n",
" 49.995502]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nav_lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.75 73.0 73.25 ... 73.0 73.0</div><input id='attrs-bbd7ad62-2142-41dd-9b03-479c543fd331' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbd7ad62-2142-41dd-9b03-479c543fd331' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df16f92c-60c3-4ba3-8ec2-6a243be222b3' class='xr-var-data-in' type='checkbox'><label for='data-df16f92c-60c3-4ba3-8ec2-6a243be222b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([[72.75 , 73. , 73.25 , ..., 72.5 , 72.75 ,\n",
" 73. ],\n",
" [72.75 , 73. , 73.25 , ..., 72.5 , 72.75 ,\n",
" 73. ],\n",
" [72.75 , 73. , 73.25 , ..., 72.5 , 72.75 ,\n",
" 73. ],\n",
" ...,\n",
" [72.999985, 73. , 73.000015, ..., 72.99432 , 72.999985,\n",
" 73. ],\n",
" [73. , 73. , 73. , ..., 73. , 73. ,\n",
" 73. ],\n",
" [73.000015, 73. , 72.999985, ..., 73.00568 , 73.000015,\n",
" 73. ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_centered</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00 ... 2018-12-...</div><input id='attrs-750daffa-57df-4bb5-b197-0bbe5be66eaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-750daffa-57df-4bb5-b197-0bbe5be66eaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ced5fcb-c988-4367-a57b-621d5fe606df' class='xr-var-data-in' type='checkbox'><label for='data-6ced5fcb-c988-4367-a57b-621d5fe606df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_centered_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-03T12:00:00.000000000&#x27;, &#x27;2018-01-08T12:00:00.000000000&#x27;,\n",
" &#x27;2018-01-13T12:00:00.000000000&#x27;, &#x27;2018-01-18T12:00:00.000000000&#x27;,\n",
" &#x27;2018-01-23T12:00:00.000000000&#x27;, &#x27;2018-01-28T12:00:00.000000000&#x27;,\n",
" &#x27;2018-02-02T12:00:00.000000000&#x27;, &#x27;2018-02-07T12:00:00.000000000&#x27;,\n",
" &#x27;2018-02-12T12:00:00.000000000&#x27;, &#x27;2018-02-17T12:00:00.000000000&#x27;,\n",
" &#x27;2018-02-22T12:00:00.000000000&#x27;, &#x27;2018-02-27T12:00:00.000000000&#x27;,\n",
" &#x27;2018-03-04T12:00:00.000000000&#x27;, &#x27;2018-03-09T12:00:00.000000000&#x27;,\n",
" &#x27;2018-03-14T12:00:00.000000000&#x27;, &#x27;2018-03-19T12:00:00.000000000&#x27;,\n",
" &#x27;2018-03-24T12:00:00.000000000&#x27;, &#x27;2018-03-29T12:00:00.000000000&#x27;,\n",
" &#x27;2018-04-03T12:00:00.000000000&#x27;, &#x27;2018-04-08T12:00:00.000000000&#x27;,\n",
" &#x27;2018-04-13T12:00:00.000000000&#x27;, &#x27;2018-04-18T12:00:00.000000000&#x27;,\n",
" &#x27;2018-04-23T12:00:00.000000000&#x27;, &#x27;2018-04-28T12:00:00.000000000&#x27;,\n",
" &#x27;2018-05-03T12:00:00.000000000&#x27;, &#x27;2018-05-08T12:00:00.000000000&#x27;,\n",
" &#x27;2018-05-13T12:00:00.000000000&#x27;, &#x27;2018-05-18T12:00:00.000000000&#x27;,\n",
" &#x27;2018-05-23T12:00:00.000000000&#x27;, &#x27;2018-05-28T12:00:00.000000000&#x27;,\n",
" &#x27;2018-06-02T12:00:00.000000000&#x27;, &#x27;2018-06-07T12:00:00.000000000&#x27;,\n",
" &#x27;2018-06-12T12:00:00.000000000&#x27;, &#x27;2018-06-17T12:00:00.000000000&#x27;,\n",
" &#x27;2018-06-22T12:00:00.000000000&#x27;, &#x27;2018-06-27T12:00:00.000000000&#x27;,\n",
" &#x27;2018-07-02T12:00:00.000000000&#x27;, &#x27;2018-07-07T12:00:00.000000000&#x27;,\n",
" &#x27;2018-07-12T12:00:00.000000000&#x27;, &#x27;2018-07-17T12:00:00.000000000&#x27;,\n",
" &#x27;2018-07-22T12:00:00.000000000&#x27;, &#x27;2018-07-27T12:00:00.000000000&#x27;,\n",
" &#x27;2018-08-01T12:00:00.000000000&#x27;, &#x27;2018-08-06T12:00:00.000000000&#x27;,\n",
" &#x27;2018-08-11T12:00:00.000000000&#x27;, &#x27;2018-08-16T12:00:00.000000000&#x27;,\n",
" &#x27;2018-08-21T12:00:00.000000000&#x27;, &#x27;2018-08-26T12:00:00.000000000&#x27;,\n",
" &#x27;2018-08-31T12:00:00.000000000&#x27;, &#x27;2018-09-05T12:00:00.000000000&#x27;,\n",
" &#x27;2018-09-10T12:00:00.000000000&#x27;, &#x27;2018-09-15T12:00:00.000000000&#x27;,\n",
" &#x27;2018-09-20T12:00:00.000000000&#x27;, &#x27;2018-09-25T12:00:00.000000000&#x27;,\n",
" &#x27;2018-09-30T12:00:00.000000000&#x27;, &#x27;2018-10-05T12:00:00.000000000&#x27;,\n",
" &#x27;2018-10-10T12:00:00.000000000&#x27;, &#x27;2018-10-15T12:00:00.000000000&#x27;,\n",
" &#x27;2018-10-20T12:00:00.000000000&#x27;, &#x27;2018-10-25T12:00:00.000000000&#x27;,\n",
" &#x27;2018-10-30T12:00:00.000000000&#x27;, &#x27;2018-11-04T12:00:00.000000000&#x27;,\n",
" &#x27;2018-11-09T12:00:00.000000000&#x27;, &#x27;2018-11-14T12:00:00.000000000&#x27;,\n",
" &#x27;2018-11-19T12:00:00.000000000&#x27;, &#x27;2018-11-24T12:00:00.000000000&#x27;,\n",
" &#x27;2018-11-29T12:00:00.000000000&#x27;, &#x27;2018-12-04T12:00:00.000000000&#x27;,\n",
" &#x27;2018-12-09T12:00:00.000000000&#x27;, &#x27;2018-12-14T12:00:00.000000000&#x27;,\n",
" &#x27;2018-12-19T12:00:00.000000000&#x27;, &#x27;2018-12-24T12:00:00.000000000&#x27;,\n",
" &#x27;2018-12-29T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00 ... 2018-12-...</div><input id='attrs-944182a2-97cf-4940-85b2-6c5d92c7e03f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-944182a2-97cf-4940-85b2-6c5d92c7e03f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-819979f9-d8c4-458b-8168-3809f7d139da' class='xr-var-data-in' type='checkbox'><label for='data-819979f9-d8c4-458b-8168-3809f7d139da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-03T12:00:00.000000000&#x27;, &#x27;2018-01-08T12:00:00.000000000&#x27;,\n",
" &#x27;2018-01-13T12:00:00.000000000&#x27;, &#x27;2018-01-18T12:00:00.000000000&#x27;,\n",
" &#x27;2018-01-23T12:00:00.000000000&#x27;, &#x27;2018-01-28T12:00:00.000000000&#x27;,\n",
" &#x27;2018-02-02T12:00:00.000000000&#x27;, &#x27;2018-02-07T12:00:00.000000000&#x27;,\n",
" &#x27;2018-02-12T12:00:00.000000000&#x27;, &#x27;2018-02-17T12:00:00.000000000&#x27;,\n",
" &#x27;2018-02-22T12:00:00.000000000&#x27;, &#x27;2018-02-27T12:00:00.000000000&#x27;,\n",
" &#x27;2018-03-04T12:00:00.000000000&#x27;, &#x27;2018-03-09T12:00:00.000000000&#x27;,\n",
" &#x27;2018-03-14T12:00:00.000000000&#x27;, &#x27;2018-03-19T12:00:00.000000000&#x27;,\n",
" &#x27;2018-03-24T12:00:00.000000000&#x27;, &#x27;2018-03-29T12:00:00.000000000&#x27;,\n",
" &#x27;2018-04-03T12:00:00.000000000&#x27;, &#x27;2018-04-08T12:00:00.000000000&#x27;,\n",
" &#x27;2018-04-13T12:00:00.000000000&#x27;, &#x27;2018-04-18T12:00:00.000000000&#x27;,\n",
" &#x27;2018-04-23T12:00:00.000000000&#x27;, &#x27;2018-04-28T12:00:00.000000000&#x27;,\n",
" &#x27;2018-05-03T12:00:00.000000000&#x27;, &#x27;2018-05-08T12:00:00.000000000&#x27;,\n",
" &#x27;2018-05-13T12:00:00.000000000&#x27;, &#x27;2018-05-18T12:00:00.000000000&#x27;,\n",
" &#x27;2018-05-23T12:00:00.000000000&#x27;, &#x27;2018-05-28T12:00:00.000000000&#x27;,\n",
" &#x27;2018-06-02T12:00:00.000000000&#x27;, &#x27;2018-06-07T12:00:00.000000000&#x27;,\n",
" &#x27;2018-06-12T12:00:00.000000000&#x27;, &#x27;2018-06-17T12:00:00.000000000&#x27;,\n",
" &#x27;2018-06-22T12:00:00.000000000&#x27;, &#x27;2018-06-27T12:00:00.000000000&#x27;,\n",
" &#x27;2018-07-02T12:00:00.000000000&#x27;, &#x27;2018-07-07T12:00:00.000000000&#x27;,\n",
" &#x27;2018-07-12T12:00:00.000000000&#x27;, &#x27;2018-07-17T12:00:00.000000000&#x27;,\n",
" &#x27;2018-07-22T12:00:00.000000000&#x27;, &#x27;2018-07-27T12:00:00.000000000&#x27;,\n",
" &#x27;2018-08-01T12:00:00.000000000&#x27;, &#x27;2018-08-06T12:00:00.000000000&#x27;,\n",
" &#x27;2018-08-11T12:00:00.000000000&#x27;, &#x27;2018-08-16T12:00:00.000000000&#x27;,\n",
" &#x27;2018-08-21T12:00:00.000000000&#x27;, &#x27;2018-08-26T12:00:00.000000000&#x27;,\n",
" &#x27;2018-08-31T12:00:00.000000000&#x27;, &#x27;2018-09-05T12:00:00.000000000&#x27;,\n",
" &#x27;2018-09-10T12:00:00.000000000&#x27;, &#x27;2018-09-15T12:00:00.000000000&#x27;,\n",
" &#x27;2018-09-20T12:00:00.000000000&#x27;, &#x27;2018-09-25T12:00:00.000000000&#x27;,\n",
" &#x27;2018-09-30T12:00:00.000000000&#x27;, &#x27;2018-10-05T12:00:00.000000000&#x27;,\n",
" &#x27;2018-10-10T12:00:00.000000000&#x27;, &#x27;2018-10-15T12:00:00.000000000&#x27;,\n",
" &#x27;2018-10-20T12:00:00.000000000&#x27;, &#x27;2018-10-25T12:00:00.000000000&#x27;,\n",
" &#x27;2018-10-30T12:00:00.000000000&#x27;, &#x27;2018-11-04T12:00:00.000000000&#x27;,\n",
" &#x27;2018-11-09T12:00:00.000000000&#x27;, &#x27;2018-11-14T12:00:00.000000000&#x27;,\n",
" &#x27;2018-11-19T12:00:00.000000000&#x27;, &#x27;2018-11-24T12:00:00.000000000&#x27;,\n",
" &#x27;2018-11-29T12:00:00.000000000&#x27;, &#x27;2018-12-04T12:00:00.000000000&#x27;,\n",
" &#x27;2018-12-09T12:00:00.000000000&#x27;, &#x27;2018-12-14T12:00:00.000000000&#x27;,\n",
" &#x27;2018-12-19T12:00:00.000000000&#x27;, &#x27;2018-12-24T12:00:00.000000000&#x27;,\n",
" &#x27;2018-12-29T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1438 1439 1440 1441</div><input id='attrs-462b440b-f7af-4040-807d-f0f5ae48cc62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-462b440b-f7af-4040-807d-f0f5ae48cc62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1528b46-8d7d-4a2e-be41-90c7aadfebcd' class='xr-var-data-in' type='checkbox'><label for='data-c1528b46-8d7d-4a2e-be41-90c7aadfebcd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 1439, 1440, 1441])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1017 1018 1019 1020</div><input id='attrs-fc175a95-d7f5-4486-99b1-775c57c4f8d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc175a95-d7f5-4486-99b1-775c57c4f8d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbf1e1f2-058c-4639-8125-4867fc512fd5' class='xr-var-data-in' type='checkbox'><label for='data-bbf1e1f2-058c-4639-8125-4867fc512fd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 1018, 1019, 1020])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ef1b310-3311-4bc7-a33c-9bb9917ed84a' class='xr-section-summary-in' type='checkbox' checked><label for='section-8ef1b310-3311-4bc7-a33c-9bb9917ed84a' class='xr-section-summary' >Data variables: <span>(14)</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>deptht_bounds</span></div><div class='xr-var-dims'>(axis_nbounds, time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-4d6af89c-f9f1-452f-bc05-b362e65ef12d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d6af89c-f9f1-452f-bc05-b362e65ef12d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9b2dd30-5c73-45b6-87ce-5e2d367ddcd9' class='xr-var-data-in' type='checkbox'><label for='data-c9b2dd30-5c73-45b6-87ce-5e2d367ddcd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_centered_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds, y, x)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>NaT NaT NaT NaT ... NaT NaT NaT NaT</div><input id='attrs-2d18bc74-a093-45a2-9481-76febe0b799e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d18bc74-a093-45a2-9481-76febe0b799e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d072b327-88f7-4808-957e-2be021935a57' class='xr-var-data-in' type='checkbox'><label for='data-d072b327-88f7-4808-957e-2be021935a57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" ...,\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;]],\n",
"\n",
" [[&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" ...,\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;]]],\n",
"\n",
"\n",
" [[[&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
"...\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;]]],\n",
"\n",
"\n",
" [[[&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" ...,\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;]],\n",
"\n",
" [[&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" ...,\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;]]]],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds, y, x)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>NaT NaT NaT NaT ... NaT NaT NaT NaT</div><input id='attrs-ee1ded63-0a64-4e99-91a3-761f7783c6da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee1ded63-0a64-4e99-91a3-761f7783c6da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f9795f4-ae9f-4a7c-bca2-6ed51b504632' class='xr-var-data-in' type='checkbox'><label for='data-1f9795f4-ae9f-4a7c-bca2-6ed51b504632' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" ...,\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;]],\n",
"\n",
" [[&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" ...,\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;]]],\n",
"\n",
"\n",
" [[[&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
"...\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;]]],\n",
"\n",
"\n",
" [[[&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" ...,\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;]],\n",
"\n",
" [[&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" ...,\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;],\n",
" [&#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;, ..., &#x27;NaT&#x27;, &#x27;NaT&#x27;, &#x27;NaT&#x27;]]]],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>votemper</span></div><div class='xr-var-dims'>(time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-7d61c76b-fed5-4787-93de-21371562b2b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d61c76b-fed5-4787-93de-21371562b2b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1165ac77-7849-435a-91bd-80c913ecda7e' class='xr-var-data-in' type='checkbox'><label for='data-1165ac77-7849-435a-91bd-80c913ecda7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vosaline</span></div><div class='xr-var-dims'>(time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-5160b6d7-e803-4860-a91b-d2859e1904a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5160b6d7-e803-4860-a91b-d2859e1904a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-370d62da-8028-4308-b089-892b2d8f9024' class='xr-var-data-in' type='checkbox'><label for='data-370d62da-8028-4308-b089-892b2d8f9024' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>long_name :</span></dt><dd>Sea Water Salinity</dd><dt><span>units :</span></dt><dd>0.001</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowaflup</span></div><div class='xr-var-dims'>(time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-230c43a6-95b3-415b-ae3b-dce7c7312798' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-230c43a6-95b3-415b-ae3b-dce7c7312798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-558dc1d5-a86d-4d2a-9a49-343eda347ff8' class='xr-var-data-in' type='checkbox'><label for='data-558dc1d5-a86d-4d2a-9a49-343eda347ff8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>water_flux_out_of_sea_ice_and_sea_water</dd><dt><span>long_name :</span></dt><dd>Net Upward Water Flux</dd><dt><span>units :</span></dt><dd>kg/m2/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soshfldo</span></div><div class='xr-var-dims'>(time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-86acdc19-73bb-4bcc-bd15-9edeaafef54c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86acdc19-73bb-4bcc-bd15-9edeaafef54c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87dc856d-276c-4b6c-a783-7ac723923ccd' class='xr-var-data-in' type='checkbox'><label for='data-87dc856d-276c-4b6c-a783-7ac723923ccd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>net_downward_shortwave_flux_at_sea_water_surface</dd><dt><span>long_name :</span></dt><dd>Shortwave Radiation</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sohefldo</span></div><div class='xr-var-dims'>(time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-f252cc29-d929-487a-a84d-5dd96de09f33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f252cc29-d929-487a-a84d-5dd96de09f33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-694da21e-cb26-4023-b153-ff9b3ac1dabd' class='xr-var-data-in' type='checkbox'><label for='data-694da21e-cb26-4023-b153-ff9b3ac1dabd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_downward_heat_flux_in_sea_water</dd><dt><span>long_name :</span></dt><dd>Net Downward Heat Flux</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>somxl010</span></div><div class='xr-var-dims'>(time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-571a643a-c072-4806-af69-814b5ce3b137' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-571a643a-c072-4806-af69-814b5ce3b137' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39e95b5c-1f5f-4e3a-8bb7-96a4f079549c' class='xr-var-data-in' type='checkbox'><label for='data-39e95b5c-1f5f-4e3a-8bb7-96a4f079549c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_sigma_theta</dd><dt><span>long_name :</span></dt><dd>Mixed Layer Depth (dsigma = 0.01 wrt 10m)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>somixhgt</span></div><div class='xr-var-dims'>(time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-932d493c-100a-46df-ad96-5d0cfce9525b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-932d493c-100a-46df-ad96-5d0cfce9525b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-571cc46e-217e-4bb8-90ca-ed3c492f2165' class='xr-var-data-in' type='checkbox'><label for='data-571cc46e-217e-4bb8-90ca-ed3c492f2165' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_vertical_tracer_diffusivity</dd><dt><span>long_name :</span></dt><dd>Turbocline depth (Kz = 5e-4)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowindsp</span></div><div class='xr-var-dims'>(time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-cdc7616f-9bf6-4a2b-a919-1b85a1da2c16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdc7616f-9bf6-4a2b-a919-1b85a1da2c16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5026696b-272e-47f5-b3d3-e36032c7cd1d' class='xr-var-data-in' type='checkbox'><label for='data-5026696b-272e-47f5-b3d3-e36032c7cd1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>long_name :</span></dt><dd>wind speed module</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sohefldp</span></div><div class='xr-var-dims'>(time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-8b1a54b8-8bb5-40dd-887d-be0463247736' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b1a54b8-8bb5-40dd-887d-be0463247736' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89d27c0f-de05-47e6-a088-37bacc03f609' class='xr-var-data-in' type='checkbox'><label for='data-89d27c0f-de05-47e6-a088-37bacc03f609' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>heat_flux_into_sea_water_due_to_newtonian_relaxation</dd><dt><span>long_name :</span></dt><dd>Surface Heat Flux: Damping</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowafldp</span></div><div class='xr-var-dims'>(time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-d1f5c415-f7de-4267-889d-2a32bdc856b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1f5c415-f7de-4267-889d-2a32bdc856b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2679179-6123-4be7-a852-edb5d5c337fd' class='xr-var-data-in' type='checkbox'><label for='data-f2679179-6123-4be7-a852-edb5d5c337fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>water_flux_out_of_sea_water_due_to_newtonian_relaxation</dd><dt><span>long_name :</span></dt><dd>Surface Water Flux: Damping</dd><dt><span>units :</span></dt><dd>kg/m2/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sobowlin</span></div><div class='xr-var-dims'>(time_counter, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-d9bf33a3-5f1d-4171-af7d-b6dcb96f751a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9bf33a3-5f1d-4171-af7d-b6dcb96f751a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b36cf974-5e62-45dc-b03e-257c00d74ef0' class='xr-var-data-in' type='checkbox'><label for='data-b36cf974-5e62-45dc-b03e-257c00d74ef0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_sigma_theta</dd><dt><span>long_name :</span></dt><dd>Mixed Layer Depth (dsigma = 0.01 wrt 10m)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>maximum</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
"...\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]],\n",
"\n",
" [[nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" ...,\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan],\n",
" [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-88c262df-647b-4751-a870-14aa2468f505' class='xr-section-summary-in' type='checkbox' ><label for='section-88c262df-647b-4751-a870-14aa2468f505' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2dc018dc-3f1f-4672-bf52-e786f1ab6ae0' class='xr-index-data-in' type='checkbox'/><label for='index-2dc018dc-3f1f-4672-bf52-e786f1ab6ae0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2018-01-03 12:00:00&#x27;, &#x27;2018-01-08 12:00:00&#x27;,\n",
" &#x27;2018-01-13 12:00:00&#x27;, &#x27;2018-01-18 12:00:00&#x27;,\n",
" &#x27;2018-01-23 12:00:00&#x27;, &#x27;2018-01-28 12:00:00&#x27;,\n",
" &#x27;2018-02-02 12:00:00&#x27;, &#x27;2018-02-07 12:00:00&#x27;,\n",
" &#x27;2018-02-12 12:00:00&#x27;, &#x27;2018-02-17 12:00:00&#x27;,\n",
" &#x27;2018-02-22 12:00:00&#x27;, &#x27;2018-02-27 12:00:00&#x27;,\n",
" &#x27;2018-03-04 12:00:00&#x27;, &#x27;2018-03-09 12:00:00&#x27;,\n",
" &#x27;2018-03-14 12:00:00&#x27;, &#x27;2018-03-19 12:00:00&#x27;,\n",
" &#x27;2018-03-24 12:00:00&#x27;, &#x27;2018-03-29 12:00:00&#x27;,\n",
" &#x27;2018-04-03 12:00:00&#x27;, &#x27;2018-04-08 12:00:00&#x27;,\n",
" &#x27;2018-04-13 12:00:00&#x27;, &#x27;2018-04-18 12:00:00&#x27;,\n",
" &#x27;2018-04-23 12:00:00&#x27;, &#x27;2018-04-28 12:00:00&#x27;,\n",
" &#x27;2018-05-03 12:00:00&#x27;, &#x27;2018-05-08 12:00:00&#x27;,\n",
" &#x27;2018-05-13 12:00:00&#x27;, &#x27;2018-05-18 12:00:00&#x27;,\n",
" &#x27;2018-05-23 12:00:00&#x27;, &#x27;2018-05-28 12:00:00&#x27;,\n",
" &#x27;2018-06-02 12:00:00&#x27;, &#x27;2018-06-07 12:00:00&#x27;,\n",
" &#x27;2018-06-12 12:00:00&#x27;, &#x27;2018-06-17 12:00:00&#x27;,\n",
" &#x27;2018-06-22 12:00:00&#x27;, &#x27;2018-06-27 12:00:00&#x27;,\n",
" &#x27;2018-07-02 12:00:00&#x27;, &#x27;2018-07-07 12:00:00&#x27;,\n",
" &#x27;2018-07-12 12:00:00&#x27;, &#x27;2018-07-17 12:00:00&#x27;,\n",
" &#x27;2018-07-22 12:00:00&#x27;, &#x27;2018-07-27 12:00:00&#x27;,\n",
" &#x27;2018-08-01 12:00:00&#x27;, &#x27;2018-08-06 12:00:00&#x27;,\n",
" &#x27;2018-08-11 12:00:00&#x27;, &#x27;2018-08-16 12:00:00&#x27;,\n",
" &#x27;2018-08-21 12:00:00&#x27;, &#x27;2018-08-26 12:00:00&#x27;,\n",
" &#x27;2018-08-31 12:00:00&#x27;, &#x27;2018-09-05 12:00:00&#x27;,\n",
" &#x27;2018-09-10 12:00:00&#x27;, &#x27;2018-09-15 12:00:00&#x27;,\n",
" &#x27;2018-09-20 12:00:00&#x27;, &#x27;2018-09-25 12:00:00&#x27;,\n",
" &#x27;2018-09-30 12:00:00&#x27;, &#x27;2018-10-05 12:00:00&#x27;,\n",
" &#x27;2018-10-10 12:00:00&#x27;, &#x27;2018-10-15 12:00:00&#x27;,\n",
" &#x27;2018-10-20 12:00:00&#x27;, &#x27;2018-10-25 12:00:00&#x27;,\n",
" &#x27;2018-10-30 12:00:00&#x27;, &#x27;2018-11-04 12:00:00&#x27;,\n",
" &#x27;2018-11-09 12:00:00&#x27;, &#x27;2018-11-14 12:00:00&#x27;,\n",
" &#x27;2018-11-19 12:00:00&#x27;, &#x27;2018-11-24 12:00:00&#x27;,\n",
" &#x27;2018-11-29 12:00:00&#x27;, &#x27;2018-12-04 12:00:00&#x27;,\n",
" &#x27;2018-12-09 12:00:00&#x27;, &#x27;2018-12-14 12:00:00&#x27;,\n",
" &#x27;2018-12-19 12:00:00&#x27;, &#x27;2018-12-24 12:00:00&#x27;,\n",
" &#x27;2018-12-29 12:00:00&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f6c9be6b-f1a1-4456-b6c2-9fc204876630' class='xr-index-data-in' type='checkbox'/><label for='index-f6c9be6b-f1a1-4456-b6c2-9fc204876630' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441],\n",
" dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;, length=1442))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-16c01991-35c6-40f6-968b-597c18e3801b' class='xr-index-data-in' type='checkbox'/><label for='index-16c01991-35c6-40f6-968b-597c18e3801b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
" ...\n",
" 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020],\n",
" dtype=&#x27;int64&#x27;, name=&#x27;y&#x27;, length=1021))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1b2e97cb-c53a-4e8c-bcf6-2dad1c5ab13f' class='xr-section-summary-in' type='checkbox' checked><label for='section-1b2e97cb-c53a-4e8c-bcf6-2dad1c5ab13f' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T</dd><dt><span>description :</span></dt><dd>ocean T grid variables</dd><dt><span>title :</span></dt><dd>ocean T grid variables</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>timeStamp :</span></dt><dd>2020-Jan-04 09:32:22 GMT</dd><dt><span>uuid :</span></dt><dd>c1428d50-e458-41e7-83f6-b2813d89831b</dd></dl></div></li></ul></div></div>"
],
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"<xarray.Dataset>\n",
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"Coordinates:\n",
" nav_lat (y, x) float32 -77.01 -77.01 -77.01 ... 50.0 50.0\n",
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" time_centered (time_counter) datetime64[ns] 2018-01-03T12:00:00 ....\n",
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"Data variables: (12/14)\n",
" deptht_bounds (axis_nbounds, time_counter, y, x) float32 nan ... nan\n",
" time_centered_bounds (time_counter, axis_nbounds, y, x) datetime64[ns] N...\n",
" time_counter_bounds (time_counter, axis_nbounds, y, x) datetime64[ns] N...\n",
" votemper (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" vosaline (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" sowaflup (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" ... ...\n",
" somxl010 (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" somixhgt (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" sowindsp (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" sohefldp (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" sowafldp (time_counter, y, x) float32 nan nan nan ... nan nan\n",
" sobowlin (time_counter, y, x) float32 nan nan nan ... nan nan\n",
"Attributes:\n",
" name: ./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T\n",
" description: ocean T grid variables\n",
" title: ocean T grid variables\n",
" Conventions: CF-1.6\n",
" timeStamp: 2020-Jan-04 09:32:22 GMT\n",
" uuid: c1428d50-e458-41e7-83f6-b2813d89831b"
]
},
"execution_count": 6,
"metadata": {},
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],
"source": [
"ds_nemo = xr.open_dataset(\n",
" \"/home/jovyan/shared_materials/model_data/INALT20.L46-KFS104/INALT20.L46-KFS104_5d_20180101_20181231_grid_T.nc\",\n",
")\n",
"ds_nemo = ds_nemo.set_coords([\"nav_lat\", \"nav_lon\"])\n",
"ds_nemo = ds_nemo.assign_coords(\n",
" x=np.arange(ds_nemo.dims[\"x\"]),\n",
" y=np.arange(ds_nemo.dims[\"y\"]),\n",
")\n",
"ds_nemo = ds_nemo.isel(deptht=0, drop=True)\n",
"ds_nemo = ds_nemo.where(ds_nemo.votemper != 0)\n",
"ds_nemo"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "1266e59b-1cbd-4836-8cb3-875e5c17f2fd",
"metadata": {
"tags": []
},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 632980)&gt;\n",
"array([&#x27;2018-01-03T12:00:00.000000000&#x27;, &#x27;2018-01-03T12:00:04.899999744&#x27;,\n",
" &#x27;2018-01-03T12:00:05.880000000&#x27;, ..., &#x27;2018-01-17T06:06:28.416099072&#x27;,\n",
" &#x27;2018-01-17T06:06:29.396099072&#x27;, &#x27;2018-01-17T06:06:32.336098816&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)\n",
"Coordinates:\n",
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" longitude (time) float64 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" latitude (time) float64 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" time_centered datetime64[ns] 2018-01-03T12:00:00\n",
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" &#x27;2018-01-03T12:00:05.880000000&#x27;, ..., &#x27;2018-01-17T06:06:28.416099072&#x27;,\n",
" &#x27;2018-01-17T06:06:29.396099072&#x27;, &#x27;2018-01-17T06:06:32.336098816&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-aa63c1bf-1436-44e6-b046-7d8dddcfc6e0' class='xr-section-summary-in' type='checkbox' checked><label for='section-aa63c1bf-1436-44e6-b046-7d8dddcfc6e0' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00 ... 2018-01-...</div><input id='attrs-05795761-ec4d-4dfb-8acd-b63775bcccba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05795761-ec4d-4dfb-8acd-b63775bcccba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d088079a-0324-4fc0-908d-aad5ae66d5d0' class='xr-var-data-in' type='checkbox'><label for='data-d088079a-0324-4fc0-908d-aad5ae66d5d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-03T12:00:00.000000000&#x27;, &#x27;2018-01-03T12:00:04.899999744&#x27;,\n",
" &#x27;2018-01-03T12:00:05.880000000&#x27;, ..., &#x27;2018-01-17T06:06:28.416099072&#x27;,\n",
" &#x27;2018-01-17T06:06:29.396099072&#x27;, &#x27;2018-01-17T06:06:32.336098816&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-263ba02e-09f5-4c04-8b63-b430fb892f48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-263ba02e-09f5-4c04-8b63-b430fb892f48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd3ef479-892d-4f4a-81ac-7a9d84aa2417' class='xr-var-data-in' type='checkbox'><label for='data-dd3ef479-892d-4f4a-81ac-7a9d84aa2417' 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 of measurement</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",
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" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
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" </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-763ba932-075b-4a0e-b50c-b9024bfd3ac5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-763ba932-075b-4a0e-b50c-b9024bfd3ac5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b46cf616-4a67-496e-abfd-c6bdedd8fc16' class='xr-var-data-in' type='checkbox'><label for='data-b46cf616-4a67-496e-abfd-c6bdedd8fc16' 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 of measurement</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",
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" <td> 377.74 kiB </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_centered</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00</div><input id='attrs-54edaf99-6d34-4f04-ba66-732ce1c4c765' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54edaf99-6d34-4f04-ba66-732ce1c4c765' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61cb4698-8fcf-4700-b2df-88e6d3669b6e' class='xr-var-data-in' type='checkbox'><label for='data-61cb4698-8fcf-4700-b2df-88e6d3669b6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_centered_bounds</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2018-01-03T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00</div><input id='attrs-ff6284d7-562c-4fa8-a1ca-5d0ff8b086a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff6284d7-562c-4fa8-a1ca-5d0ff8b086a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1458471-e9da-40cd-957b-f1a46feceda8' class='xr-var-data-in' type='checkbox'><label for='data-d1458471-e9da-40cd-957b-f1a46feceda8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2018-01-03T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-57a9991c-e6af-4486-99c0-693a01a6e84b' class='xr-section-summary-in' type='checkbox' ><label for='section-57a9991c-e6af-4486-99c0-693a01a6e84b' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-25187063-ac87-4740-86e0-f580cbc20dd1' class='xr-index-data-in' type='checkbox'/><label for='index-25187063-ac87-4740-86e0-f580cbc20dd1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([ &#x27;2018-01-03 12:00:00&#x27;,\n",
" &#x27;2018-01-03 12:00:04.899999744&#x27;,\n",
" &#x27;2018-01-03 12:00:05.880000&#x27;,\n",
" &#x27;2018-01-03 12:00:06.860000&#x27;,\n",
" &#x27;2018-01-03 12:00:07.840000&#x27;,\n",
" &#x27;2018-01-03 12:00:08.820000&#x27;,\n",
" &#x27;2018-01-03 12:00:09.800000&#x27;,\n",
" &#x27;2018-01-03 12:00:10.779999744&#x27;,\n",
" &#x27;2018-01-03 12:00:11.759999744&#x27;,\n",
" &#x27;2018-01-03 12:00:12.739999744&#x27;,\n",
" ...\n",
" &#x27;2018-01-17 06:06:21.556099072&#x27;,\n",
" &#x27;2018-01-17 06:06:22.536099072&#x27;,\n",
" &#x27;2018-01-17 06:06:23.516098816&#x27;,\n",
" &#x27;2018-01-17 06:06:24.496098816&#x27;,\n",
" &#x27;2018-01-17 06:06:25.476098816&#x27;,\n",
" &#x27;2018-01-17 06:06:26.456098816&#x27;,\n",
" &#x27;2018-01-17 06:06:27.436098816&#x27;,\n",
" &#x27;2018-01-17 06:06:28.416099072&#x27;,\n",
" &#x27;2018-01-17 06:06:29.396099072&#x27;,\n",
" &#x27;2018-01-17 06:06:32.336098816&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=632980, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56f93ef1-2913-406a-9353-bcea4b9ba27e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-56f93ef1-2913-406a-9353-bcea4b9ba27e' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
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"array(['2018-01-03T12:00:00.000000000', '2018-01-03T12:00:04.899999744',\n",
" '2018-01-03T12:00:05.880000000', ..., '2018-01-17T06:06:28.416099072',\n",
" '2018-01-17T06:06:29.396099072', '2018-01-17T06:06:32.336098816'],\n",
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"Coordinates:\n",
" * time (time) datetime64[ns] 2018-01-03T12:00:00 ... 2018-01-17T0...\n",
" longitude (time) float64 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" latitude (time) float64 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" time_centered datetime64[ns] 2018-01-03T12:00:00\n",
" time_counter datetime64[ns] 2018-01-03T12:00:00"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_track[\"time\"] = ds_track[\"time\"] - (ds_track.time[0] - ds_nemo.time_counter[0])\n",
"ds_track[\"time\"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "526c2bdf-f611-4c90-8a80-e39f459a465e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import xoak"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "906e772e-3d9c-4170-ae28-cf9ed9646e41",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.37 s, sys: 12 ms, total: 1.38 s\n",
"Wall time: 1.38 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"ds_nemo.xoak.set_index(['nav_lat', 'nav_lon'], 'sklearn_geo_balltree')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ec4661fa-974f-4432-99e8-2313bb95d15f",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3min 16s, sys: 877 ms, total: 3min 17s\n",
"Wall time: 18 s\n"
]
},
{
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" --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
" --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
" --xr-background-color: var(--jp-layout-color0, white);\n",
" --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
" --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"}\n",
"\n",
"html[theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
" --xr-border-color: #1F1F1F;\n",
" --xr-disabled-color: #515151;\n",
" --xr-background-color: #111111;\n",
" --xr-background-color-row-even: #111111;\n",
" --xr-background-color-row-odd: #313131;\n",
"}\n",
"\n",
".xr-wrap {\n",
" display: block !important;\n",
" min-width: 300px;\n",
" max-width: 700px;\n",
"}\n",
"\n",
".xr-text-repr-fallback {\n",
" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
" display: none;\n",
"}\n",
"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-obj-type,\n",
".xr-array-name {\n",
" margin-left: 2px;\n",
" margin-right: 10px;\n",
"}\n",
"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-section-item input {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (axis_nbounds: 2, time: 632980)\n",
"Coordinates:\n",
" nav_lat (time) float32 -65.6 -65.39 -65.29 ... -70.19 -70.36\n",
" nav_lon (time) float32 -3.25 -3.5 -3.5 ... -138.5 -138.8\n",
" time_centered datetime64[ns] 2018-01-03T12:00:00\n",
" time_counter datetime64[ns] 2018-01-03T12:00:00\n",
" x (time) int64 1136 1135 1135 1135 ... 595 595 595 594\n",
" y (time) int64 147 149 150 150 151 ... 100 99 99 98 96\n",
" * time (time) datetime64[ns] 2018-01-03T12:00:00 ... 2018-...\n",
" longitude (time) float64 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" latitude (time) float64 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
"Dimensions without coordinates: axis_nbounds\n",
"Data variables: (12/14)\n",
" deptht_bounds (axis_nbounds, time) float32 0.0 0.0 ... 6.194 6.194\n",
" time_centered_bounds (time, axis_nbounds) datetime64[ns] 2018-01-01 ... ...\n",
" time_counter_bounds (time, axis_nbounds) datetime64[ns] 2018-01-01 ... ...\n",
" votemper (time) float32 0.1954 0.1521 0.1361 ... -1.903 -1.906\n",
" vosaline (time) float32 33.49 33.52 33.54 ... 33.0 33.02 33.06\n",
" sowaflup (time) float32 2.072e-05 1.973e-05 ... -1.079e-05\n",
" ... ...\n",
" somxl010 (time) float32 14.0 13.76 13.51 ... 12.84 12.84 12.84\n",
" somixhgt (time) float32 15.21 14.63 14.53 ... 12.84 12.84 12.84\n",
" sowindsp (time) float32 4.463 4.384 4.348 ... 7.221 7.187 7.1\n",
" sohefldp (time) float32 nan nan nan nan nan ... nan nan nan nan\n",
" sowafldp (time) float32 2.368e-05 2.351e-05 ... 1.09e-05\n",
" sobowlin (time) float32 20.04 20.04 20.04 ... 12.84 12.84 12.84\n",
"Attributes:\n",
" name: ./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T\n",
" description: ocean T grid variables\n",
" title: ocean T grid variables\n",
" Conventions: CF-1.6\n",
" timeStamp: 2020-Jan-04 09:32:22 GMT\n",
" uuid: c1428d50-e458-41e7-83f6-b2813d89831b</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-4c75bd06-7b26-4b25-a335-e9ec1550076b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4c75bd06-7b26-4b25-a335-e9ec1550076b' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>axis_nbounds</span>: 2</li><li><span class='xr-has-index'>time</span>: 632980</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0f10abff-1700-4d70-ab50-4229c2931faf' class='xr-section-summary-in' type='checkbox' checked><label for='section-0f10abff-1700-4d70-ab50-4229c2931faf' 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>nav_lat</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-65.6 -65.39 ... -70.19 -70.36</div><input id='attrs-056b6c5e-f5b1-4be4-bdf0-7dea113c365b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-056b6c5e-f5b1-4be4-bdf0-7dea113c365b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43240aec-e923-4d00-9660-eec8ea039235' class='xr-var-data-in' type='checkbox'><label for='data-43240aec-e923-4d00-9660-eec8ea039235' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-65.60025 , -65.392876, -65.28857 , ..., -70.10849 , -70.193375,\n",
" -70.36211 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nav_lon</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.25 -3.5 -3.5 ... -138.5 -138.8</div><input id='attrs-d37805b8-acbd-4f49-a028-73e5ae65b295' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d37805b8-acbd-4f49-a028-73e5ae65b295' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-081d17b7-be61-4ac1-b6f9-16cc9bcec376' class='xr-var-data-in' type='checkbox'><label for='data-081d17b7-be61-4ac1-b6f9-16cc9bcec376' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ -3.25, -3.5 , -3.5 , ..., -138.5 , -138.5 , -138.75],\n",
" dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_centered</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00</div><input id='attrs-a3a1e547-b8e2-48eb-b9ee-2621c61c475b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3a1e547-b8e2-48eb-b9ee-2621c61c475b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6629611-e05a-4cd3-9274-e37615c37dcd' class='xr-var-data-in' type='checkbox'><label for='data-c6629611-e05a-4cd3-9274-e37615c37dcd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_centered_bounds</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2018-01-03T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00</div><input id='attrs-e05e40e5-09ad-44eb-af8c-50b94068caba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e05e40e5-09ad-44eb-af8c-50b94068caba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36c692ab-e2dd-4d5c-92bc-4afa8defaa1c' class='xr-var-data-in' type='checkbox'><label for='data-36c692ab-e2dd-4d5c-92bc-4afa8defaa1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2018-01-03T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1136 1135 1135 1135 ... 595 595 594</div><input id='attrs-5720936c-94e7-48fc-860f-1d6c1938b73d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5720936c-94e7-48fc-860f-1d6c1938b73d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78621c17-ea7a-47b1-bc5f-9b9869040da9' class='xr-var-data-in' type='checkbox'><label for='data-78621c17-ea7a-47b1-bc5f-9b9869040da9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1136, 1135, 1135, ..., 595, 595, 594])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>147 149 150 150 151 ... 99 99 98 96</div><input id='attrs-d23ddd93-2924-471b-a97c-16408f4e352c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d23ddd93-2924-471b-a97c-16408f4e352c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f8692a2-a9ac-46fe-9f2a-a22af4b827e3' class='xr-var-data-in' type='checkbox'><label for='data-0f8692a2-a9ac-46fe-9f2a-a22af4b827e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([147, 149, 150, ..., 99, 98, 96])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00 ... 2018-01-...</div><input id='attrs-08345449-467c-40cb-8be0-b6ec93c8c73b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08345449-467c-40cb-8be0-b6ec93c8c73b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0889f38a-db00-4e64-895a-26b9a488a71f' class='xr-var-data-in' type='checkbox'><label for='data-0889f38a-db00-4e64-895a-26b9a488a71f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-03T12:00:00.000000000&#x27;, &#x27;2018-01-03T12:00:04.899999744&#x27;,\n",
" &#x27;2018-01-03T12:00:05.880000000&#x27;, ..., &#x27;2018-01-17T06:06:28.416099072&#x27;,\n",
" &#x27;2018-01-17T06:06:29.396099072&#x27;, &#x27;2018-01-17T06:06:32.336098816&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-0badf3c3-a5ca-4bfe-9b6a-a6ad1dee7701' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0badf3c3-a5ca-4bfe-9b6a-a6ad1dee7701' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe34f720-23e8-47f8-b0da-2a246a80ef5a' class='xr-var-data-in' type='checkbox'><label for='data-fe34f720-23e8-47f8-b0da-2a246a80ef5a' 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 of measurement</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 style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 4.83 MiB </td>\n",
" <td> 377.74 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 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",
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"\n",
" <!-- Vertical lines -->\n",
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" <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
" <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
" <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"25\" />\n",
" <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"25\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\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\" >632980</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>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-bfe73e20-a5ce-455c-9988-78277dc225e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfe73e20-a5ce-455c-9988-78277dc225e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c56625b3-4b01-4008-bdf8-53f23be5135a' class='xr-var-data-in' type='checkbox'><label for='data-c56625b3-4b01-4008-bdf8-53f23be5135a' 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 of measurement</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 style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 4.83 MiB </td>\n",
" <td> 377.74 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 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=\"8\" y1=\"0\" x2=\"8\" y2=\"25\" />\n",
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" <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
" <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"25\" />\n",
" <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
" <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
" <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"25\" />\n",
" <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"25\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\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\" >632980</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></ul></div></li><li class='xr-section-item'><input id='section-6ec81730-dda9-4a6a-ab3d-5e8d44b2d04d' class='xr-section-summary-in' type='checkbox' checked><label for='section-6ec81730-dda9-4a6a-ab3d-5e8d44b2d04d' class='xr-section-summary' >Data variables: <span>(14)</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>deptht_bounds</span></div><div class='xr-var-dims'>(axis_nbounds, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 6.194 6.194 6.194</div><input id='attrs-a7d26c2a-8e80-4239-9ae1-351e9c9dfc93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7d26c2a-8e80-4239-9ae1-351e9c9dfc93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7cf1d22-323f-4785-bc67-8016ed7d0bdc' class='xr-var-data-in' type='checkbox'><label for='data-c7cf1d22-323f-4785-bc67-8016ed7d0bdc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[0. , 0. , 0. , ..., 0. , 0. ,\n",
" 0. ],\n",
" [6.1942425, 6.1942425, 6.1942425, ..., 6.1942425, 6.1942425,\n",
" 6.1942425]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_centered_bounds</span></div><div class='xr-var-dims'>(time, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01 ... 2018-01-21</div><input id='attrs-8dd3ea28-46fb-4640-a2cd-226651b89a70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8dd3ea28-46fb-4640-a2cd-226651b89a70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37a38bd2-3b44-453d-ba50-f473fac8dbe5' class='xr-var-data-in' type='checkbox'><label for='data-37a38bd2-3b44-453d-ba50-f473fac8dbe5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" ...,\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;]],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01 ... 2018-01-21</div><input id='attrs-59c462e0-eb52-4a5b-970d-29a021412e6c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59c462e0-eb52-4a5b-970d-29a021412e6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb514eb2-f2d5-4861-8c02-a54c1afc8ca1' class='xr-var-data-in' type='checkbox'><label for='data-bb514eb2-f2d5-4861-8c02-a54c1afc8ca1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" ...,\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;]],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>votemper</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1954 0.1521 ... -1.903 -1.906</div><input id='attrs-5f4fd738-d96b-4f1b-b5be-1db86f159f72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f4fd738-d96b-4f1b-b5be-1db86f159f72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db6923ee-897b-4603-a4ad-40a1c9d847b5' class='xr-var-data-in' type='checkbox'><label for='data-db6923ee-897b-4603-a4ad-40a1c9d847b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([ 0.19541377, 0.15214379, 0.13606602, ..., -1.9021894 ,\n",
" -1.9031733 , -1.9055033 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vosaline</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>33.49 33.52 33.54 ... 33.02 33.06</div><input id='attrs-380392ca-76e6-4c7e-b043-239735e51dca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-380392ca-76e6-4c7e-b043-239735e51dca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84b95ce5-bb23-418a-834a-21b0fb460622' class='xr-var-data-in' type='checkbox'><label for='data-84b95ce5-bb23-418a-834a-21b0fb460622' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>long_name :</span></dt><dd>Sea Water Salinity</dd><dt><span>units :</span></dt><dd>0.001</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([33.485085, 33.523163, 33.54488 , ..., 33.000164, 33.020435,\n",
" 33.06488 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowaflup</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.072e-05 1.973e-05 ... -1.079e-05</div><input id='attrs-cf6a3911-00c6-4050-a57a-f76013eb3b08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf6a3911-00c6-4050-a57a-f76013eb3b08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf7ddbb9-6d41-4e00-838f-4e490cc4f3c2' class='xr-var-data-in' type='checkbox'><label for='data-cf7ddbb9-6d41-4e00-838f-4e490cc4f3c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>water_flux_out_of_sea_ice_and_sea_water</dd><dt><span>long_name :</span></dt><dd>Net Upward Water Flux</dd><dt><span>units :</span></dt><dd>kg/m2/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([ 2.0716534e-05, 1.9730687e-05, 1.8308405e-05, ...,\n",
" -1.0019070e-05, -1.0157182e-05, -1.0794108e-05], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soshfldo</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>226.9 225.5 225.8 ... 86.33 87.52</div><input id='attrs-416934f1-3032-48d9-8ccc-f961ef19b065' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-416934f1-3032-48d9-8ccc-f961ef19b065' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a4be0fd-395e-4b4e-94f4-53a46a0f82fe' class='xr-var-data-in' type='checkbox'><label for='data-2a4be0fd-395e-4b4e-94f4-53a46a0f82fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>net_downward_shortwave_flux_at_sea_water_surface</dd><dt><span>long_name :</span></dt><dd>Shortwave Radiation</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([226.89275, 225.52641, 225.80482, ..., 85.35307, 86.33183,\n",
" 87.52219], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sohefldo</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>132.7 133.2 134.0 ... 14.66 14.75</div><input id='attrs-d1b51823-f960-479d-b811-e7fe96876ced' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1b51823-f960-479d-b811-e7fe96876ced' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-879147b4-897e-4f01-94b2-c961481fbbe1' class='xr-var-data-in' type='checkbox'><label for='data-879147b4-897e-4f01-94b2-c961481fbbe1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_downward_heat_flux_in_sea_water</dd><dt><span>long_name :</span></dt><dd>Net Downward Heat Flux</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([132.7295 , 133.22234 , 134.00003 , ..., 14.756789, 14.657626,\n",
" 14.754332], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>somxl010</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.0 13.76 13.51 ... 12.84 12.84</div><input id='attrs-5aa2f02f-5482-4789-b57b-1d84091d5308' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5aa2f02f-5482-4789-b57b-1d84091d5308' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-624524ba-a76e-4934-a9f2-d71a6d11c11b' class='xr-var-data-in' type='checkbox'><label for='data-624524ba-a76e-4934-a9f2-d71a6d11c11b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_sigma_theta</dd><dt><span>long_name :</span></dt><dd>Mixed Layer Depth (dsigma = 0.01 wrt 10m)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([14.004021, 13.763841, 13.511652, ..., 12.839149, 12.839149,\n",
" 12.839149], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>somixhgt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.21 14.63 14.53 ... 12.84 12.84</div><input id='attrs-86246e37-bfab-4e14-a928-4170e51de18f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86246e37-bfab-4e14-a928-4170e51de18f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47249a4c-d752-4952-b3cb-042d20c8f3b1' class='xr-var-data-in' type='checkbox'><label for='data-47249a4c-d752-4952-b3cb-042d20c8f3b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_vertical_tracer_diffusivity</dd><dt><span>long_name :</span></dt><dd>Turbocline depth (Kz = 5e-4)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([15.212641, 14.628489, 14.532416, ..., 12.839149, 12.839149,\n",
" 12.839149], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowindsp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.463 4.384 4.348 ... 7.187 7.1</div><input id='attrs-78eb788d-ebef-413a-81e3-6eb798be213e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78eb788d-ebef-413a-81e3-6eb798be213e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-077afdc7-a2b6-4637-a489-14b522368d7c' class='xr-var-data-in' type='checkbox'><label for='data-077afdc7-a2b6-4637-a489-14b522368d7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>long_name :</span></dt><dd>wind speed module</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([4.462639 , 4.383871 , 4.348183 , ..., 7.2213616, 7.186725 ,\n",
" 7.0996017], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sohefldp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-99511f29-a6c0-41ca-b4a9-bd1d6c94dab3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99511f29-a6c0-41ca-b4a9-bd1d6c94dab3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae131079-787b-46a7-b8c3-453caf78520c' class='xr-var-data-in' type='checkbox'><label for='data-ae131079-787b-46a7-b8c3-453caf78520c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>heat_flux_into_sea_water_due_to_newtonian_relaxation</dd><dt><span>long_name :</span></dt><dd>Surface Heat Flux: Damping</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowafldp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.368e-05 2.351e-05 ... 1.09e-05</div><input id='attrs-d5c9d014-58f2-4ae3-809d-966232013079' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5c9d014-58f2-4ae3-809d-966232013079' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb92c056-bf79-4063-9b42-39a4d423ee00' class='xr-var-data-in' type='checkbox'><label for='data-eb92c056-bf79-4063-9b42-39a4d423ee00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>water_flux_out_of_sea_water_due_to_newtonian_relaxation</dd><dt><span>long_name :</span></dt><dd>Surface Water Flux: Damping</dd><dt><span>units :</span></dt><dd>kg/m2/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([2.3677101e-05, 2.3514443e-05, 2.2799197e-05, ..., 1.0678544e-05,\n",
" 1.0772024e-05, 1.0900458e-05], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sobowlin</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.04 20.04 20.04 ... 12.84 12.84</div><input id='attrs-6e725c81-a254-439a-9736-c887aa1a8e5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e725c81-a254-439a-9736-c887aa1a8e5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9af7f42-3e5f-4a47-98cf-0259946e8f5e' class='xr-var-data-in' type='checkbox'><label for='data-e9af7f42-3e5f-4a47-98cf-0259946e8f5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_sigma_theta</dd><dt><span>long_name :</span></dt><dd>Mixed Layer Depth (dsigma = 0.01 wrt 10m)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>maximum</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([20.04454 , 20.04454 , 20.04454 , ..., 12.839149, 12.839149,\n",
" 12.839149], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc2dbffa-0b39-4ae9-a93f-7b491a5765e5' class='xr-section-summary-in' type='checkbox' ><label for='section-bc2dbffa-0b39-4ae9-a93f-7b491a5765e5' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3c82b4b8-2130-47dc-b1c3-9f63ec48f99c' class='xr-index-data-in' type='checkbox'/><label for='index-3c82b4b8-2130-47dc-b1c3-9f63ec48f99c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([ &#x27;2018-01-03 12:00:00&#x27;,\n",
" &#x27;2018-01-03 12:00:04.899999744&#x27;,\n",
" &#x27;2018-01-03 12:00:05.880000&#x27;,\n",
" &#x27;2018-01-03 12:00:06.860000&#x27;,\n",
" &#x27;2018-01-03 12:00:07.840000&#x27;,\n",
" &#x27;2018-01-03 12:00:08.820000&#x27;,\n",
" &#x27;2018-01-03 12:00:09.800000&#x27;,\n",
" &#x27;2018-01-03 12:00:10.779999744&#x27;,\n",
" &#x27;2018-01-03 12:00:11.759999744&#x27;,\n",
" &#x27;2018-01-03 12:00:12.739999744&#x27;,\n",
" ...\n",
" &#x27;2018-01-17 06:06:21.556099072&#x27;,\n",
" &#x27;2018-01-17 06:06:22.536099072&#x27;,\n",
" &#x27;2018-01-17 06:06:23.516098816&#x27;,\n",
" &#x27;2018-01-17 06:06:24.496098816&#x27;,\n",
" &#x27;2018-01-17 06:06:25.476098816&#x27;,\n",
" &#x27;2018-01-17 06:06:26.456098816&#x27;,\n",
" &#x27;2018-01-17 06:06:27.436098816&#x27;,\n",
" &#x27;2018-01-17 06:06:28.416099072&#x27;,\n",
" &#x27;2018-01-17 06:06:29.396099072&#x27;,\n",
" &#x27;2018-01-17 06:06:32.336098816&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=632980, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-048c5619-0056-4709-8158-d80177ffdf55' class='xr-section-summary-in' type='checkbox' checked><label for='section-048c5619-0056-4709-8158-d80177ffdf55' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T</dd><dt><span>description :</span></dt><dd>ocean T grid variables</dd><dt><span>title :</span></dt><dd>ocean T grid variables</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>timeStamp :</span></dt><dd>2020-Jan-04 09:32:22 GMT</dd><dt><span>uuid :</span></dt><dd>c1428d50-e458-41e7-83f6-b2813d89831b</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (axis_nbounds: 2, time: 632980)\n",
"Coordinates:\n",
" nav_lat (time) float32 -65.6 -65.39 -65.29 ... -70.19 -70.36\n",
" nav_lon (time) float32 -3.25 -3.5 -3.5 ... -138.5 -138.8\n",
" time_centered datetime64[ns] 2018-01-03T12:00:00\n",
" time_counter datetime64[ns] 2018-01-03T12:00:00\n",
" x (time) int64 1136 1135 1135 1135 ... 595 595 595 594\n",
" y (time) int64 147 149 150 150 151 ... 100 99 99 98 96\n",
" * time (time) datetime64[ns] 2018-01-03T12:00:00 ... 2018-...\n",
" longitude (time) float64 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" latitude (time) float64 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
"Dimensions without coordinates: axis_nbounds\n",
"Data variables: (12/14)\n",
" deptht_bounds (axis_nbounds, time) float32 0.0 0.0 ... 6.194 6.194\n",
" time_centered_bounds (time, axis_nbounds) datetime64[ns] 2018-01-01 ... ...\n",
" time_counter_bounds (time, axis_nbounds) datetime64[ns] 2018-01-01 ... ...\n",
" votemper (time) float32 0.1954 0.1521 0.1361 ... -1.903 -1.906\n",
" vosaline (time) float32 33.49 33.52 33.54 ... 33.0 33.02 33.06\n",
" sowaflup (time) float32 2.072e-05 1.973e-05 ... -1.079e-05\n",
" ... ...\n",
" somxl010 (time) float32 14.0 13.76 13.51 ... 12.84 12.84 12.84\n",
" somixhgt (time) float32 15.21 14.63 14.53 ... 12.84 12.84 12.84\n",
" sowindsp (time) float32 4.463 4.384 4.348 ... 7.221 7.187 7.1\n",
" sohefldp (time) float32 nan nan nan nan nan ... nan nan nan nan\n",
" sowafldp (time) float32 2.368e-05 2.351e-05 ... 1.09e-05\n",
" sobowlin (time) float32 20.04 20.04 20.04 ... 12.84 12.84 12.84\n",
"Attributes:\n",
" name: ./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T\n",
" description: ocean T grid variables\n",
" title: ocean T grid variables\n",
" Conventions: CF-1.6\n",
" timeStamp: 2020-Jan-04 09:32:22 GMT\n",
" uuid: c1428d50-e458-41e7-83f6-b2813d89831b"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"\n",
"ds_nemo_along_track = ds_nemo.xoak.sel(\n",
" nav_lon=ds_track.longitude,\n",
" nav_lat=ds_track.latitude,\n",
").sel(\n",
" time_counter=ds_track.time,\n",
" method=\"nearest\",\n",
")\n",
"\n",
"ds_nemo_along_track = ds_nemo_along_track.assign_coords(\n",
" longitude=ds_track.longitude,\n",
" latitude=ds_track.latitude,\n",
" time=ds_track.time,\n",
")\n",
"\n",
"ds_nemo_along_track"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "9e32b7ab-8227-446c-8435-6a23a82813df",
"metadata": {
"tags": []
},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (axis_nbounds: 2, time: 632980)\n",
"Coordinates:\n",
" nav_lat (time) float32 -65.6 -65.39 -65.29 ... -70.19 -70.36\n",
" nav_lon (time) float32 -3.25 -3.5 -3.5 ... -138.5 -138.8\n",
" time_centered datetime64[ns] 2018-01-03T12:00:00\n",
" time_counter datetime64[ns] 2018-01-03T12:00:00\n",
" x (time) int64 1136 1135 1135 1135 ... 595 595 595 594\n",
" y (time) int64 147 149 150 150 151 ... 100 99 99 98 96\n",
" * time (time) datetime64[ns] 2018-01-03T12:00:00 ... 2018-...\n",
" longitude (time) float64 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" latitude (time) float64 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" days_elapsed (time) float64 0.0 5.671e-05 6.806e-05 ... 13.75 13.75\n",
"Dimensions without coordinates: axis_nbounds\n",
"Data variables: (12/14)\n",
" deptht_bounds (axis_nbounds, time) float32 0.0 0.0 ... 6.194 6.194\n",
" time_centered_bounds (time, axis_nbounds) datetime64[ns] 2018-01-01 ... ...\n",
" time_counter_bounds (time, axis_nbounds) datetime64[ns] 2018-01-01 ... ...\n",
" votemper (time) float32 0.1954 0.1521 0.1361 ... -1.903 -1.906\n",
" vosaline (time) float32 33.49 33.52 33.54 ... 33.0 33.02 33.06\n",
" sowaflup (time) float32 2.072e-05 1.973e-05 ... -1.079e-05\n",
" ... ...\n",
" somxl010 (time) float32 14.0 13.76 13.51 ... 12.84 12.84 12.84\n",
" somixhgt (time) float32 15.21 14.63 14.53 ... 12.84 12.84 12.84\n",
" sowindsp (time) float32 4.463 4.384 4.348 ... 7.221 7.187 7.1\n",
" sohefldp (time) float32 nan nan nan nan nan ... nan nan nan nan\n",
" sowafldp (time) float32 2.368e-05 2.351e-05 ... 1.09e-05\n",
" sobowlin (time) float32 20.04 20.04 20.04 ... 12.84 12.84 12.84\n",
"Attributes:\n",
" name: ./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T\n",
" description: ocean T grid variables\n",
" title: ocean T grid variables\n",
" Conventions: CF-1.6\n",
" timeStamp: 2020-Jan-04 09:32:22 GMT\n",
" uuid: c1428d50-e458-41e7-83f6-b2813d89831b</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-ae7193fb-0dd7-49b4-9185-2e5d6ef92fcc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae7193fb-0dd7-49b4-9185-2e5d6ef92fcc' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>axis_nbounds</span>: 2</li><li><span class='xr-has-index'>time</span>: 632980</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f3d1d8c5-9804-482c-ab47-75e3c462f8f5' class='xr-section-summary-in' type='checkbox' checked><label for='section-f3d1d8c5-9804-482c-ab47-75e3c462f8f5' class='xr-section-summary' >Coordinates: <span>(10)</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>nav_lat</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-65.6 -65.39 ... -70.19 -70.36</div><input id='attrs-252f392a-6336-48cd-a3e4-53b8d220cfc4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-252f392a-6336-48cd-a3e4-53b8d220cfc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f0aebec-c9a7-458b-9726-ab6f9a969668' class='xr-var-data-in' type='checkbox'><label for='data-9f0aebec-c9a7-458b-9726-ab6f9a969668' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-65.60025 , -65.392876, -65.28857 , ..., -70.10849 , -70.193375,\n",
" -70.36211 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nav_lon</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.25 -3.5 -3.5 ... -138.5 -138.8</div><input id='attrs-d1a58fab-0a38-4f18-a45c-d492d8b2e838' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1a58fab-0a38-4f18-a45c-d492d8b2e838' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a90beebb-c8f1-4769-8f22-0314f0fc0846' class='xr-var-data-in' type='checkbox'><label for='data-a90beebb-c8f1-4769-8f22-0314f0fc0846' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ -3.25, -3.5 , -3.5 , ..., -138.5 , -138.5 , -138.75],\n",
" dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_centered</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00</div><input id='attrs-80c26154-fc4b-4741-8ad1-8e547f642603' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80c26154-fc4b-4741-8ad1-8e547f642603' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1082fca1-f90f-473a-a6af-5a46923da352' class='xr-var-data-in' type='checkbox'><label for='data-1082fca1-f90f-473a-a6af-5a46923da352' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_centered_bounds</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2018-01-03T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00</div><input id='attrs-13ffe7bd-d01d-401d-a0c5-41d6ddd13a7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13ffe7bd-d01d-401d-a0c5-41d6ddd13a7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-597157d9-c588-4529-b819-fa973dfe0d11' class='xr-var-data-in' type='checkbox'><label for='data-597157d9-c588-4529-b819-fa973dfe0d11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2018-01-03T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1136 1135 1135 1135 ... 595 595 594</div><input id='attrs-2a3c5895-b299-46fb-9a6f-ee2cf79cc91f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a3c5895-b299-46fb-9a6f-ee2cf79cc91f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acdb59ca-9453-46f8-b435-182fe64f3c60' class='xr-var-data-in' type='checkbox'><label for='data-acdb59ca-9453-46f8-b435-182fe64f3c60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1136, 1135, 1135, ..., 595, 595, 594])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>147 149 150 150 151 ... 99 99 98 96</div><input id='attrs-c0150bd6-4b00-4864-a6ab-cd02fd051252' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0150bd6-4b00-4864-a6ab-cd02fd051252' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7dbd9c5-906b-41b3-b75f-3b4853bc2b44' class='xr-var-data-in' type='checkbox'><label for='data-d7dbd9c5-906b-41b3-b75f-3b4853bc2b44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([147, 149, 150, ..., 99, 98, 96])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00 ... 2018-01-...</div><input id='attrs-5cea65eb-4565-44ca-a77e-b192e526e6ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cea65eb-4565-44ca-a77e-b192e526e6ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fb561c7-f723-40cb-8212-aecc3da08978' class='xr-var-data-in' type='checkbox'><label for='data-4fb561c7-f723-40cb-8212-aecc3da08978' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-03T12:00:00.000000000&#x27;, &#x27;2018-01-03T12:00:04.899999744&#x27;,\n",
" &#x27;2018-01-03T12:00:05.880000000&#x27;, ..., &#x27;2018-01-17T06:06:28.416099072&#x27;,\n",
" &#x27;2018-01-17T06:06:29.396099072&#x27;, &#x27;2018-01-17T06:06:32.336098816&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-659e0778-5b83-41bc-8b48-d12b4db429c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-659e0778-5b83-41bc-8b48-d12b4db429c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4458432-c2c7-46c4-a12e-8657d6412a7d' class='xr-var-data-in' type='checkbox'><label for='data-a4458432-c2c7-46c4-a12e-8657d6412a7d' 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 of measurement</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 style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 4.83 MiB </td>\n",
" <td> 377.74 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 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",
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"\n",
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" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"25\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
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"\n",
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" <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\" >632980</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>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-61161d92-deda-4eef-b785-6709d7816a66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61161d92-deda-4eef-b785-6709d7816a66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ebd2a12-e9e1-47da-a7c6-2aa64824fbaa' class='xr-var-data-in' type='checkbox'><label for='data-1ebd2a12-e9e1-47da-a7c6-2aa64824fbaa' 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 of measurement</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 style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 4.83 MiB </td>\n",
" <td> 377.74 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 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=\"8\" y1=\"0\" x2=\"8\" y2=\"25\" />\n",
" <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"25\" />\n",
" <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
" <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"25\" />\n",
" <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
" <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
" <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"25\" />\n",
" <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"25\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\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\" >632980</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>days_elapsed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.671e-05 ... 13.75 13.75</div><input id='attrs-41dc8ec3-4fa8-4c4b-a2b2-7042c5db6c37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41dc8ec3-4fa8-4c4b-a2b2-7042c5db6c37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ed97ee2-81b2-45b7-96e7-7a2aa3e30ac7' class='xr-var-data-in' type='checkbox'><label for='data-9ed97ee2-81b2-45b7-96e7-7a2aa3e30ac7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.00000000e+00, 5.67129600e-05, 6.80555556e-05, ...,\n",
" 1.37544956e+01, 1.37545069e+01, 1.37545409e+01])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fcfe599c-f403-45f2-83c6-8398624b5580' class='xr-section-summary-in' type='checkbox' checked><label for='section-fcfe599c-f403-45f2-83c6-8398624b5580' class='xr-section-summary' >Data variables: <span>(14)</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>deptht_bounds</span></div><div class='xr-var-dims'>(axis_nbounds, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 6.194 6.194 6.194</div><input id='attrs-48977ac0-00dc-47b7-8b58-25ffe9809173' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48977ac0-00dc-47b7-8b58-25ffe9809173' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-308e05f0-27c2-4b05-8e2b-b42c6982c2eb' class='xr-var-data-in' type='checkbox'><label for='data-308e05f0-27c2-4b05-8e2b-b42c6982c2eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[0. , 0. , 0. , ..., 0. , 0. ,\n",
" 0. ],\n",
" [6.1942425, 6.1942425, 6.1942425, ..., 6.1942425, 6.1942425,\n",
" 6.1942425]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_centered_bounds</span></div><div class='xr-var-dims'>(time, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01 ... 2018-01-21</div><input id='attrs-2c15b767-3de2-4f54-b9cd-7ac1a2874a13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c15b767-3de2-4f54-b9cd-7ac1a2874a13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df8e5a98-d3a5-4839-8932-3ca2deaf21f2' class='xr-var-data-in' type='checkbox'><label for='data-df8e5a98-d3a5-4839-8932-3ca2deaf21f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" ...,\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;]],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01 ... 2018-01-21</div><input id='attrs-7ba15518-b114-4c1e-8ff6-62b3c7300fb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ba15518-b114-4c1e-8ff6-62b3c7300fb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcb1fc1f-171f-49b8-af38-49920297ca75' class='xr-var-data-in' type='checkbox'><label for='data-dcb1fc1f-171f-49b8-af38-49920297ca75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" ...,\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;]],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>votemper</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1954 0.1521 ... -1.903 -1.906</div><input id='attrs-0748c7d6-a7b0-4dd2-aaad-9743d854932e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0748c7d6-a7b0-4dd2-aaad-9743d854932e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-716ef2d3-5c86-45df-9350-5aa09daa238c' class='xr-var-data-in' type='checkbox'><label for='data-716ef2d3-5c86-45df-9350-5aa09daa238c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([ 0.19541377, 0.15214379, 0.13606602, ..., -1.9021894 ,\n",
" -1.9031733 , -1.9055033 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vosaline</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>33.49 33.52 33.54 ... 33.02 33.06</div><input id='attrs-7f4caea5-a8ee-44df-ae31-54653d89c9cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f4caea5-a8ee-44df-ae31-54653d89c9cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c01fbbf-7389-4681-bb53-7add230f2a95' class='xr-var-data-in' type='checkbox'><label for='data-3c01fbbf-7389-4681-bb53-7add230f2a95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>long_name :</span></dt><dd>Sea Water Salinity</dd><dt><span>units :</span></dt><dd>0.001</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([33.485085, 33.523163, 33.54488 , ..., 33.000164, 33.020435,\n",
" 33.06488 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowaflup</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.072e-05 1.973e-05 ... -1.079e-05</div><input id='attrs-609c9018-9b71-424b-a0ce-9ee69df6e7e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-609c9018-9b71-424b-a0ce-9ee69df6e7e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9da1e8f5-2561-4dc5-b250-1ae0808302af' class='xr-var-data-in' type='checkbox'><label for='data-9da1e8f5-2561-4dc5-b250-1ae0808302af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>water_flux_out_of_sea_ice_and_sea_water</dd><dt><span>long_name :</span></dt><dd>Net Upward Water Flux</dd><dt><span>units :</span></dt><dd>kg/m2/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([ 2.0716534e-05, 1.9730687e-05, 1.8308405e-05, ...,\n",
" -1.0019070e-05, -1.0157182e-05, -1.0794108e-05], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soshfldo</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>226.9 225.5 225.8 ... 86.33 87.52</div><input id='attrs-82d8b5e9-80c0-4c18-8cdc-9f86a64d14b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82d8b5e9-80c0-4c18-8cdc-9f86a64d14b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bee2edb1-e84b-4c2b-bb3f-8dbfc6cd6dc6' class='xr-var-data-in' type='checkbox'><label for='data-bee2edb1-e84b-4c2b-bb3f-8dbfc6cd6dc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>net_downward_shortwave_flux_at_sea_water_surface</dd><dt><span>long_name :</span></dt><dd>Shortwave Radiation</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([226.89275, 225.52641, 225.80482, ..., 85.35307, 86.33183,\n",
" 87.52219], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sohefldo</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>132.7 133.2 134.0 ... 14.66 14.75</div><input id='attrs-6eb1dd0d-0211-4803-919e-55b1ead59513' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6eb1dd0d-0211-4803-919e-55b1ead59513' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67dd5019-a01b-4180-b000-a2889c6d6d9d' class='xr-var-data-in' type='checkbox'><label for='data-67dd5019-a01b-4180-b000-a2889c6d6d9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_downward_heat_flux_in_sea_water</dd><dt><span>long_name :</span></dt><dd>Net Downward Heat Flux</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([132.7295 , 133.22234 , 134.00003 , ..., 14.756789, 14.657626,\n",
" 14.754332], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>somxl010</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.0 13.76 13.51 ... 12.84 12.84</div><input id='attrs-846802e1-9a7a-4847-a068-5a7d3837e443' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-846802e1-9a7a-4847-a068-5a7d3837e443' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2079f645-da2c-42e8-8121-44b55894fa33' class='xr-var-data-in' type='checkbox'><label for='data-2079f645-da2c-42e8-8121-44b55894fa33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_sigma_theta</dd><dt><span>long_name :</span></dt><dd>Mixed Layer Depth (dsigma = 0.01 wrt 10m)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([14.004021, 13.763841, 13.511652, ..., 12.839149, 12.839149,\n",
" 12.839149], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>somixhgt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.21 14.63 14.53 ... 12.84 12.84</div><input id='attrs-68affcee-acb9-4912-9300-4f0d8d5f9a66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68affcee-acb9-4912-9300-4f0d8d5f9a66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34ddefee-27fc-4bad-8cd4-1f8453bd58e3' class='xr-var-data-in' type='checkbox'><label for='data-34ddefee-27fc-4bad-8cd4-1f8453bd58e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_vertical_tracer_diffusivity</dd><dt><span>long_name :</span></dt><dd>Turbocline depth (Kz = 5e-4)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([15.212641, 14.628489, 14.532416, ..., 12.839149, 12.839149,\n",
" 12.839149], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowindsp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.463 4.384 4.348 ... 7.187 7.1</div><input id='attrs-c519bc81-7f6c-4943-9e89-9d975fbe0426' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c519bc81-7f6c-4943-9e89-9d975fbe0426' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b23f17bc-ad67-47bc-a9ac-0b581d45007e' class='xr-var-data-in' type='checkbox'><label for='data-b23f17bc-ad67-47bc-a9ac-0b581d45007e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>long_name :</span></dt><dd>wind speed module</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([4.462639 , 4.383871 , 4.348183 , ..., 7.2213616, 7.186725 ,\n",
" 7.0996017], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sohefldp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-9239c06b-7cff-4d32-abde-5f8eb9ab0ca1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9239c06b-7cff-4d32-abde-5f8eb9ab0ca1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6db93dfc-d44a-493b-b2ac-8895feda2521' class='xr-var-data-in' type='checkbox'><label for='data-6db93dfc-d44a-493b-b2ac-8895feda2521' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>heat_flux_into_sea_water_due_to_newtonian_relaxation</dd><dt><span>long_name :</span></dt><dd>Surface Heat Flux: Damping</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowafldp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.368e-05 2.351e-05 ... 1.09e-05</div><input id='attrs-45150eba-188d-4c98-a29a-5e9b8ea3075f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45150eba-188d-4c98-a29a-5e9b8ea3075f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05dd22fb-4161-4841-a70a-a40af9d027d8' class='xr-var-data-in' type='checkbox'><label for='data-05dd22fb-4161-4841-a70a-a40af9d027d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>water_flux_out_of_sea_water_due_to_newtonian_relaxation</dd><dt><span>long_name :</span></dt><dd>Surface Water Flux: Damping</dd><dt><span>units :</span></dt><dd>kg/m2/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([2.3677101e-05, 2.3514443e-05, 2.2799197e-05, ..., 1.0678544e-05,\n",
" 1.0772024e-05, 1.0900458e-05], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sobowlin</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.04 20.04 20.04 ... 12.84 12.84</div><input id='attrs-98a0c512-1abe-4a17-88f0-53e4831c0372' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98a0c512-1abe-4a17-88f0-53e4831c0372' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66d1fce2-85ae-452d-8389-816352dd8d3a' class='xr-var-data-in' type='checkbox'><label for='data-66d1fce2-85ae-452d-8389-816352dd8d3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_sigma_theta</dd><dt><span>long_name :</span></dt><dd>Mixed Layer Depth (dsigma = 0.01 wrt 10m)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>maximum</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([20.04454 , 20.04454 , 20.04454 , ..., 12.839149, 12.839149,\n",
" 12.839149], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-13343919-0f41-41fb-869a-84edc51f8502' class='xr-section-summary-in' type='checkbox' ><label for='section-13343919-0f41-41fb-869a-84edc51f8502' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-311b33df-2394-4006-bb04-284043e4521c' class='xr-index-data-in' type='checkbox'/><label for='index-311b33df-2394-4006-bb04-284043e4521c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([ &#x27;2018-01-03 12:00:00&#x27;,\n",
" &#x27;2018-01-03 12:00:04.899999744&#x27;,\n",
" &#x27;2018-01-03 12:00:05.880000&#x27;,\n",
" &#x27;2018-01-03 12:00:06.860000&#x27;,\n",
" &#x27;2018-01-03 12:00:07.840000&#x27;,\n",
" &#x27;2018-01-03 12:00:08.820000&#x27;,\n",
" &#x27;2018-01-03 12:00:09.800000&#x27;,\n",
" &#x27;2018-01-03 12:00:10.779999744&#x27;,\n",
" &#x27;2018-01-03 12:00:11.759999744&#x27;,\n",
" &#x27;2018-01-03 12:00:12.739999744&#x27;,\n",
" ...\n",
" &#x27;2018-01-17 06:06:21.556099072&#x27;,\n",
" &#x27;2018-01-17 06:06:22.536099072&#x27;,\n",
" &#x27;2018-01-17 06:06:23.516098816&#x27;,\n",
" &#x27;2018-01-17 06:06:24.496098816&#x27;,\n",
" &#x27;2018-01-17 06:06:25.476098816&#x27;,\n",
" &#x27;2018-01-17 06:06:26.456098816&#x27;,\n",
" &#x27;2018-01-17 06:06:27.436098816&#x27;,\n",
" &#x27;2018-01-17 06:06:28.416099072&#x27;,\n",
" &#x27;2018-01-17 06:06:29.396099072&#x27;,\n",
" &#x27;2018-01-17 06:06:32.336098816&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=632980, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-be258954-6b04-4da9-8b1b-d3dfb97839c8' class='xr-section-summary-in' type='checkbox' checked><label for='section-be258954-6b04-4da9-8b1b-d3dfb97839c8' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T</dd><dt><span>description :</span></dt><dd>ocean T grid variables</dd><dt><span>title :</span></dt><dd>ocean T grid variables</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>timeStamp :</span></dt><dd>2020-Jan-04 09:32:22 GMT</dd><dt><span>uuid :</span></dt><dd>c1428d50-e458-41e7-83f6-b2813d89831b</dd></dl></div></li></ul></div></div>"
],
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"<xarray.Dataset>\n",
"Dimensions: (axis_nbounds: 2, time: 632980)\n",
"Coordinates:\n",
" nav_lat (time) float32 -65.6 -65.39 -65.29 ... -70.19 -70.36\n",
" nav_lon (time) float32 -3.25 -3.5 -3.5 ... -138.5 -138.8\n",
" time_centered datetime64[ns] 2018-01-03T12:00:00\n",
" time_counter datetime64[ns] 2018-01-03T12:00:00\n",
" x (time) int64 1136 1135 1135 1135 ... 595 595 595 594\n",
" y (time) int64 147 149 150 150 151 ... 100 99 99 98 96\n",
" * time (time) datetime64[ns] 2018-01-03T12:00:00 ... 2018-...\n",
" longitude (time) float64 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
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" days_elapsed (time) float64 0.0 5.671e-05 6.806e-05 ... 13.75 13.75\n",
"Dimensions without coordinates: axis_nbounds\n",
"Data variables: (12/14)\n",
" deptht_bounds (axis_nbounds, time) float32 0.0 0.0 ... 6.194 6.194\n",
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" time_counter_bounds (time, axis_nbounds) datetime64[ns] 2018-01-01 ... ...\n",
" votemper (time) float32 0.1954 0.1521 0.1361 ... -1.903 -1.906\n",
" vosaline (time) float32 33.49 33.52 33.54 ... 33.0 33.02 33.06\n",
" sowaflup (time) float32 2.072e-05 1.973e-05 ... -1.079e-05\n",
" ... ...\n",
" somxl010 (time) float32 14.0 13.76 13.51 ... 12.84 12.84 12.84\n",
" somixhgt (time) float32 15.21 14.63 14.53 ... 12.84 12.84 12.84\n",
" sowindsp (time) float32 4.463 4.384 4.348 ... 7.221 7.187 7.1\n",
" sohefldp (time) float32 nan nan nan nan nan ... nan nan nan nan\n",
" sowafldp (time) float32 2.368e-05 2.351e-05 ... 1.09e-05\n",
" sobowlin (time) float32 20.04 20.04 20.04 ... 12.84 12.84 12.84\n",
"Attributes:\n",
" name: ./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T\n",
" description: ocean T grid variables\n",
" title: ocean T grid variables\n",
" Conventions: CF-1.6\n",
" timeStamp: 2020-Jan-04 09:32:22 GMT\n",
" uuid: c1428d50-e458-41e7-83f6-b2813d89831b"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_nemo_along_track[\"days_elapsed\"] = (\n",
" ds_nemo_along_track.time \n",
" - ds_nemo_along_track.time[0]\n",
") / np.timedelta64(24, \"h\")\n",
"\n",
"ds_nemo_along_track = ds_nemo_along_track.set_coords([\"days_elapsed\", ])\n",
"\n",
"ds_nemo_along_track"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c3491d5a-ae0e-4cba-8c9c-ac87116f12b5",
"metadata": {
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},
"outputs": [
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".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (axis_nbounds: 2, time: 632980)\n",
"Coordinates:\n",
" nav_lat (time) float32 -65.6 -65.39 -65.29 ... -70.19 -70.36\n",
" nav_lon (time) float32 -3.25 -3.5 -3.5 ... -138.5 -138.8\n",
" time_centered datetime64[ns] 2018-01-03T12:00:00\n",
" time_counter datetime64[ns] 2018-01-03T12:00:00\n",
" x (time) int64 1136 1135 1135 1135 ... 595 595 595 594\n",
" y (time) int64 147 149 150 150 151 ... 100 99 99 98 96\n",
" * time (time) datetime64[ns] 2018-01-03T12:00:00 ... 2018-...\n",
" longitude (time) float64 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" latitude (time) float64 dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;\n",
" days_elapsed (time) float64 0.0 5.671e-05 6.806e-05 ... 13.75 13.75\n",
"Dimensions without coordinates: axis_nbounds\n",
"Data variables: (12/14)\n",
" deptht_bounds (axis_nbounds, time) float32 0.0 0.0 ... 6.194 6.194\n",
" time_centered_bounds (time, axis_nbounds) datetime64[ns] 2018-01-01 ... ...\n",
" time_counter_bounds (time, axis_nbounds) datetime64[ns] 2018-01-01 ... ...\n",
" votemper (time) float32 0.1954 0.1521 0.1361 ... -1.903 -1.906\n",
" vosaline (time) float32 33.49 33.52 33.54 ... 33.0 33.02 33.06\n",
" sowaflup (time) float32 2.072e-05 1.973e-05 ... -1.079e-05\n",
" ... ...\n",
" somxl010 (time) float32 14.0 13.76 13.51 ... 12.84 12.84 12.84\n",
" somixhgt (time) float32 15.21 14.63 14.53 ... 12.84 12.84 12.84\n",
" sowindsp (time) float32 4.463 4.384 4.348 ... 7.221 7.187 7.1\n",
" sohefldp (time) float32 nan nan nan nan nan ... nan nan nan nan\n",
" sowafldp (time) float32 2.368e-05 2.351e-05 ... 1.09e-05\n",
" sobowlin (time) float32 20.04 20.04 20.04 ... 12.84 12.84 12.84\n",
"Attributes:\n",
" name: ./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T\n",
" description: ocean T grid variables\n",
" title: ocean T grid variables\n",
" Conventions: CF-1.6\n",
" timeStamp: 2020-Jan-04 09:32:22 GMT\n",
" uuid: c1428d50-e458-41e7-83f6-b2813d89831b</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-a57c629f-bb37-436a-954f-85a887d67794' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a57c629f-bb37-436a-954f-85a887d67794' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>axis_nbounds</span>: 2</li><li><span class='xr-has-index'>time</span>: 632980</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-00246a6e-2377-4d8a-844e-b13edfe671d1' class='xr-section-summary-in' type='checkbox' checked><label for='section-00246a6e-2377-4d8a-844e-b13edfe671d1' class='xr-section-summary' >Coordinates: <span>(10)</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>nav_lat</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-65.6 -65.39 ... -70.19 -70.36</div><input id='attrs-29193123-e27e-4680-9ce9-2cae0feaab86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29193123-e27e-4680-9ce9-2cae0feaab86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf0e35a9-20ae-48a8-a61b-aa827147b77e' class='xr-var-data-in' type='checkbox'><label for='data-bf0e35a9-20ae-48a8-a61b-aa827147b77e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-65.60025 , -65.392876, -65.28857 , ..., -70.10849 , -70.193375,\n",
" -70.36211 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nav_lon</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.25 -3.5 -3.5 ... -138.5 -138.8</div><input id='attrs-75231b82-2e11-438f-8794-08a907bd28ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75231b82-2e11-438f-8794-08a907bd28ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be4d0d74-59de-43b0-aed9-19cc76385857' class='xr-var-data-in' type='checkbox'><label for='data-be4d0d74-59de-43b0-aed9-19cc76385857' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ -3.25, -3.5 , -3.5 , ..., -138.5 , -138.5 , -138.75],\n",
" dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_centered</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00</div><input id='attrs-a5b009f2-72cb-4c59-a796-48f3753b3210' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5b009f2-72cb-4c59-a796-48f3753b3210' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3928daf7-7835-47ac-9a4d-38421181676b' class='xr-var-data-in' type='checkbox'><label for='data-3928daf7-7835-47ac-9a4d-38421181676b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_centered_bounds</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2018-01-03T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00</div><input id='attrs-fe2d185a-d9e6-4381-9b5e-4a1cf8b87c4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe2d185a-d9e6-4381-9b5e-4a1cf8b87c4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c02d1f84-53ca-4a71-89a3-1c517d98a995' class='xr-var-data-in' type='checkbox'><label for='data-c02d1f84-53ca-4a71-89a3-1c517d98a995' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2018-01-03T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1136 1135 1135 1135 ... 595 595 594</div><input id='attrs-63fe490c-4c8f-4ef1-858b-40888c78f9b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63fe490c-4c8f-4ef1-858b-40888c78f9b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c69cb83e-4a1f-4c46-8272-2c437568f46c' class='xr-var-data-in' type='checkbox'><label for='data-c69cb83e-4a1f-4c46-8272-2c437568f46c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1136, 1135, 1135, ..., 595, 595, 594])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>147 149 150 150 151 ... 99 99 98 96</div><input id='attrs-10b8238d-fa24-460c-a3bb-fbf83e665141' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10b8238d-fa24-460c-a3bb-fbf83e665141' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0d4d2b1-dd1f-4ee4-90fb-f26fb2b7b7e7' class='xr-var-data-in' type='checkbox'><label for='data-c0d4d2b1-dd1f-4ee4-90fb-f26fb2b7b7e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([147, 149, 150, ..., 99, 98, 96])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-03T12:00:00 ... 2018-01-...</div><input id='attrs-5c12ff7f-e45c-4e7e-95f0-afeb1883c239' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c12ff7f-e45c-4e7e-95f0-afeb1883c239' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d675f625-b6d4-4b30-b998-460a2ba4c169' class='xr-var-data-in' type='checkbox'><label for='data-d675f625-b6d4-4b30-b998-460a2ba4c169' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-03T12:00:00.000000000&#x27;, &#x27;2018-01-03T12:00:04.899999744&#x27;,\n",
" &#x27;2018-01-03T12:00:05.880000000&#x27;, ..., &#x27;2018-01-17T06:06:28.416099072&#x27;,\n",
" &#x27;2018-01-17T06:06:29.396099072&#x27;, &#x27;2018-01-17T06:06:32.336098816&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-60ace27c-618c-4af9-93d9-d164b1cbafcc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60ace27c-618c-4af9-93d9-d164b1cbafcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40be6a92-ecb2-4395-ae65-144f19f7c6fa' class='xr-var-data-in' type='checkbox'><label for='data-40be6a92-ecb2-4395-ae65-144f19f7c6fa' 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 of measurement</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 style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 4.83 MiB </td>\n",
" <td> 377.74 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 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",
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"\n",
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"\n",
" <!-- Colored Rectangle -->\n",
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"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >632980</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>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44791,), meta=np.ndarray&gt;</div><input id='attrs-48f4a1d8-5940-48c8-9daa-bf7f1c07d00d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48f4a1d8-5940-48c8-9daa-bf7f1c07d00d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c4573de-700e-483b-94ea-a623d56f87be' class='xr-var-data-in' type='checkbox'><label for='data-9c4573de-700e-483b-94ea-a623d56f87be' 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 of measurement</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 style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 4.83 MiB </td>\n",
" <td> 377.74 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (632980,) </td>\n",
" <td> (48351,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 14 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
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"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
" <line x1=\"8\" y1=\"0\" x2=\"8\" y2=\"25\" />\n",
" <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"25\" />\n",
" <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
" <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"25\" />\n",
" <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"25\" />\n",
" <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
" <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"25\" />\n",
" <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"25\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\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\" >632980</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>days_elapsed</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.671e-05 ... 13.75 13.75</div><input id='attrs-7c8ddebc-cc91-4a95-94ba-632a441c324c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c8ddebc-cc91-4a95-94ba-632a441c324c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9c7b108-060f-41ae-b5c6-2bc4d0285881' class='xr-var-data-in' type='checkbox'><label for='data-d9c7b108-060f-41ae-b5c6-2bc4d0285881' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.00000000e+00, 5.67129600e-05, 6.80555556e-05, ...,\n",
" 1.37544956e+01, 1.37545069e+01, 1.37545409e+01])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0cffd8d4-0548-499f-bdfd-0d6b62b8c3ad' class='xr-section-summary-in' type='checkbox' checked><label for='section-0cffd8d4-0548-499f-bdfd-0d6b62b8c3ad' class='xr-section-summary' >Data variables: <span>(14)</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>deptht_bounds</span></div><div class='xr-var-dims'>(axis_nbounds, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 6.194 6.194 6.194</div><input id='attrs-9eb029e3-0959-43bd-88e2-f1e5843fd2a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9eb029e3-0959-43bd-88e2-f1e5843fd2a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b5e100a-79f9-4ebf-b3ae-8b24742f9f6e' class='xr-var-data-in' type='checkbox'><label for='data-0b5e100a-79f9-4ebf-b3ae-8b24742f9f6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[0. , 0. , 0. , ..., 0. , 0. ,\n",
" 0. ],\n",
" [6.1942425, 6.1942425, 6.1942425, ..., 6.1942425, 6.1942425,\n",
" 6.1942425]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_centered_bounds</span></div><div class='xr-var-dims'>(time, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01 ... 2018-01-21</div><input id='attrs-29025e1e-6422-44a2-b8b4-7c6d82aaf6e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29025e1e-6422-44a2-b8b4-7c6d82aaf6e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71baa94b-58eb-4739-918f-fe09edca0efa' class='xr-var-data-in' type='checkbox'><label for='data-71baa94b-58eb-4739-918f-fe09edca0efa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" ...,\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;]],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01 ... 2018-01-21</div><input id='attrs-81160e0d-da21-4b36-8ce7-40758591b3f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81160e0d-da21-4b36-8ce7-40758591b3f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-196e4181-f7b6-4531-a275-39ef6170742b' class='xr-var-data-in' type='checkbox'><label for='data-196e4181-f7b6-4531-a275-39ef6170742b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-06T00:00:00.000000000&#x27;],\n",
" ...,\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;],\n",
" [&#x27;2018-01-16T00:00:00.000000000&#x27;, &#x27;2018-01-21T00:00:00.000000000&#x27;]],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>votemper</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1954 0.1521 ... -1.903 -1.906</div><input id='attrs-f4333551-1d75-4ef6-b5b1-3f337c596e25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4333551-1d75-4ef6-b5b1-3f337c596e25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb2c5be0-7ad3-43cc-9746-71d7336eb743' class='xr-var-data-in' type='checkbox'><label for='data-fb2c5be0-7ad3-43cc-9746-71d7336eb743' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([ 0.19541377, 0.15214379, 0.13606602, ..., -1.9021894 ,\n",
" -1.9031733 , -1.9055033 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vosaline</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>33.49 33.52 33.54 ... 33.02 33.06</div><input id='attrs-ed15a7a1-e432-477b-9344-bfefda75fd47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed15a7a1-e432-477b-9344-bfefda75fd47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c4f7d27-6e36-40a5-8b83-352c2eafcbd4' class='xr-var-data-in' type='checkbox'><label for='data-0c4f7d27-6e36-40a5-8b83-352c2eafcbd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>long_name :</span></dt><dd>Sea Water Salinity</dd><dt><span>units :</span></dt><dd>0.001</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([33.485085, 33.523163, 33.54488 , ..., 33.000164, 33.020435,\n",
" 33.06488 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowaflup</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.072e-05 1.973e-05 ... -1.079e-05</div><input id='attrs-fee461c9-2beb-43da-99ad-142129d67b42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fee461c9-2beb-43da-99ad-142129d67b42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ec08ea1-2eea-47d0-ab1d-c04dafd819fd' class='xr-var-data-in' type='checkbox'><label for='data-1ec08ea1-2eea-47d0-ab1d-c04dafd819fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>water_flux_out_of_sea_ice_and_sea_water</dd><dt><span>long_name :</span></dt><dd>Net Upward Water Flux</dd><dt><span>units :</span></dt><dd>kg/m2/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([ 2.0716534e-05, 1.9730687e-05, 1.8308405e-05, ...,\n",
" -1.0019070e-05, -1.0157182e-05, -1.0794108e-05], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soshfldo</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>226.9 225.5 225.8 ... 86.33 87.52</div><input id='attrs-765873de-b449-44c0-8c14-7816139c2d7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-765873de-b449-44c0-8c14-7816139c2d7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28ed71d0-981a-48aa-a748-50e586b8dc45' class='xr-var-data-in' type='checkbox'><label for='data-28ed71d0-981a-48aa-a748-50e586b8dc45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>net_downward_shortwave_flux_at_sea_water_surface</dd><dt><span>long_name :</span></dt><dd>Shortwave Radiation</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([226.89275, 225.52641, 225.80482, ..., 85.35307, 86.33183,\n",
" 87.52219], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sohefldo</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>132.7 133.2 134.0 ... 14.66 14.75</div><input id='attrs-60fa004f-dd1f-401b-abaa-8efbe0cde266' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60fa004f-dd1f-401b-abaa-8efbe0cde266' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9090146-6269-4359-811d-55bb3bf600f2' class='xr-var-data-in' type='checkbox'><label for='data-b9090146-6269-4359-811d-55bb3bf600f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_downward_heat_flux_in_sea_water</dd><dt><span>long_name :</span></dt><dd>Net Downward Heat Flux</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([132.7295 , 133.22234 , 134.00003 , ..., 14.756789, 14.657626,\n",
" 14.754332], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>somxl010</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.0 13.76 13.51 ... 12.84 12.84</div><input id='attrs-eb1267e4-40ae-4484-b95d-0552c9ffab51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb1267e4-40ae-4484-b95d-0552c9ffab51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee1afcb6-2643-47c1-97a6-140c5b6ef8ab' class='xr-var-data-in' type='checkbox'><label for='data-ee1afcb6-2643-47c1-97a6-140c5b6ef8ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_sigma_theta</dd><dt><span>long_name :</span></dt><dd>Mixed Layer Depth (dsigma = 0.01 wrt 10m)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([14.004021, 13.763841, 13.511652, ..., 12.839149, 12.839149,\n",
" 12.839149], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>somixhgt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.21 14.63 14.53 ... 12.84 12.84</div><input id='attrs-328324ea-c59f-4dd9-bad2-d220666e9fc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-328324ea-c59f-4dd9-bad2-d220666e9fc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-945c234c-5334-4747-b718-83ac18f41844' class='xr-var-data-in' type='checkbox'><label for='data-945c234c-5334-4747-b718-83ac18f41844' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_vertical_tracer_diffusivity</dd><dt><span>long_name :</span></dt><dd>Turbocline depth (Kz = 5e-4)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([15.212641, 14.628489, 14.532416, ..., 12.839149, 12.839149,\n",
" 12.839149], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowindsp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.463 4.384 4.348 ... 7.187 7.1</div><input id='attrs-f09a082f-f070-4dbf-9d6f-3e793518082a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f09a082f-f070-4dbf-9d6f-3e793518082a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77845920-6137-4d33-b82f-3964d82e5bba' class='xr-var-data-in' type='checkbox'><label for='data-77845920-6137-4d33-b82f-3964d82e5bba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>long_name :</span></dt><dd>wind speed module</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([4.462639 , 4.383871 , 4.348183 , ..., 7.2213616, 7.186725 ,\n",
" 7.0996017], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sohefldp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-8e2f5e63-cec0-41e7-bffa-dffb4276a269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e2f5e63-cec0-41e7-bffa-dffb4276a269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bb65bcb-5fba-465f-a17e-8603a1b531b2' class='xr-var-data-in' type='checkbox'><label for='data-8bb65bcb-5fba-465f-a17e-8603a1b531b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>heat_flux_into_sea_water_due_to_newtonian_relaxation</dd><dt><span>long_name :</span></dt><dd>Surface Heat Flux: Damping</dd><dt><span>units :</span></dt><dd>W/m2</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sowafldp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.368e-05 2.351e-05 ... 1.09e-05</div><input id='attrs-2a6087d7-eff7-4b29-a7e8-2067fe6815e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a6087d7-eff7-4b29-a7e8-2067fe6815e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c9e2d17-efeb-4de8-aea4-6933362a62b6' class='xr-var-data-in' type='checkbox'><label for='data-5c9e2d17-efeb-4de8-aea4-6933362a62b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>water_flux_out_of_sea_water_due_to_newtonian_relaxation</dd><dt><span>long_name :</span></dt><dd>Surface Water Flux: Damping</dd><dt><span>units :</span></dt><dd>kg/m2/s</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>3600 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 3600 s)</dd></dl></div><div class='xr-var-data'><pre>array([2.3677101e-05, 2.3514443e-05, 2.2799197e-05, ..., 1.0678544e-05,\n",
" 1.0772024e-05, 1.0900458e-05], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sobowlin</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.04 20.04 20.04 ... 12.84 12.84</div><input id='attrs-f5b54bf0-5dc2-466f-90db-7a7fc78a31cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5b54bf0-5dc2-466f-90db-7a7fc78a31cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4b76e6e-3b0f-4238-8476-72afa3d517c9' class='xr-var-data-in' type='checkbox'><label for='data-d4b76e6e-3b0f-4238-8476-72afa3d517c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ocean_mixed_layer_thickness_defined_by_sigma_theta</dd><dt><span>long_name :</span></dt><dd>Mixed Layer Depth (dsigma = 0.01 wrt 10m)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>online_operation :</span></dt><dd>maximum</dd><dt><span>interval_operation :</span></dt><dd>720 s</dd><dt><span>interval_write :</span></dt><dd>5 d</dd><dt><span>cell_methods :</span></dt><dd>time: maximum (interval: 720 s)</dd></dl></div><div class='xr-var-data'><pre>array([20.04454 , 20.04454 , 20.04454 , ..., 12.839149, 12.839149,\n",
" 12.839149], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-051c4be6-64a7-4522-b6ef-779c07453d9c' class='xr-section-summary-in' type='checkbox' ><label for='section-051c4be6-64a7-4522-b6ef-779c07453d9c' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9dca9c4e-9e6d-4888-9c78-b2b24f10b331' class='xr-index-data-in' type='checkbox'/><label for='index-9dca9c4e-9e6d-4888-9c78-b2b24f10b331' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([ &#x27;2018-01-03 12:00:00&#x27;,\n",
" &#x27;2018-01-03 12:00:04.899999744&#x27;,\n",
" &#x27;2018-01-03 12:00:05.880000&#x27;,\n",
" &#x27;2018-01-03 12:00:06.860000&#x27;,\n",
" &#x27;2018-01-03 12:00:07.840000&#x27;,\n",
" &#x27;2018-01-03 12:00:08.820000&#x27;,\n",
" &#x27;2018-01-03 12:00:09.800000&#x27;,\n",
" &#x27;2018-01-03 12:00:10.779999744&#x27;,\n",
" &#x27;2018-01-03 12:00:11.759999744&#x27;,\n",
" &#x27;2018-01-03 12:00:12.739999744&#x27;,\n",
" ...\n",
" &#x27;2018-01-17 06:06:21.556099072&#x27;,\n",
" &#x27;2018-01-17 06:06:22.536099072&#x27;,\n",
" &#x27;2018-01-17 06:06:23.516098816&#x27;,\n",
" &#x27;2018-01-17 06:06:24.496098816&#x27;,\n",
" &#x27;2018-01-17 06:06:25.476098816&#x27;,\n",
" &#x27;2018-01-17 06:06:26.456098816&#x27;,\n",
" &#x27;2018-01-17 06:06:27.436098816&#x27;,\n",
" &#x27;2018-01-17 06:06:28.416099072&#x27;,\n",
" &#x27;2018-01-17 06:06:29.396099072&#x27;,\n",
" &#x27;2018-01-17 06:06:32.336098816&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=632980, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23476e9a-12c4-4bd6-986a-b70b9498f4a1' class='xr-section-summary-in' type='checkbox' checked><label for='section-23476e9a-12c4-4bd6-986a-b70b9498f4a1' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T</dd><dt><span>description :</span></dt><dd>ocean T grid variables</dd><dt><span>title :</span></dt><dd>ocean T grid variables</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>timeStamp :</span></dt><dd>2020-Jan-04 09:32:22 GMT</dd><dt><span>uuid :</span></dt><dd>c1428d50-e458-41e7-83f6-b2813d89831b</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (axis_nbounds: 2, time: 632980)\n",
"Coordinates:\n",
" nav_lat (time) float32 -65.6 -65.39 -65.29 ... -70.19 -70.36\n",
" nav_lon (time) float32 -3.25 -3.5 -3.5 ... -138.5 -138.8\n",
" time_centered datetime64[ns] 2018-01-03T12:00:00\n",
" time_counter datetime64[ns] 2018-01-03T12:00:00\n",
" x (time) int64 1136 1135 1135 1135 ... 595 595 595 594\n",
" y (time) int64 147 149 150 150 151 ... 100 99 99 98 96\n",
" * time (time) datetime64[ns] 2018-01-03T12:00:00 ... 2018-...\n",
" longitude (time) float64 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" latitude (time) float64 dask.array<chunksize=(44791,), meta=np.ndarray>\n",
" days_elapsed (time) float64 0.0 5.671e-05 6.806e-05 ... 13.75 13.75\n",
"Dimensions without coordinates: axis_nbounds\n",
"Data variables: (12/14)\n",
" deptht_bounds (axis_nbounds, time) float32 0.0 0.0 ... 6.194 6.194\n",
" time_centered_bounds (time, axis_nbounds) datetime64[ns] 2018-01-01 ... ...\n",
" time_counter_bounds (time, axis_nbounds) datetime64[ns] 2018-01-01 ... ...\n",
" votemper (time) float32 0.1954 0.1521 0.1361 ... -1.903 -1.906\n",
" vosaline (time) float32 33.49 33.52 33.54 ... 33.0 33.02 33.06\n",
" sowaflup (time) float32 2.072e-05 1.973e-05 ... -1.079e-05\n",
" ... ...\n",
" somxl010 (time) float32 14.0 13.76 13.51 ... 12.84 12.84 12.84\n",
" somixhgt (time) float32 15.21 14.63 14.53 ... 12.84 12.84 12.84\n",
" sowindsp (time) float32 4.463 4.384 4.348 ... 7.221 7.187 7.1\n",
" sohefldp (time) float32 nan nan nan nan nan ... nan nan nan nan\n",
" sowafldp (time) float32 2.368e-05 2.351e-05 ... 1.09e-05\n",
" sobowlin (time) float32 20.04 20.04 20.04 ... 12.84 12.84 12.84\n",
"Attributes:\n",
" name: ./OUTPUT/INALT20.L46-KFS104_5d_20180101_20181231_grid_T\n",
" description: ocean T grid variables\n",
" title: ocean T grid variables\n",
" Conventions: CF-1.6\n",
" timeStamp: 2020-Jan-04 09:32:22 GMT\n",
" uuid: c1428d50-e458-41e7-83f6-b2813d89831b"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_nemo_along_track = ds_nemo_along_track.where(~ds_nemo_along_track.votemper.isnull())\n",
"ds_nemo_along_track"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "3f0e55e7-c778-491a-9bec-afc2aef26b96",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='nav_lon', ylabel='nav_lat'>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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