MML 2 - Compare Data with own data.ipynb

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    "# MML 2 - Merging Data and Computing Anomalies\n",
    "\n",
    "In this notebook, we combine two datasets. For this, we need to bring them to the same grid, so we do that as well. Finally, we compute monthly anomalies.\n",
    "\n",
    "We once more start by taking the snippet from xcube viewer to open the SST dataset:"
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       "Dimensions:       (time: 3085, latitude: 200, longitude: 321)\n",
       "Coordinates:\n",
       "  * latitude      (latitude) float32 38.72 38.78 38.83 ... 48.67 48.72 48.78\n",
       "  * longitude     (longitude) float32 26.38 26.42 26.48 ... 42.28 42.33 42.38\n",
       "  * time          (time) datetime64[ns] 2016-01-01 2016-01-02 ... 2024-06-11\n",
       "Data variables:\n",
       "    analysed_sst  (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;\n",
       "Attributes: (12/52)\n",
       "    Conventions:                CF-1.4\n",
       "    DSD_entry_id:               -GOS-L4HRfnd-BLK\n",
       "    Metadata_Conventions:       Unidata Dataset Discovery v1.0\n",
       "    Scaling_Equation:           (scale_factor*data) + add_offset\n",
       "    acknowledgment:             Please acknowledge the use of these data with...\n",
       "    cdm_data_type:              grid\n",
       "    ...                         ...\n",
       "    summary:                    [1982-2018] Daily gap-free maps (L4) at 0.05d...\n",
       "    time_coverage_end:          20240508T190000Z\n",
       "    time_coverage_start:        20160101T070000Z\n",
       "    title:                      CMEMS SST Black Sea\n",
       "    uuid:                        \n",
       "    westernmost_longitude:      26.375</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-124508a5-77d2-463d-a17f-c82f1332e0da' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-124508a5-77d2-463d-a17f-c82f1332e0da' 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>: 3085</li><li><span class='xr-has-index'>latitude</span>: 200</li><li><span class='xr-has-index'>longitude</span>: 321</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-889b76f9-31ca-4506-9045-21991abe05db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-889b76f9-31ca-4506-9045-21991abe05db' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>38.72 38.78 38.83 ... 48.72 48.78</div><input id='attrs-0fe1e37c-0f3b-4b6e-bbc3-0cb2ac7448cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fe1e37c-0f3b-4b6e-bbc3-0cb2ac7448cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-597dee76-1687-4591-824f-a5fec48e879d' class='xr-var-data-in' type='checkbox'><label for='data-597dee76-1687-4591-824f-a5fec48e879d' 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>Y</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>48.775001525878906</dd><dt><span>valid_min :</span></dt><dd>38.724998474121094</dd></dl></div><div class='xr-var-data'><pre>array([38.725   , 38.7755  , 38.826004, 38.876507, 38.92701 , 38.977512,\n",
       "       39.028015, 39.078518, 39.129017, 39.17952 , 39.230022, 39.280525,\n",
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       "       39.634045, 39.684547, 39.73505 , 39.785553, 39.836056, 39.88656 ,\n",
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       "       41.14912 , 41.199623, 41.250126, 41.30063 , 41.35113 , 41.401634,\n",
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       "       42.664196, 42.7147  , 42.7652  , 42.815704, 42.866207, 42.91671 ,\n",
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       "       46.90641 , 46.95691 , 47.007412, 47.057915, 47.108418, 47.15892 ,\n",
       "       47.209423, 47.259926, 47.31043 , 47.36093 , 47.411434, 47.461937,\n",
       "       47.512436, 47.562943, 47.61344 , 47.663948, 47.714447, 47.76495 ,\n",
       "       47.815453, 47.865955, 47.91646 , 47.96696 , 48.017464, 48.067966,\n",
       "       48.11847 , 48.168972, 48.219475, 48.269978, 48.32048 , 48.37098 ,\n",
       "       48.421486, 48.471985, 48.522488, 48.57299 , 48.623493, 48.673996,\n",
       "       48.7245  , 48.775   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>26.38 26.42 26.48 ... 42.33 42.38</div><input id='attrs-6ff4a1d9-d0a8-4633-9948-7fc2d171e72a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ff4a1d9-d0a8-4633-9948-7fc2d171e72a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fe7ff04-c2f6-403a-8da6-3d498d1e2be6' class='xr-var-data-in' type='checkbox'><label for='data-5fe7ff04-c2f6-403a-8da6-3d498d1e2be6' 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>X</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>42.375</dd><dt><span>valid_min :</span></dt><dd>26.375</dd></dl></div><div class='xr-var-data'><pre>array([26.375, 26.425, 26.475, ..., 42.275, 42.325, 42.375], dtype=float32)</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'>2016-01-01 ... 2024-06-11</div><input id='attrs-f3620a00-6c53-4954-9110-5e5a6a5ef9bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3620a00-6c53-4954-9110-5e5a6a5ef9bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fc578b4-01b5-4817-9851-baa3d3b00673' class='xr-var-data-in' type='checkbox'><label for='data-1fc578b4-01b5-4817-9851-baa3d3b00673' 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>valid_max :</span></dt><dd>2024-05-08T00:00:00.000000000</dd><dt><span>valid_min :</span></dt><dd>2016-01-01T00:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-03T00:00:00.000000000&#x27;, ..., &#x27;2024-06-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-06-10T00:00:00.000000000&#x27;, &#x27;2024-06-11T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9515fde-81a5-44c3-a8ee-ab2f773f6719' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9515fde-81a5-44c3-a8ee-ab2f773f6719' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>analysed_sst</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;</div><input id='attrs-41832851-8c12-4336-b9a8-7eb596ca2729' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41832851-8c12-4336-b9a8-7eb596ca2729' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a47e04a9-9c8a-4940-956c-4e91b2e56020' class='xr-var-data-in' type='checkbox'><label for='data-a47e04a9-9c8a-4940-956c-4e91b2e56020' 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>[1982-2018] Optimal interpolation (OI) SST measurements from ESA CCI SST v2.0, C3S v.2.0 and PFV53</dd><dt><span>long name :</span></dt><dd>Analysed Sea Surface Temperature</dd><dt><span>long_name :</span></dt><dd>Analysed Sea Surface Temperature</dd><dt><span>source :</span></dt><dd>[1982-2016] ESA CCI SST v.2.0 L3C product (SST at 0.2m); [2017-2018] C3S v.2.0 L3C product (SST at 0.2m); [1982-2014]Pathfinder-PFV5.3 L3C product (SST skin)</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>time_coverage_end :</span></dt><dd>20240508T190000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20160101T070000Z</dd><dt><span>type :</span></dt><dd>foundation</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>4500</dd><dt><span>valid_min :</span></dt><dd>-300</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> 755.53 MiB </td>\n",
       "                        <td> 250.78 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (3085, 200, 321) </td>\n",
       "                        <td> (1, 200, 321) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 3085 chunks in 2 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",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0491d58-f2f0-4532-bcb8-325e2fe53094' class='xr-section-summary-in' type='checkbox'  ><label for='section-f0491d58-f2f0-4532-bcb8-325e2fe53094' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-32a97f81-7040-499a-a3e0-8860adb9830c' class='xr-index-data-in' type='checkbox'/><label for='index-32a97f81-7040-499a-a3e0-8860adb9830c' 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(Float64Index([38.724998474121094,   38.7755012512207,  38.82600402832031,\n",
       "               38.87650680541992,  38.92700958251953,  38.97751235961914,\n",
       "               39.02801513671875,  39.07851791381836,   39.1290168762207,\n",
       "               39.17951965332031,\n",
       "              ...\n",
       "               48.32048034667969,  48.37097930908203, 48.421485900878906,\n",
       "               48.47198486328125,  48.52248764038086,  48.57299041748047,\n",
       "               48.62349319458008,  48.67399597167969,   48.7244987487793,\n",
       "              48.775001525878906],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=200))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-03976919-1b8e-41c7-8cdf-5a1c5181d7ce' class='xr-index-data-in' type='checkbox'/><label for='index-03976919-1b8e-41c7-8cdf-5a1c5181d7ce' 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(Float64Index([            26.375, 26.424999237060547, 26.475000381469727,\n",
       "              26.524999618530273, 26.575000762939453,             26.625,\n",
       "              26.674999237060547, 26.725000381469727, 26.774999618530273,\n",
       "              26.825000762939453,\n",
       "              ...\n",
       "               41.92499923706055, 41.974998474121094, 42.025001525878906,\n",
       "               42.07500076293945,             42.125,  42.17499923706055,\n",
       "              42.224998474121094, 42.275001525878906,  42.32500076293945,\n",
       "                          42.375],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=321))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-457c6aa8-77ca-4aa5-8420-8ea23c628b4d' class='xr-index-data-in' type='checkbox'/><label for='index-457c6aa8-77ca-4aa5-8420-8ea23c628b4d' 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;2016-01-01&#x27;, &#x27;2016-01-02&#x27;, &#x27;2016-01-03&#x27;, &#x27;2016-01-04&#x27;,\n",
       "               &#x27;2016-01-05&#x27;, &#x27;2016-01-06&#x27;, &#x27;2016-01-07&#x27;, &#x27;2016-01-08&#x27;,\n",
       "               &#x27;2016-01-09&#x27;, &#x27;2016-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-06-02&#x27;, &#x27;2024-06-03&#x27;, &#x27;2024-06-04&#x27;, &#x27;2024-06-05&#x27;,\n",
       "               &#x27;2024-06-06&#x27;, &#x27;2024-06-07&#x27;, &#x27;2024-06-08&#x27;, &#x27;2024-06-09&#x27;,\n",
       "               &#x27;2024-06-10&#x27;, &#x27;2024-06-11&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=3085, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3d847cd6-c3ac-4207-b564-60a40299dcde' class='xr-section-summary-in' type='checkbox'  ><label for='section-3d847cd6-c3ac-4207-b564-60a40299dcde' class='xr-section-summary' >Attributes: <span>(52)</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-1.4</dd><dt><span>DSD_entry_id :</span></dt><dd>-GOS-L4HRfnd-BLK</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Scaling_Equation :</span></dt><dd>(scale_factor*data) + add_offset</dd><dt><span>acknowledgment :</span></dt><dd>Please acknowledge the use of these data with the following statement: Generated/provided by Copernicus Marine Service and CNR - ISMAR ROME. We would also appreciate being informed of any publications.</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>comment :</span></dt><dd>WARNING: some applications are unable to properly handle byte values. If Values &gt;127 are encounterd, please subtract 256</dd><dt><span>copernicusmarine_version :</span></dt><dd>1.0.0</dd><dt><span>creator_email :</span></dt><dd>gsdk@isac.cnr.it</dd><dt><span>creator_name :</span></dt><dd>ISMAR - Institute of Marine Sciences (CNR - ISMAR - GOS - Rome)</dd><dt><span>creator_url :</span></dt><dd>http://gosweb.artov.isac.cnr.it/</dd><dt><span>date_created :</span></dt><dd>20200610T223736Z</dd><dt><span>easternmost_longitude :</span></dt><dd>42.375</dd><dt><span>end_time :</span></dt><dd>20231231T190000Z</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>gds_version_id :</span></dt><dd>v2.0.5</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>GOS-CMEMS processor V4: new version</dd><dt><span>id :</span></dt><dd> </dd><dt><span>institution :</span></dt><dd>GOS</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>free registration at Copernicus Marine Service (http://marine.copernicus.eu/web/56-user-registration-form.php)</dd><dt><span>metadata_link :</span></dt><dd>Link to collection metadata record at archive</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>netcdf_version_id :</span></dt><dd>4.1.1, build date: JUN 18 2010&quot;</dd><dt><span>northernmost_latitude :</span></dt><dd>48.775001525878906</dd><dt><span>platform :</span></dt><dd>NOAA AVHRR, Metop-A AVHRR, (A)ATSR, Sentinel-3A SLSTR series of sensors</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_version :</span></dt><dd>3.0</dd><dt><span>project :</span></dt><dd>Copernicus Marine Environment Monitoring Service (CMEMS)</dd><dt><span>publisher_email :</span></dt><dd>servicedesk.cmems@mercator-ocean.eu, gsdk@isac.cnr.it</dd><dt><span>publisher_name :</span></dt><dd>CNR ISMAR GOS - CMEMS SST-TAC</dd><dt><span>publisher_url :</span></dt><dd>http://marine.copernicus.eu/</dd><dt><span>references :</span></dt><dd>A. Pisano, B. Buongiorno Nardelli, C. Tronconi, R. Santoleri: The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982-2012). /Remote Sensing of Environment./ 176 (2016) 107-116, doi:10.1016/j.rse.2016.01.019; http://pathfinder.nodc.noaa.gov and Casey, K.S., T.B. Brandon, P. Cornillon, and R. Evans: The Past, Present and Future of the AVHRR Pathfinder SST Program, in Oceanography from Space: Revisited, eds. V. Barale, J.F.R. Gower, and L. Alberotanza, Springer, 2010. DOI: 10.1007/978-90-481-8681-5_16.</dd><dt><span>sensor :</span></dt><dd>[1981-2016] NOAA AVHRR series of sensors (NOAA-07/09/11/12/14/15/16/17/18/19), ATSR1, ATSR2, (A)ATSR, MetopA AVHRR; [2017-2018]  NOAA AVHRR-19, MetopA AVHRR, SLSTR-3A</dd><dt><span>software_version :</span></dt><dd>Copernicus Marine Service HR L4 Processor V.2</dd><dt><span>source :</span></dt><dd>[1982-2016] ESA CCI SST v.2.0 L3C product (SST at 0.2m); [2017-2018] C3S v.2.0 L3C product (SST at 0.2m); [1982-2014]  Pathfinder-PFV5.3 L3C product (SST skin)</dd><dt><span>southernmost_latitude :</span></dt><dd>38.724998474121094</dd><dt><span>spatial_resolution :</span></dt><dd>0.05 degree</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>start_time :</span></dt><dd>20160101T070000Z</dd><dt><span>stop_time :</span></dt><dd>19810825T070000Z</dd><dt><span>summary :</span></dt><dd>[1982-2018] Daily gap-free maps (L4) at 0.05deg. x 0.05deg. horizontal resolution over the Black Sea. The maps are obtained using nighttime data extracted from ESA CCI SST v.2.0, C3S v.2.0 and PFV53 data</dd><dt><span>time_coverage_end :</span></dt><dd>20240508T190000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20160101T070000Z</dd><dt><span>title :</span></dt><dd>CMEMS SST Black Sea</dd><dt><span>uuid :</span></dt><dd> </dd><dt><span>westernmost_longitude :</span></dt><dd>26.375</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (time: 3085, latitude: 200, longitude: 321)\n",
       "Coordinates:\n",
       "  * latitude      (latitude) float32 38.72 38.78 38.83 ... 48.67 48.72 48.78\n",
       "  * longitude     (longitude) float32 26.38 26.42 26.48 ... 42.28 42.33 42.38\n",
       "  * time          (time) datetime64[ns] 2016-01-01 2016-01-02 ... 2024-06-11\n",
       "Data variables:\n",
       "    analysed_sst  (time, latitude, longitude) float32 dask.array<chunksize=(1, 200, 321), meta=np.ndarray>\n",
       "Attributes: (12/52)\n",
       "    Conventions:                CF-1.4\n",
       "    DSD_entry_id:               -GOS-L4HRfnd-BLK\n",
       "    Metadata_Conventions:       Unidata Dataset Discovery v1.0\n",
       "    Scaling_Equation:           (scale_factor*data) + add_offset\n",
       "    acknowledgment:             Please acknowledge the use of these data with...\n",
       "    cdm_data_type:              grid\n",
       "    ...                         ...\n",
       "    summary:                    [1982-2018] Daily gap-free maps (L4) at 0.05d...\n",
       "    time_coverage_end:          20240508T190000Z\n",
       "    time_coverage_start:        20160101T070000Z\n",
       "    title:                      CMEMS SST Black Sea\n",
       "    uuid:                        \n",
       "    westernmost_longitude:      26.375"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xcube.core.store import new_data_store\n",
    "\n",
    "store = new_data_store(\n",
    "    \"s3\",\n",
    "    root=\"datasets\",  # can also use \"pyramids\" here\n",
    "    storage_options={\n",
    "        \"anon\": True,\n",
    "        \"client_kwargs\": {\n",
    "            \"endpoint_url\": \"https://doors.api.brockmann-consult.de/api/s3\"\n",
    "        }\n",
    "    }\n",
    ")\n",
    "# store.list_data_ids()\n",
    "sst_dataset = store.open_data(data_id=\"cmems-sst-bs.zarr\")\n",
    "sst_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8276e6d2-480f-4340-af69-5f59da05e08d",
   "metadata": {},
   "source": [
    "Now, let's open another dataset. We use `sst_monthly.zarr`, which you can find in the `data` folder. We simply open it with xarray, which we import first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e7d4eada-5042-412d-909e-3246a2fc2f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b252febc-3f64-41ac-9313-db2fe11aa960",
   "metadata": {},
   "outputs": [
    {
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       "Dimensions:    (latitude: 215, longitude: 395, time: 102)\n",
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       "  * latitude   (latitude) float64 40.86 40.89 40.92 40.94 ... 46.75 46.78 46.8\n",
       "  * longitude  (longitude) float64 27.37 27.41 27.44 27.48 ... 41.89 41.93 41.96\n",
       "  * time       (time) datetime64[ns] 2015-12-31 2016-01-31 ... 2024-05-31\n",
       "Data variables:\n",
       "    other_sst  (time, latitude, longitude) float64 dask.array&lt;chunksize=(1, 215, 395), meta=np.ndarray&gt;</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-743d7ee4-0b25-4b95-87e6-edaa0fe6b5dd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-743d7ee4-0b25-4b95-87e6-edaa0fe6b5dd' 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'>latitude</span>: 215</li><li><span class='xr-has-index'>longitude</span>: 395</li><li><span class='xr-has-index'>time</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-635ccbee-8d5d-49c9-ad29-616768102576' class='xr-section-summary-in' type='checkbox'  checked><label for='section-635ccbee-8d5d-49c9-ad29-616768102576' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>40.86 40.89 40.92 ... 46.78 46.8</div><input id='attrs-9f1d751a-b46b-491b-842b-7fa9f7739865' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f1d751a-b46b-491b-842b-7fa9f7739865' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbd13c81-2928-4445-b434-0b9734ea78e4' class='xr-var-data-in' type='checkbox'><label for='data-bbd13c81-2928-4445-b434-0b9734ea78e4' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([40.860001, 40.887778, 40.915556, ..., 46.748884, 46.776662, 46.80444 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.37 27.41 27.44 ... 41.93 41.96</div><input id='attrs-c5bb5deb-de3b-4da2-9c02-c16197378a82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5bb5deb-de3b-4da2-9c02-c16197378a82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57a25e62-730d-4553-b048-5213c7b74d4b' class='xr-var-data-in' type='checkbox'><label for='data-57a25e62-730d-4553-b048-5213c7b74d4b' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>longitude coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([27.370001, 27.407038, 27.444075, ..., 41.888515, 41.925552, 41.962589])</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'>2015-12-31 ... 2024-05-31</div><input id='attrs-e1b0f5d0-586f-4f01-809d-b4f588a13872' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1b0f5d0-586f-4f01-809d-b4f588a13872' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cedbd7e8-df29-4d6b-9ba9-5afb01e4f2d7' class='xr-var-data-in' type='checkbox'><label for='data-cedbd7e8-df29-4d6b-9ba9-5afb01e4f2d7' 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>valid_max :</span></dt><dd>2024-05-08T00:00:00.000000000</dd><dt><span>valid_min :</span></dt><dd>2016-01-01T00:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-12-31T00:00:00.000000000&#x27;, &#x27;2016-01-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2023-12-31T00:00:00.000000000&#x27;, &#x27;2024-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-02-29T00:00:00.000000000&#x27;, &#x27;2024-03-31T00:00:00.000000000&#x27;,\n",
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       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-773a45ab-f172-469e-af03-c8d625d85810' class='xr-section-summary-in' type='checkbox'  checked><label for='section-773a45ab-f172-469e-af03-c8d625d85810' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>other_sst</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 215, 395), meta=np.ndarray&gt;</div><input id='attrs-d16badde-1a53-4bd7-827d-7579b10b64da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d16badde-1a53-4bd7-827d-7579b10b64da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d41c8a4-6ec1-4a2f-8f65-2459f2870a97' class='xr-var-data-in' type='checkbox'><label for='data-7d41c8a4-6ec1-4a2f-8f65-2459f2870a97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 66.09 MiB </td>\n",
       "                        <td> 663.48 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (102, 215, 395) </td>\n",
       "                        <td> (1, 215, 395) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 102 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7dd0ec67-fba5-43f6-b640-004d88551c7b' class='xr-section-summary-in' type='checkbox'  ><label for='section-7dd0ec67-fba5-43f6-b640-004d88551c7b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-be7b7676-026c-4fdf-9d15-52d003b667f2' class='xr-index-data-in' type='checkbox'/><label for='index-be7b7676-026c-4fdf-9d15-52d003b667f2' 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(Float64Index([ 40.86000061035156,  40.88777836238113, 40.915556114410684,\n",
       "               40.94333386644025, 40.971111618469806,  40.99888937049937,\n",
       "              41.026667122528934,  41.05444487455849, 41.082222626588056,\n",
       "               41.11000037861761,\n",
       "              ...\n",
       "              46.554439776411684,  46.58221752844124, 46.609995280470805,\n",
       "               46.63777303250036,  46.66555078452993,  46.69332853655949,\n",
       "               46.72110628858905,  46.74888404061861,  46.77666179264817,\n",
       "              46.804439544677734],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=215))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-95e74ef8-eee2-4fac-9bbc-330f0baee574' class='xr-index-data-in' type='checkbox'/><label for='index-95e74ef8-eee2-4fac-9bbc-330f0baee574' 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(Float64Index([  27.3700008392334, 27.407037865691983,  27.44407489215057,\n",
       "              27.481111918609155,  27.51814894506774, 27.555185971526324,\n",
       "              27.592222997984912, 27.629260024443496,  27.66629705090208,\n",
       "              27.703334077360665,\n",
       "              ...\n",
       "               41.62925602578875,  41.66629305224733,  41.70333007870592,\n",
       "                41.7403671051645, 41.777404131623086,  41.81444115808168,\n",
       "               41.85147818454026,  41.88851521099885,  41.92555223745743,\n",
       "              41.962589263916016],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=395))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c4427718-edb2-4e9d-bc56-4835448dcd19' class='xr-index-data-in' type='checkbox'/><label for='index-c4427718-edb2-4e9d-bc56-4835448dcd19' 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;2015-12-31&#x27;, &#x27;2016-01-31&#x27;, &#x27;2016-02-29&#x27;, &#x27;2016-03-31&#x27;,\n",
       "               &#x27;2016-04-30&#x27;, &#x27;2016-05-31&#x27;, &#x27;2016-06-30&#x27;, &#x27;2016-07-31&#x27;,\n",
       "               &#x27;2016-08-31&#x27;, &#x27;2016-09-30&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-08-31&#x27;, &#x27;2023-09-30&#x27;, &#x27;2023-10-31&#x27;, &#x27;2023-11-30&#x27;,\n",
       "               &#x27;2023-12-31&#x27;, &#x27;2024-01-31&#x27;, &#x27;2024-02-29&#x27;, &#x27;2024-03-31&#x27;,\n",
       "               &#x27;2024-04-30&#x27;, &#x27;2024-05-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=102, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c4698c9-b947-4cf9-8b75-0a7743d3b1b3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c4698c9-b947-4cf9-8b75-0a7743d3b1b3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (latitude: 215, longitude: 395, time: 102)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 40.86 40.89 40.92 40.94 ... 46.75 46.78 46.8\n",
       "  * longitude  (longitude) float64 27.37 27.41 27.44 27.48 ... 41.89 41.93 41.96\n",
       "  * time       (time) datetime64[ns] 2015-12-31 2016-01-31 ... 2024-05-31\n",
       "Data variables:\n",
       "    other_sst  (time, latitude, longitude) float64 dask.array<chunksize=(1, 215, 395), meta=np.ndarray>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_sst_data = xr.open_zarr(\"data/sst_monthly.zarr\")\n",
    "other_sst_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a0cdc9-fdf2-468b-9d95-0c94b47f7b4d",
   "metadata": {},
   "source": [
    "On inspection, you can see the dimensions are different. The datasets come in different spatial and temporal grids. The DOORS CMEMS dataset we importe first is in a daily resolution, while the other one in a monthly. If we want to combine them, it is good to bring them to the same grids. Let's take care of the time first by resampling the DOORS CMEMS dataset to a monthly resolution. We use the `resample_in_time` function from xcube for this. `frequency` denotes the desired temporal period, `method` the way the values are aggregated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5df3c5c9-f9d0-4d22-b16e-9c5873fac887",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xcube.core.resampling import resample_in_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a40f1e90-8f01-49e3-8bec-98e6f6ba0662",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (time: 102, latitude: 200, longitude: 321)\n",
       "Coordinates:\n",
       "  * latitude           (latitude) float32 38.72 38.78 38.83 ... 48.72 48.78\n",
       "  * longitude          (longitude) float32 26.38 26.42 26.48 ... 42.33 42.38\n",
       "  * time               (time) datetime64[ns] 2015-12-31 ... 2024-05-31\n",
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       "Attributes: (12/52)\n",
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       "    DSD_entry_id:               -GOS-L4HRfnd-BLK\n",
       "    Metadata_Conventions:       Unidata Dataset Discovery v1.0\n",
       "    Scaling_Equation:           (scale_factor*data) + add_offset\n",
       "    acknowledgment:             Please acknowledge the use of these data with...\n",
       "    cdm_data_type:              grid\n",
       "    ...                         ...\n",
       "    summary:                    [1982-2018] Daily gap-free maps (L4) at 0.05d...\n",
       "    time_coverage_end:          &lt;xarray.DataArray &#x27;time&#x27; ()&gt;\\narray(&#x27;2024-06-...\n",
       "    time_coverage_start:        &lt;xarray.DataArray &#x27;time&#x27; ()&gt;\\narray(&#x27;2016-01-...\n",
       "    title:                      CMEMS SST Black Sea\n",
       "    uuid:                        \n",
       "    westernmost_longitude:      26.375</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-6a52b3bb-1843-4274-be52-ed01786c3df6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6a52b3bb-1843-4274-be52-ed01786c3df6' 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>: 102</li><li><span class='xr-has-index'>latitude</span>: 200</li><li><span class='xr-has-index'>longitude</span>: 321</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fdf709a0-0f8d-4b4e-8ed9-f75bbdfbf44e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fdf709a0-0f8d-4b4e-8ed9-f75bbdfbf44e' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>38.72 38.78 38.83 ... 48.72 48.78</div><input id='attrs-846c1f3e-831c-4b42-abed-adcdb7d3e954' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-846c1f3e-831c-4b42-abed-adcdb7d3e954' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d9cc52e-a572-4fe9-b733-15b800b50481' class='xr-var-data-in' type='checkbox'><label for='data-0d9cc52e-a572-4fe9-b733-15b800b50481' 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>Y</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>48.775001525878906</dd><dt><span>valid_min :</span></dt><dd>38.724998474121094</dd></dl></div><div class='xr-var-data'><pre>array([38.725   , 38.7755  , 38.826004, 38.876507, 38.92701 , 38.977512,\n",
       "       39.028015, 39.078518, 39.129017, 39.17952 , 39.230022, 39.280525,\n",
       "       39.331028, 39.38153 , 39.432034, 39.482536, 39.53304 , 39.58354 ,\n",
       "       39.634045, 39.684547, 39.73505 , 39.785553, 39.836056, 39.88656 ,\n",
       "       39.937057, 39.98756 , 40.038063, 40.088566, 40.13907 , 40.18957 ,\n",
       "       40.240074, 40.290577, 40.34108 , 40.391582, 40.442085, 40.492588,\n",
       "       40.54309 , 40.593594, 40.644096, 40.694595, 40.7451  , 40.7956  ,\n",
       "       40.846104, 40.896606, 40.94711 , 40.997612, 41.048115, 41.098618,\n",
       "       41.14912 , 41.199623, 41.250126, 41.30063 , 41.35113 , 41.401634,\n",
       "       41.452133, 41.502636, 41.55314 , 41.60364 , 41.654144, 41.704647,\n",
       "       41.75515 , 41.805653, 41.856155, 41.90666 , 41.95716 , 42.007664,\n",
       "       42.058167, 42.10867 , 42.159172, 42.20967 , 42.260174, 42.310677,\n",
       "       42.36118 , 42.411682, 42.462185, 42.512688, 42.56319 , 42.613693,\n",
       "       42.664196, 42.7147  , 42.7652  , 42.815704, 42.866207, 42.91671 ,\n",
       "       42.96721 , 43.017715, 43.068214, 43.118717, 43.16922 , 43.219723,\n",
       "       43.270226, 43.32073 , 43.37123 , 43.421734, 43.472237, 43.52274 ,\n",
       "       43.573242, 43.623745, 43.674248, 43.724747, 43.775253, 43.825752,\n",
       "       43.876255, 43.926758, 43.97726 , 44.027763, 44.078266, 44.12877 ,\n",
       "       44.17927 , 44.229774, 44.280277, 44.33078 , 44.381283, 44.431786,\n",
       "       44.482285, 44.53279 , 44.58329 , 44.633793, 44.684296, 44.7348  ,\n",
       "       44.7853  , 44.835804, 44.886307, 44.93681 , 44.987312, 45.037815,\n",
       "       45.088318, 45.13882 , 45.189323, 45.239822, 45.29033 , 45.340828,\n",
       "       45.39133 , 45.441833, 45.492336, 45.54284 , 45.59334 , 45.643845,\n",
       "       45.694347, 45.74485 , 45.795353, 45.845856, 45.89636 , 45.94686 ,\n",
       "       45.997364, 46.047867, 46.098366, 46.14887 , 46.19937 , 46.249874,\n",
       "       46.300377, 46.35088 , 46.401382, 46.451885, 46.502388, 46.55289 ,\n",
       "       46.603394, 46.653896, 46.7044  , 46.754898, 46.805405, 46.855904,\n",
       "       46.90641 , 46.95691 , 47.007412, 47.057915, 47.108418, 47.15892 ,\n",
       "       47.209423, 47.259926, 47.31043 , 47.36093 , 47.411434, 47.461937,\n",
       "       47.512436, 47.562943, 47.61344 , 47.663948, 47.714447, 47.76495 ,\n",
       "       47.815453, 47.865955, 47.91646 , 47.96696 , 48.017464, 48.067966,\n",
       "       48.11847 , 48.168972, 48.219475, 48.269978, 48.32048 , 48.37098 ,\n",
       "       48.421486, 48.471985, 48.522488, 48.57299 , 48.623493, 48.673996,\n",
       "       48.7245  , 48.775   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>26.38 26.42 26.48 ... 42.33 42.38</div><input id='attrs-176014d4-a49d-492c-af93-046f54248441' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-176014d4-a49d-492c-af93-046f54248441' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88802493-be32-43c9-b52a-10ec995cf341' class='xr-var-data-in' type='checkbox'><label for='data-88802493-be32-43c9-b52a-10ec995cf341' 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>X</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>42.375</dd><dt><span>valid_min :</span></dt><dd>26.375</dd></dl></div><div class='xr-var-data'><pre>array([26.375, 26.425, 26.475, ..., 42.275, 42.325, 42.375], dtype=float32)</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'>2015-12-31 ... 2024-05-31</div><input id='attrs-e574d57e-fd57-44b3-9f78-447fe5d53585' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e574d57e-fd57-44b3-9f78-447fe5d53585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ae9839e-f17f-464c-918f-05ff38940596' class='xr-var-data-in' type='checkbox'><label for='data-3ae9839e-f17f-464c-918f-05ff38940596' 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>valid_max :</span></dt><dd>2024-05-08T00:00:00.000000000</dd><dt><span>valid_min :</span></dt><dd>2016-01-01T00:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-12-31T00:00:00.000000000&#x27;, &#x27;2016-01-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2016-10-31T00:00:00.000000000&#x27;, &#x27;2016-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-31T00:00:00.000000000&#x27;, &#x27;2017-01-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2017-06-30T00:00:00.000000000&#x27;, &#x27;2017-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-31T00:00:00.000000000&#x27;, &#x27;2017-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-31T00:00:00.000000000&#x27;, &#x27;2017-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-12-31T00:00:00.000000000&#x27;, &#x27;2018-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-02-28T00:00:00.000000000&#x27;, &#x27;2018-03-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2018-06-30T00:00:00.000000000&#x27;, &#x27;2018-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-08-31T00:00:00.000000000&#x27;, &#x27;2018-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-10-31T00:00:00.000000000&#x27;, &#x27;2018-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-12-31T00:00:00.000000000&#x27;, &#x27;2019-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-02-28T00:00:00.000000000&#x27;, &#x27;2019-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-04-30T00:00:00.000000000&#x27;, &#x27;2019-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-06-30T00:00:00.000000000&#x27;, &#x27;2019-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-08-31T00:00:00.000000000&#x27;, &#x27;2019-09-30T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2020-02-29T00:00:00.000000000&#x27;, &#x27;2020-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-04-30T00:00:00.000000000&#x27;, &#x27;2020-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-06-30T00:00:00.000000000&#x27;, &#x27;2020-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-08-31T00:00:00.000000000&#x27;, &#x27;2020-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-10-31T00:00:00.000000000&#x27;, &#x27;2020-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T00:00:00.000000000&#x27;, &#x27;2021-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-02-28T00:00:00.000000000&#x27;, &#x27;2021-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-04-30T00:00:00.000000000&#x27;, &#x27;2021-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-06-30T00:00:00.000000000&#x27;, &#x27;2021-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-31T00:00:00.000000000&#x27;, &#x27;2021-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-10-31T00:00:00.000000000&#x27;, &#x27;2021-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-31T00:00:00.000000000&#x27;, &#x27;2022-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-02-28T00:00:00.000000000&#x27;, &#x27;2022-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-04-30T00:00:00.000000000&#x27;, &#x27;2022-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-30T00:00:00.000000000&#x27;, &#x27;2022-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-31T00:00:00.000000000&#x27;, &#x27;2022-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-10-31T00:00:00.000000000&#x27;, &#x27;2022-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-12-31T00:00:00.000000000&#x27;, &#x27;2023-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-02-28T00:00:00.000000000&#x27;, &#x27;2023-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-04-30T00:00:00.000000000&#x27;, &#x27;2023-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-06-30T00:00:00.000000000&#x27;, &#x27;2023-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-08-31T00:00:00.000000000&#x27;, &#x27;2023-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-10-31T00:00:00.000000000&#x27;, &#x27;2023-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-12-31T00:00:00.000000000&#x27;, &#x27;2024-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-02-29T00:00:00.000000000&#x27;, &#x27;2024-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-04-30T00:00:00.000000000&#x27;, &#x27;2024-05-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1bbeae1-079f-47ac-866b-3a35437acc0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1bbeae1-079f-47ac-866b-3a35437acc0c' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>analysed_sst_mean</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;</div><input id='attrs-87c3bcae-917b-439b-b3b0-4ed5a919c286' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87c3bcae-917b-439b-b3b0-4ed5a919c286' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-993a2236-b34d-4d3b-9c8e-f10af466986d' class='xr-var-data-in' type='checkbox'><label for='data-993a2236-b34d-4d3b-9c8e-f10af466986d' 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>[1982-2018] Optimal interpolation (OI) SST measurements from ESA CCI SST v2.0, C3S v.2.0 and PFV53</dd><dt><span>long name :</span></dt><dd>Analysed Sea Surface Temperature</dd><dt><span>long_name :</span></dt><dd>Analysed Sea Surface Temperature</dd><dt><span>source :</span></dt><dd>[1982-2016] ESA CCI SST v.2.0 L3C product (SST at 0.2m); [2017-2018] C3S v.2.0 L3C product (SST at 0.2m); [1982-2014]Pathfinder-PFV5.3 L3C product (SST skin)</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>time_coverage_end :</span></dt><dd>20240508T190000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20160101T070000Z</dd><dt><span>type :</span></dt><dd>foundation</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>4500</dd><dt><span>valid_min :</span></dt><dd>-300</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> 24.98 MiB </td>\n",
       "                        <td> 250.78 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (102, 200, 321) </td>\n",
       "                        <td> (1, 200, 321) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 102 chunks in 614 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6bafa2b-6c1b-48cb-8eda-f77d05a65824' class='xr-section-summary-in' type='checkbox'  ><label for='section-d6bafa2b-6c1b-48cb-8eda-f77d05a65824' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fbcdfcef-137f-46f5-9cd0-92aaf13e49f3' class='xr-index-data-in' type='checkbox'/><label for='index-fbcdfcef-137f-46f5-9cd0-92aaf13e49f3' 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(Float64Index([38.724998474121094,   38.7755012512207,  38.82600402832031,\n",
       "               38.87650680541992,  38.92700958251953,  38.97751235961914,\n",
       "               39.02801513671875,  39.07851791381836,   39.1290168762207,\n",
       "               39.17951965332031,\n",
       "              ...\n",
       "               48.32048034667969,  48.37097930908203, 48.421485900878906,\n",
       "               48.47198486328125,  48.52248764038086,  48.57299041748047,\n",
       "               48.62349319458008,  48.67399597167969,   48.7244987487793,\n",
       "              48.775001525878906],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=200))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c655dc9d-2226-4a6e-8fae-d220a9637af5' class='xr-index-data-in' type='checkbox'/><label for='index-c655dc9d-2226-4a6e-8fae-d220a9637af5' 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(Float64Index([            26.375, 26.424999237060547, 26.475000381469727,\n",
       "              26.524999618530273, 26.575000762939453,             26.625,\n",
       "              26.674999237060547, 26.725000381469727, 26.774999618530273,\n",
       "              26.825000762939453,\n",
       "              ...\n",
       "               41.92499923706055, 41.974998474121094, 42.025001525878906,\n",
       "               42.07500076293945,             42.125,  42.17499923706055,\n",
       "              42.224998474121094, 42.275001525878906,  42.32500076293945,\n",
       "                          42.375],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=321))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f79e5a1c-b97f-43b5-93ce-48c21385147b' class='xr-index-data-in' type='checkbox'/><label for='index-f79e5a1c-b97f-43b5-93ce-48c21385147b' 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;2015-12-31&#x27;, &#x27;2016-01-31&#x27;, &#x27;2016-02-29&#x27;, &#x27;2016-03-31&#x27;,\n",
       "               &#x27;2016-04-30&#x27;, &#x27;2016-05-31&#x27;, &#x27;2016-06-30&#x27;, &#x27;2016-07-31&#x27;,\n",
       "               &#x27;2016-08-31&#x27;, &#x27;2016-09-30&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-08-31&#x27;, &#x27;2023-09-30&#x27;, &#x27;2023-10-31&#x27;, &#x27;2023-11-30&#x27;,\n",
       "               &#x27;2023-12-31&#x27;, &#x27;2024-01-31&#x27;, &#x27;2024-02-29&#x27;, &#x27;2024-03-31&#x27;,\n",
       "               &#x27;2024-04-30&#x27;, &#x27;2024-05-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=102, freq=&#x27;M&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bbef0fe9-c7e7-4095-ac92-e55c36264eda' class='xr-section-summary-in' type='checkbox'  ><label for='section-bbef0fe9-c7e7-4095-ac92-e55c36264eda' class='xr-section-summary' >Attributes: <span>(52)</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-1.4</dd><dt><span>DSD_entry_id :</span></dt><dd>-GOS-L4HRfnd-BLK</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Scaling_Equation :</span></dt><dd>(scale_factor*data) + add_offset</dd><dt><span>acknowledgment :</span></dt><dd>Please acknowledge the use of these data with the following statement: Generated/provided by Copernicus Marine Service and CNR - ISMAR ROME. We would also appreciate being informed of any publications.</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>comment :</span></dt><dd>WARNING: some applications are unable to properly handle byte values. If Values &gt;127 are encounterd, please subtract 256</dd><dt><span>copernicusmarine_version :</span></dt><dd>1.0.0</dd><dt><span>creator_email :</span></dt><dd>gsdk@isac.cnr.it</dd><dt><span>creator_name :</span></dt><dd>ISMAR - Institute of Marine Sciences (CNR - ISMAR - GOS - Rome)</dd><dt><span>creator_url :</span></dt><dd>http://gosweb.artov.isac.cnr.it/</dd><dt><span>date_created :</span></dt><dd>20200610T223736Z</dd><dt><span>easternmost_longitude :</span></dt><dd>42.375</dd><dt><span>end_time :</span></dt><dd>20231231T190000Z</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>gds_version_id :</span></dt><dd>v2.0.5</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>GOS-CMEMS processor V4: new version</dd><dt><span>id :</span></dt><dd> </dd><dt><span>institution :</span></dt><dd>GOS</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>free registration at Copernicus Marine Service (http://marine.copernicus.eu/web/56-user-registration-form.php)</dd><dt><span>metadata_link :</span></dt><dd>Link to collection metadata record at archive</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>netcdf_version_id :</span></dt><dd>4.1.1, build date: JUN 18 2010&quot;</dd><dt><span>northernmost_latitude :</span></dt><dd>48.775001525878906</dd><dt><span>platform :</span></dt><dd>NOAA AVHRR, Metop-A AVHRR, (A)ATSR, Sentinel-3A SLSTR series of sensors</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_version :</span></dt><dd>3.0</dd><dt><span>project :</span></dt><dd>Copernicus Marine Environment Monitoring Service (CMEMS)</dd><dt><span>publisher_email :</span></dt><dd>servicedesk.cmems@mercator-ocean.eu, gsdk@isac.cnr.it</dd><dt><span>publisher_name :</span></dt><dd>CNR ISMAR GOS - CMEMS SST-TAC</dd><dt><span>publisher_url :</span></dt><dd>http://marine.copernicus.eu/</dd><dt><span>references :</span></dt><dd>A. Pisano, B. Buongiorno Nardelli, C. Tronconi, R. Santoleri: The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982-2012). /Remote Sensing of Environment./ 176 (2016) 107-116, doi:10.1016/j.rse.2016.01.019; http://pathfinder.nodc.noaa.gov and Casey, K.S., T.B. Brandon, P. Cornillon, and R. Evans: The Past, Present and Future of the AVHRR Pathfinder SST Program, in Oceanography from Space: Revisited, eds. V. Barale, J.F.R. Gower, and L. Alberotanza, Springer, 2010. DOI: 10.1007/978-90-481-8681-5_16.</dd><dt><span>sensor :</span></dt><dd>[1981-2016] NOAA AVHRR series of sensors (NOAA-07/09/11/12/14/15/16/17/18/19), ATSR1, ATSR2, (A)ATSR, MetopA AVHRR; [2017-2018]  NOAA AVHRR-19, MetopA AVHRR, SLSTR-3A</dd><dt><span>software_version :</span></dt><dd>Copernicus Marine Service HR L4 Processor V.2</dd><dt><span>source :</span></dt><dd>[1982-2016] ESA CCI SST v.2.0 L3C product (SST at 0.2m); [2017-2018] C3S v.2.0 L3C product (SST at 0.2m); [1982-2014]  Pathfinder-PFV5.3 L3C product (SST skin)</dd><dt><span>southernmost_latitude :</span></dt><dd>38.724998474121094</dd><dt><span>spatial_resolution :</span></dt><dd>0.05 degree</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>start_time :</span></dt><dd>20160101T070000Z</dd><dt><span>stop_time :</span></dt><dd>19810825T070000Z</dd><dt><span>summary :</span></dt><dd>[1982-2018] Daily gap-free maps (L4) at 0.05deg. x 0.05deg. horizontal resolution over the Black Sea. The maps are obtained using nighttime data extracted from ESA CCI SST v.2.0, C3S v.2.0 and PFV53 data</dd><dt><span>time_coverage_end :</span></dt><dd>&lt;xarray.DataArray &#x27;time&#x27; ()&gt;\n",
       "array(&#x27;2024-06-11T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2024-06-11\n",
       "Attributes:\n",
       "    valid_max:  2024-05-08T00:00:00.000000000\n",
       "    valid_min:  2016-01-01T00:00:00.000000000</dd><dt><span>time_coverage_start :</span></dt><dd>&lt;xarray.DataArray &#x27;time&#x27; ()&gt;\n",
       "array(&#x27;2016-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2016-01-01\n",
       "Attributes:\n",
       "    valid_max:  2024-05-08T00:00:00.000000000\n",
       "    valid_min:  2016-01-01T00:00:00.000000000</dd><dt><span>title :</span></dt><dd>CMEMS SST Black Sea</dd><dt><span>uuid :</span></dt><dd> </dd><dt><span>westernmost_longitude :</span></dt><dd>26.375</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (time: 102, latitude: 200, longitude: 321)\n",
       "Coordinates:\n",
       "  * latitude           (latitude) float32 38.72 38.78 38.83 ... 48.72 48.78\n",
       "  * longitude          (longitude) float32 26.38 26.42 26.48 ... 42.33 42.38\n",
       "  * time               (time) datetime64[ns] 2015-12-31 ... 2024-05-31\n",
       "Data variables:\n",
       "    analysed_sst_mean  (time, latitude, longitude) float32 dask.array<chunksize=(1, 200, 321), meta=np.ndarray>\n",
       "Attributes: (12/52)\n",
       "    Conventions:                CF-1.4\n",
       "    DSD_entry_id:               -GOS-L4HRfnd-BLK\n",
       "    Metadata_Conventions:       Unidata Dataset Discovery v1.0\n",
       "    Scaling_Equation:           (scale_factor*data) + add_offset\n",
       "    acknowledgment:             Please acknowledge the use of these data with...\n",
       "    cdm_data_type:              grid\n",
       "    ...                         ...\n",
       "    summary:                    [1982-2018] Daily gap-free maps (L4) at 0.05d...\n",
       "    time_coverage_end:          <xarray.DataArray 'time' ()>\\narray('2024-06-...\n",
       "    time_coverage_start:        <xarray.DataArray 'time' ()>\\narray('2016-01-...\n",
       "    title:                      CMEMS SST Black Sea\n",
       "    uuid:                        \n",
       "    westernmost_longitude:      26.375"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmems_sst_monthly_dataset = resample_in_time(sst_dataset, frequency='M', method='mean')\n",
    "cmems_sst_monthly_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2fa15a-2595-486d-9f2e-8aaecbb462f6",
   "metadata": {},
   "source": [
    "As the datasets were covering the same temporal period, they now have the same time dimension of 102. We can now resample the grid. For this, we can use the `resample_in_space` function. To use this, however, we need to specify the grid mappings of the two datasets. The grid mapping defines the spatial grid of a dataset and describes its properties such as the resolution, the crs, and sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a1386da-595c-4d81-af2a-3a9bfdc89719",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xcube.core.gridmapping import GridMapping\n",
    "from xcube.core.resampling import resample_in_space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "37624b07-fb37-4f7f-bf66-5e3dd2f3fea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "other_sst_gm = GridMapping.from_dataset(other_sst_data)\n",
    "cmems_sst_monthly_gm = GridMapping.from_dataset(cmems_sst_monthly_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb5e7b8c-4917-498a-bdfa-17455fecb57a",
   "metadata": {},
   "source": [
    "Now that we have the grid mappings, we can actually resample. We bring the other sst data onto the grid of the cmems sst data (we could also do it the other way round). This step might take a bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd69924e-f880-41f9-9f6c-82f3b0cf22e0",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  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:         (time: 102, latitude: 200, longitude: 321, bnds: 2)\n",
       "Coordinates:\n",
       "  * time            (time) datetime64[ns] 2015-12-31 2016-01-31 ... 2024-05-31\n",
       "  * longitude       (longitude) float64 26.38 26.43 26.48 ... 42.27 42.33 42.38\n",
       "  * latitude        (latitude) float64 38.72 38.78 38.83 ... 48.67 48.72 48.78\n",
       "    longitude_bnds  (longitude, bnds) float64 26.35 26.4 26.4 ... 42.35 42.4\n",
       "    latitude_bnds   (latitude, bnds) float64 38.7 38.75 38.75 ... 48.75 48.8\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    other_sst       (time, latitude, longitude) float64 dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;\n",
       "    crs             int64 0</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-0b6a1c72-cecd-4a93-adf2-0cf722e9144c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0b6a1c72-cecd-4a93-adf2-0cf722e9144c' 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>: 102</li><li><span class='xr-has-index'>latitude</span>: 200</li><li><span class='xr-has-index'>longitude</span>: 321</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5fcdaba0-7640-4ba4-abcd-fdf2fb22146e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5fcdaba0-7640-4ba4-abcd-fdf2fb22146e' 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'>2015-12-31 ... 2024-05-31</div><input id='attrs-68ba56c3-ebfe-46ef-9548-766c2ded89ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68ba56c3-ebfe-46ef-9548-766c2ded89ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17102646-705d-490e-ab86-57fca9eb42c1' class='xr-var-data-in' type='checkbox'><label for='data-17102646-705d-490e-ab86-57fca9eb42c1' 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>valid_max :</span></dt><dd>2024-05-08T00:00:00.000000000</dd><dt><span>valid_min :</span></dt><dd>2016-01-01T00:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-12-31T00:00:00.000000000&#x27;, &#x27;2016-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-02-29T00:00:00.000000000&#x27;, &#x27;2016-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-04-30T00:00:00.000000000&#x27;, &#x27;2016-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-30T00:00:00.000000000&#x27;, &#x27;2016-07-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2016-12-31T00:00:00.000000000&#x27;, &#x27;2017-01-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2017-06-30T00:00:00.000000000&#x27;, &#x27;2017-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-31T00:00:00.000000000&#x27;, &#x27;2017-09-30T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2018-06-30T00:00:00.000000000&#x27;, &#x27;2018-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-08-31T00:00:00.000000000&#x27;, &#x27;2018-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-10-31T00:00:00.000000000&#x27;, &#x27;2018-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-12-31T00:00:00.000000000&#x27;, &#x27;2019-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-02-28T00:00:00.000000000&#x27;, &#x27;2019-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-04-30T00:00:00.000000000&#x27;, &#x27;2019-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-06-30T00:00:00.000000000&#x27;, &#x27;2019-07-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2019-10-31T00:00:00.000000000&#x27;, &#x27;2019-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-12-31T00:00:00.000000000&#x27;, &#x27;2020-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-29T00:00:00.000000000&#x27;, &#x27;2020-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-04-30T00:00:00.000000000&#x27;, &#x27;2020-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-06-30T00:00:00.000000000&#x27;, &#x27;2020-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-08-31T00:00:00.000000000&#x27;, &#x27;2020-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-10-31T00:00:00.000000000&#x27;, &#x27;2020-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T00:00:00.000000000&#x27;, &#x27;2021-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-02-28T00:00:00.000000000&#x27;, &#x27;2021-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-04-30T00:00:00.000000000&#x27;, &#x27;2021-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-06-30T00:00:00.000000000&#x27;, &#x27;2021-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-31T00:00:00.000000000&#x27;, &#x27;2021-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-10-31T00:00:00.000000000&#x27;, &#x27;2021-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-31T00:00:00.000000000&#x27;, &#x27;2022-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-02-28T00:00:00.000000000&#x27;, &#x27;2022-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-04-30T00:00:00.000000000&#x27;, &#x27;2022-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-30T00:00:00.000000000&#x27;, &#x27;2022-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-31T00:00:00.000000000&#x27;, &#x27;2022-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-10-31T00:00:00.000000000&#x27;, &#x27;2022-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-12-31T00:00:00.000000000&#x27;, &#x27;2023-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-02-28T00:00:00.000000000&#x27;, &#x27;2023-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-04-30T00:00:00.000000000&#x27;, &#x27;2023-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-06-30T00:00:00.000000000&#x27;, &#x27;2023-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-08-31T00:00:00.000000000&#x27;, &#x27;2023-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-10-31T00:00:00.000000000&#x27;, &#x27;2023-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-12-31T00:00:00.000000000&#x27;, &#x27;2024-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-02-29T00:00:00.000000000&#x27;, &#x27;2024-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-04-30T00:00:00.000000000&#x27;, &#x27;2024-05-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>26.38 26.43 26.48 ... 42.33 42.38</div><input id='attrs-43044f56-75e3-4a83-84e9-e6221cc366e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43044f56-75e3-4a83-84e9-e6221cc366e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9f77952-9cf6-422f-b971-6af05823e98f' class='xr-var-data-in' type='checkbox'><label for='data-a9f77952-9cf6-422f-b971-6af05823e98f' 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 coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>bounds :</span></dt><dd>longitude_bnds</dd></dl></div><div class='xr-var-data'><pre>array([26.375, 26.425, 26.475, ..., 42.275, 42.325, 42.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>38.72 38.78 38.83 ... 48.72 48.78</div><input id='attrs-e588138c-9053-47bd-a218-dd5294c5c6fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e588138c-9053-47bd-a218-dd5294c5c6fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77a020d8-c263-4289-bf0e-c2bdc299ea6d' class='xr-var-data-in' type='checkbox'><label for='data-77a020d8-c263-4289-bf0e-c2bdc299ea6d' 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 coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>bounds :</span></dt><dd>latitude_bnds</dd></dl></div><div class='xr-var-data'><pre>array([38.724998, 38.775501, 38.826004, 38.876506, 38.927009, 38.977511,\n",
       "       39.028014, 39.078516, 39.129019, 39.179521, 39.230024, 39.280526,\n",
       "       39.331029, 39.381531, 39.432034, 39.482536, 39.533039, 39.583541,\n",
       "       39.634044, 39.684547, 39.735049, 39.785552, 39.836054, 39.886557,\n",
       "       39.937059, 39.987562, 40.038064, 40.088567, 40.139069, 40.189572,\n",
       "       40.240074, 40.290577, 40.341079, 40.391582, 40.442084, 40.492587,\n",
       "       40.543089, 40.593592, 40.644095, 40.694597, 40.7451  , 40.795602,\n",
       "       40.846105, 40.896607, 40.94711 , 40.997612, 41.048115, 41.098617,\n",
       "       41.14912 , 41.199622, 41.250125, 41.300627, 41.35113 , 41.401632,\n",
       "       41.452135, 41.502638, 41.55314 , 41.603643, 41.654145, 41.704648,\n",
       "       41.75515 , 41.805653, 41.856155, 41.906658, 41.95716 , 42.007663,\n",
       "       42.058165, 42.108668, 42.15917 , 42.209673, 42.260175, 42.310678,\n",
       "       42.36118 , 42.411683, 42.462186, 42.512688, 42.563191, 42.613693,\n",
       "       42.664196, 42.714698, 42.765201, 42.815703, 42.866206, 42.916708,\n",
       "       42.967211, 43.017713, 43.068216, 43.118718, 43.169221, 43.219723,\n",
       "       43.270226, 43.320729, 43.371231, 43.421734, 43.472236, 43.522739,\n",
       "       43.573241, 43.623744, 43.674246, 43.724749, 43.775251, 43.825754,\n",
       "       43.876256, 43.926759, 43.977261, 44.027764, 44.078266, 44.128769,\n",
       "       44.179271, 44.229774, 44.280277, 44.330779, 44.381282, 44.431784,\n",
       "       44.482287, 44.532789, 44.583292, 44.633794, 44.684297, 44.734799,\n",
       "       44.785302, 44.835804, 44.886307, 44.936809, 44.987312, 45.037814,\n",
       "       45.088317, 45.13882 , 45.189322, 45.239825, 45.290327, 45.34083 ,\n",
       "       45.391332, 45.441835, 45.492337, 45.54284 , 45.593342, 45.643845,\n",
       "       45.694347, 45.74485 , 45.795352, 45.845855, 45.896357, 45.94686 ,\n",
       "       45.997362, 46.047865, 46.098368, 46.14887 , 46.199373, 46.249875,\n",
       "       46.300378, 46.35088 , 46.401383, 46.451885, 46.502388, 46.55289 ,\n",
       "       46.603393, 46.653895, 46.704398, 46.7549  , 46.805403, 46.855905,\n",
       "       46.906408, 46.956911, 47.007413, 47.057916, 47.108418, 47.158921,\n",
       "       47.209423, 47.259926, 47.310428, 47.360931, 47.411433, 47.461936,\n",
       "       47.512438, 47.562941, 47.613443, 47.663946, 47.714448, 47.764951,\n",
       "       47.815453, 47.865956, 47.916459, 47.966961, 48.017464, 48.067966,\n",
       "       48.118469, 48.168971, 48.219474, 48.269976, 48.320479, 48.370981,\n",
       "       48.421484, 48.471986, 48.522489, 48.572991, 48.623494, 48.673996,\n",
       "       48.724499, 48.775002])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_bnds</span></div><div class='xr-var-dims'>(longitude, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>26.35 26.4 26.4 ... 42.35 42.4</div><input id='attrs-b57a7b12-4382-4304-a868-bf0df15aba63' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b57a7b12-4382-4304-a868-bf0df15aba63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09f8cc7e-f950-4481-8bf3-33e8833389b0' class='xr-var-data-in' type='checkbox'><label for='data-09f8cc7e-f950-4481-8bf3-33e8833389b0' 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([[26.35000038, 26.39999962],\n",
       "       [26.40000038, 26.44999963],\n",
       "       [26.45000038, 26.49999963],\n",
       "       [26.50000037, 26.54999962],\n",
       "       [26.55000037, 26.59999962],\n",
       "       [26.60000038, 26.64999962],\n",
       "       [26.65000038, 26.69999963],\n",
       "       [26.70000038, 26.74999963],\n",
       "       [26.75000037, 26.79999962],\n",
       "       [26.80000037, 26.84999962],\n",
       "       [26.85000038, 26.89999962],\n",
       "       [26.90000038, 26.94999963],\n",
       "       [26.95000038, 26.99999963],\n",
       "       [27.00000037, 27.04999962],\n",
       "       [27.05000037, 27.09999962],\n",
       "       [27.10000038, 27.14999962],\n",
       "       [27.15000038, 27.19999963],\n",
       "       [27.20000038, 27.24999963],\n",
       "       [27.25000037, 27.29999962],\n",
       "       [27.30000037, 27.34999962],\n",
       "...\n",
       "       [41.40000038, 41.44999963],\n",
       "       [41.45000038, 41.49999963],\n",
       "       [41.50000037, 41.54999962],\n",
       "       [41.55000038, 41.59999963],\n",
       "       [41.60000038, 41.64999962],\n",
       "       [41.65000038, 41.69999963],\n",
       "       [41.70000038, 41.74999963],\n",
       "       [41.75000037, 41.79999962],\n",
       "       [41.80000038, 41.84999963],\n",
       "       [41.85000038, 41.89999962],\n",
       "       [41.90000038, 41.94999963],\n",
       "       [41.95000038, 41.99999963],\n",
       "       [42.00000037, 42.04999962],\n",
       "       [42.05000038, 42.09999963],\n",
       "       [42.10000038, 42.14999962],\n",
       "       [42.15000038, 42.19999963],\n",
       "       [42.20000038, 42.24999963],\n",
       "       [42.25000037, 42.29999962],\n",
       "       [42.30000038, 42.34999963],\n",
       "       [42.35000038, 42.39999962]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_bnds</span></div><div class='xr-var-dims'>(latitude, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>38.7 38.75 38.75 ... 48.75 48.8</div><input id='attrs-0d7b88db-04ad-4a7b-bb6c-558a810d20c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d7b88db-04ad-4a7b-bb6c-558a810d20c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-331b44ce-2122-4bf3-8bc5-1f7bfbcaca35' class='xr-var-data-in' type='checkbox'><label for='data-331b44ce-2122-4bf3-8bc5-1f7bfbcaca35' 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([[38.6997471 , 38.75024985],\n",
       "       [38.75024963, 38.80075238],\n",
       "       [38.80075215, 38.8512549 ],\n",
       "       [38.85125468, 38.90175743],\n",
       "       [38.90175721, 38.95225996],\n",
       "       [38.95225974, 39.00276249],\n",
       "       [39.00276227, 39.05326502],\n",
       "       [39.05326479, 39.10376754],\n",
       "       [39.10376732, 39.15427007],\n",
       "       [39.15426985, 39.2047726 ],\n",
       "       [39.20477238, 39.25527513],\n",
       "       [39.25527491, 39.30577766],\n",
       "       [39.30577743, 39.35628018],\n",
       "       [39.35627996, 39.40678271],\n",
       "       [39.40678249, 39.45728524],\n",
       "       [39.45728502, 39.50778777],\n",
       "       [39.50778755, 39.5582903 ],\n",
       "       [39.55829007, 39.60879282],\n",
       "       [39.6087926 , 39.65929535],\n",
       "       [39.65929513, 39.70979788],\n",
       "...\n",
       "       [47.79020212, 47.84070487],\n",
       "       [47.84070465, 47.8912074 ],\n",
       "       [47.89120718, 47.94170993],\n",
       "       [47.9417097 , 47.99221245],\n",
       "       [47.99221223, 48.04271498],\n",
       "       [48.04271476, 48.09321751],\n",
       "       [48.09321729, 48.14372004],\n",
       "       [48.14371982, 48.19422257],\n",
       "       [48.19422234, 48.24472509],\n",
       "       [48.24472487, 48.29522762],\n",
       "       [48.2952274 , 48.34573015],\n",
       "       [48.34572993, 48.39623268],\n",
       "       [48.39623246, 48.44673521],\n",
       "       [48.44673498, 48.49723773],\n",
       "       [48.49723751, 48.54774026],\n",
       "       [48.54774004, 48.59824279],\n",
       "       [48.59824257, 48.64874532],\n",
       "       [48.6487451 , 48.69924785],\n",
       "       [48.69924762, 48.74975037],\n",
       "       [48.74975015, 48.8002529 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d8ed2da-5a5d-421b-8c05-f3e03c97cc0b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d8ed2da-5a5d-421b-8c05-f3e03c97cc0b' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>other_sst</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;</div><input id='attrs-5763b216-590e-4dff-ad25-63e4da775a63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5763b216-590e-4dff-ad25-63e4da775a63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4534d59b-acd6-4243-aa8a-129e3d5f8255' class='xr-var-data-in' type='checkbox'><label for='data-4534d59b-acd6-4243-aa8a-129e3d5f8255' 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>grid_mapping :</span></dt><dd>crs</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> 49.96 MiB </td>\n",
       "                        <td> 501.56 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (102, 200, 321) </td>\n",
       "                        <td> (1, 200, 321) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 102 chunks in 215 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-d96e5640-19d6-442b-85d1-9baf776c2715' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d96e5640-19d6-442b-85d1-9baf776c2715' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c99a573f-246d-453f-a6ae-c0579ff035a7' class='xr-var-data-in' type='checkbox'><label for='data-c99a573f-246d-453f-a6ae-c0579ff035a7' 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>crs_wkt :</span></dt><dd>GEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],CS[ellipsoidal,2],AXIS[&quot;geodetic latitude (Lat)&quot;,north,ORDER[1],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],AXIS[&quot;geodetic longitude (Lon)&quot;,east,ORDER[2],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],USAGE[SCOPE[&quot;Horizontal component of 3D system.&quot;],AREA[&quot;World.&quot;],BBOX[-90,-180,90,180]],ID[&quot;EPSG&quot;,4326]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984 ensemble</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4332d24-ecc0-4b84-980a-f6154dfff0ee' class='xr-section-summary-in' type='checkbox'  ><label for='section-f4332d24-ecc0-4b84-980a-f6154dfff0ee' 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</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-95818839-be66-4c87-8d33-03b0860e13c6' class='xr-index-data-in' type='checkbox'/><label for='index-95818839-be66-4c87-8d33-03b0860e13c6' 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;2015-12-31&#x27;, &#x27;2016-01-31&#x27;, &#x27;2016-02-29&#x27;, &#x27;2016-03-31&#x27;,\n",
       "               &#x27;2016-04-30&#x27;, &#x27;2016-05-31&#x27;, &#x27;2016-06-30&#x27;, &#x27;2016-07-31&#x27;,\n",
       "               &#x27;2016-08-31&#x27;, &#x27;2016-09-30&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-08-31&#x27;, &#x27;2023-09-30&#x27;, &#x27;2023-10-31&#x27;, &#x27;2023-11-30&#x27;,\n",
       "               &#x27;2023-12-31&#x27;, &#x27;2024-01-31&#x27;, &#x27;2024-02-29&#x27;, &#x27;2024-03-31&#x27;,\n",
       "               &#x27;2024-04-30&#x27;, &#x27;2024-05-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=102, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a15b7a7-2582-4f97-80ac-bd629a481d71' class='xr-index-data-in' type='checkbox'/><label for='index-8a15b7a7-2582-4f97-80ac-bd629a481d71' 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(Float64Index([26.375, 26.425, 26.475, 26.525, 26.575, 26.625, 26.675, 26.725,\n",
       "              26.775, 26.825,\n",
       "              ...\n",
       "              41.925, 41.975, 42.025, 42.075, 42.125, 42.175, 42.225, 42.275,\n",
       "              42.325, 42.375],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=321))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b77f3fdb-f779-4670-ae17-4836e540c2bd' class='xr-index-data-in' type='checkbox'/><label for='index-b77f3fdb-f779-4670-ae17-4836e540c2bd' 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(Float64Index([38.724998474121094, 38.775501002019375,  38.82600352991766,\n",
       "               38.87650605781594,  38.92700858571421,  38.97751111361249,\n",
       "              39.028013641510775, 39.078516169409056,  39.12901869730734,\n",
       "               39.17952122520562,\n",
       "              ...\n",
       "               48.32047877479438,  48.37098130269266, 48.421483830590944,\n",
       "              48.471986358489225,  48.52248888638751,  48.57299141428578,\n",
       "               48.62349394218406,  48.67399647008234, 48.724498997980625,\n",
       "              48.775001525878906],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=200))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b2a4aa4-710c-4afc-bf8d-1299243afff2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b2a4aa4-710c-4afc-bf8d-1299243afff2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (time: 102, latitude: 200, longitude: 321, bnds: 2)\n",
       "Coordinates:\n",
       "  * time            (time) datetime64[ns] 2015-12-31 2016-01-31 ... 2024-05-31\n",
       "  * longitude       (longitude) float64 26.38 26.43 26.48 ... 42.27 42.33 42.38\n",
       "  * latitude        (latitude) float64 38.72 38.78 38.83 ... 48.67 48.72 48.78\n",
       "    longitude_bnds  (longitude, bnds) float64 26.35 26.4 26.4 ... 42.35 42.4\n",
       "    latitude_bnds   (latitude, bnds) float64 38.7 38.75 38.75 ... 48.75 48.8\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    other_sst       (time, latitude, longitude) float64 dask.array<chunksize=(1, 200, 321), meta=np.ndarray>\n",
       "    crs             int64 0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_resampled_sst_data = resample_in_space(other_sst_data, source_gm=other_sst_gm, target_gm=cmems_sst_monthly_gm)\n",
    "other_resampled_sst_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd818d6-01b5-4af0-84ae-26d388c89586",
   "metadata": {},
   "source": [
    "The dimension sizes are looking good now! However, when you compare the actual latitude and longitude values between the resampled and the reference dataset, you will notice that they are slightly off due to different roundings. We overcome this issue by assigning the coordinate arrays from the reference dataset to the resampled one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3c745eed-7c20-4f03-b4d1-5a108feae93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "other_resampled_sst_data['latitude'] = cmems_sst_monthly_dataset['latitude']\n",
    "other_resampled_sst_data['longitude'] = cmems_sst_monthly_dataset['longitude']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "985bc075-ea54-46a0-a71c-9b5b3fe2902d",
   "metadata": {},
   "source": [
    "Now the datasets have the same spatial and temporal grids, and we can merge them into one. Technically, this would have also been possible without the resampling steps, but that would have resulted in a very bloated dataset with irregular dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7115bbd-b7ce-4480-b143-8ad4cafb953e",
   "metadata": {},
   "outputs": [
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       ".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:            (time: 102, latitude: 200, longitude: 321, bnds: 2)\n",
       "Coordinates:\n",
       "  * latitude           (latitude) float32 38.72 38.78 38.83 ... 48.72 48.78\n",
       "  * longitude          (longitude) float32 26.38 26.42 26.48 ... 42.33 42.38\n",
       "  * time               (time) datetime64[ns] 2015-12-31 ... 2024-05-31\n",
       "    longitude_bnds     (longitude, bnds) float64 26.35 26.4 26.4 ... 42.35 42.4\n",
       "    latitude_bnds      (latitude, bnds) float64 38.7 38.75 38.75 ... 48.75 48.8\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    analysed_sst_mean  (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;\n",
       "    other_sst          (time, latitude, longitude) float64 dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;\n",
       "    crs                int64 0\n",
       "Attributes: (12/52)\n",
       "    Conventions:                CF-1.4\n",
       "    DSD_entry_id:               -GOS-L4HRfnd-BLK\n",
       "    Metadata_Conventions:       Unidata Dataset Discovery v1.0\n",
       "    Scaling_Equation:           (scale_factor*data) + add_offset\n",
       "    acknowledgment:             Please acknowledge the use of these data with...\n",
       "    cdm_data_type:              grid\n",
       "    ...                         ...\n",
       "    summary:                    [1982-2018] Daily gap-free maps (L4) at 0.05d...\n",
       "    time_coverage_end:          &lt;xarray.DataArray &#x27;time&#x27; ()&gt;\\narray(&#x27;2024-06-...\n",
       "    time_coverage_start:        &lt;xarray.DataArray &#x27;time&#x27; ()&gt;\\narray(&#x27;2016-01-...\n",
       "    title:                      CMEMS SST Black Sea\n",
       "    uuid:                        \n",
       "    westernmost_longitude:      26.375</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-f70ce1ae-8bac-4ecc-9c3a-4e268963639e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f70ce1ae-8bac-4ecc-9c3a-4e268963639e' 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>: 102</li><li><span class='xr-has-index'>latitude</span>: 200</li><li><span class='xr-has-index'>longitude</span>: 321</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-73f4a703-a26d-4537-a85d-c5beda7afd7f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73f4a703-a26d-4537-a85d-c5beda7afd7f' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>38.72 38.78 38.83 ... 48.72 48.78</div><input id='attrs-081b86f7-a668-4843-82e2-5a82535d9a3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-081b86f7-a668-4843-82e2-5a82535d9a3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1d0e1b2-7d8c-4ae8-b79c-e1a688afe72d' class='xr-var-data-in' type='checkbox'><label for='data-d1d0e1b2-7d8c-4ae8-b79c-e1a688afe72d' 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>Y</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>48.775001525878906</dd><dt><span>valid_min :</span></dt><dd>38.724998474121094</dd></dl></div><div class='xr-var-data'><pre>array([38.725   , 38.7755  , 38.826004, 38.876507, 38.92701 , 38.977512,\n",
       "       39.028015, 39.078518, 39.129017, 39.17952 , 39.230022, 39.280525,\n",
       "       39.331028, 39.38153 , 39.432034, 39.482536, 39.53304 , 39.58354 ,\n",
       "       39.634045, 39.684547, 39.73505 , 39.785553, 39.836056, 39.88656 ,\n",
       "       39.937057, 39.98756 , 40.038063, 40.088566, 40.13907 , 40.18957 ,\n",
       "       40.240074, 40.290577, 40.34108 , 40.391582, 40.442085, 40.492588,\n",
       "       40.54309 , 40.593594, 40.644096, 40.694595, 40.7451  , 40.7956  ,\n",
       "       40.846104, 40.896606, 40.94711 , 40.997612, 41.048115, 41.098618,\n",
       "       41.14912 , 41.199623, 41.250126, 41.30063 , 41.35113 , 41.401634,\n",
       "       41.452133, 41.502636, 41.55314 , 41.60364 , 41.654144, 41.704647,\n",
       "       41.75515 , 41.805653, 41.856155, 41.90666 , 41.95716 , 42.007664,\n",
       "       42.058167, 42.10867 , 42.159172, 42.20967 , 42.260174, 42.310677,\n",
       "       42.36118 , 42.411682, 42.462185, 42.512688, 42.56319 , 42.613693,\n",
       "       42.664196, 42.7147  , 42.7652  , 42.815704, 42.866207, 42.91671 ,\n",
       "       42.96721 , 43.017715, 43.068214, 43.118717, 43.16922 , 43.219723,\n",
       "       43.270226, 43.32073 , 43.37123 , 43.421734, 43.472237, 43.52274 ,\n",
       "       43.573242, 43.623745, 43.674248, 43.724747, 43.775253, 43.825752,\n",
       "       43.876255, 43.926758, 43.97726 , 44.027763, 44.078266, 44.12877 ,\n",
       "       44.17927 , 44.229774, 44.280277, 44.33078 , 44.381283, 44.431786,\n",
       "       44.482285, 44.53279 , 44.58329 , 44.633793, 44.684296, 44.7348  ,\n",
       "       44.7853  , 44.835804, 44.886307, 44.93681 , 44.987312, 45.037815,\n",
       "       45.088318, 45.13882 , 45.189323, 45.239822, 45.29033 , 45.340828,\n",
       "       45.39133 , 45.441833, 45.492336, 45.54284 , 45.59334 , 45.643845,\n",
       "       45.694347, 45.74485 , 45.795353, 45.845856, 45.89636 , 45.94686 ,\n",
       "       45.997364, 46.047867, 46.098366, 46.14887 , 46.19937 , 46.249874,\n",
       "       46.300377, 46.35088 , 46.401382, 46.451885, 46.502388, 46.55289 ,\n",
       "       46.603394, 46.653896, 46.7044  , 46.754898, 46.805405, 46.855904,\n",
       "       46.90641 , 46.95691 , 47.007412, 47.057915, 47.108418, 47.15892 ,\n",
       "       47.209423, 47.259926, 47.31043 , 47.36093 , 47.411434, 47.461937,\n",
       "       47.512436, 47.562943, 47.61344 , 47.663948, 47.714447, 47.76495 ,\n",
       "       47.815453, 47.865955, 47.91646 , 47.96696 , 48.017464, 48.067966,\n",
       "       48.11847 , 48.168972, 48.219475, 48.269978, 48.32048 , 48.37098 ,\n",
       "       48.421486, 48.471985, 48.522488, 48.57299 , 48.623493, 48.673996,\n",
       "       48.7245  , 48.775   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>26.38 26.42 26.48 ... 42.33 42.38</div><input id='attrs-68e8aa5b-927e-44ef-bc29-d2fc068f13e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68e8aa5b-927e-44ef-bc29-d2fc068f13e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29ffeb1e-b042-4334-8470-b99d0bcf5650' class='xr-var-data-in' type='checkbox'><label for='data-29ffeb1e-b042-4334-8470-b99d0bcf5650' 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>X</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>42.375</dd><dt><span>valid_min :</span></dt><dd>26.375</dd></dl></div><div class='xr-var-data'><pre>array([26.375, 26.425, 26.475, ..., 42.275, 42.325, 42.375], dtype=float32)</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'>2015-12-31 ... 2024-05-31</div><input id='attrs-e9c80edf-097c-4b50-b50c-e04aea7bd5d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9c80edf-097c-4b50-b50c-e04aea7bd5d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-814ee183-9726-46ac-b24f-5aede89104af' class='xr-var-data-in' type='checkbox'><label for='data-814ee183-9726-46ac-b24f-5aede89104af' 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>valid_max :</span></dt><dd>2024-05-08T00:00:00.000000000</dd><dt><span>valid_min :</span></dt><dd>2016-01-01T00:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-12-31T00:00:00.000000000&#x27;, &#x27;2016-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-02-29T00:00:00.000000000&#x27;, &#x27;2016-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-04-30T00:00:00.000000000&#x27;, &#x27;2016-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-30T00:00:00.000000000&#x27;, &#x27;2016-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-08-31T00:00:00.000000000&#x27;, &#x27;2016-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-10-31T00:00:00.000000000&#x27;, &#x27;2016-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-31T00:00:00.000000000&#x27;, &#x27;2017-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-02-28T00:00:00.000000000&#x27;, &#x27;2017-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-04-30T00:00:00.000000000&#x27;, &#x27;2017-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-06-30T00:00:00.000000000&#x27;, &#x27;2017-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-31T00:00:00.000000000&#x27;, &#x27;2017-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-31T00:00:00.000000000&#x27;, &#x27;2017-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-12-31T00:00:00.000000000&#x27;, &#x27;2018-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-02-28T00:00:00.000000000&#x27;, &#x27;2018-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-04-30T00:00:00.000000000&#x27;, &#x27;2018-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-06-30T00:00:00.000000000&#x27;, &#x27;2018-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-08-31T00:00:00.000000000&#x27;, &#x27;2018-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-10-31T00:00:00.000000000&#x27;, &#x27;2018-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-12-31T00:00:00.000000000&#x27;, &#x27;2019-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-02-28T00:00:00.000000000&#x27;, &#x27;2019-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-04-30T00:00:00.000000000&#x27;, &#x27;2019-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-06-30T00:00:00.000000000&#x27;, &#x27;2019-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-08-31T00:00:00.000000000&#x27;, &#x27;2019-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-10-31T00:00:00.000000000&#x27;, &#x27;2019-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-12-31T00:00:00.000000000&#x27;, &#x27;2020-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-29T00:00:00.000000000&#x27;, &#x27;2020-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-04-30T00:00:00.000000000&#x27;, &#x27;2020-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-06-30T00:00:00.000000000&#x27;, &#x27;2020-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-08-31T00:00:00.000000000&#x27;, &#x27;2020-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-10-31T00:00:00.000000000&#x27;, &#x27;2020-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T00:00:00.000000000&#x27;, &#x27;2021-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-02-28T00:00:00.000000000&#x27;, &#x27;2021-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-04-30T00:00:00.000000000&#x27;, &#x27;2021-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-06-30T00:00:00.000000000&#x27;, &#x27;2021-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-31T00:00:00.000000000&#x27;, &#x27;2021-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-10-31T00:00:00.000000000&#x27;, &#x27;2021-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-31T00:00:00.000000000&#x27;, &#x27;2022-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-02-28T00:00:00.000000000&#x27;, &#x27;2022-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-04-30T00:00:00.000000000&#x27;, &#x27;2022-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-30T00:00:00.000000000&#x27;, &#x27;2022-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-08-31T00:00:00.000000000&#x27;, &#x27;2022-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-10-31T00:00:00.000000000&#x27;, &#x27;2022-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-12-31T00:00:00.000000000&#x27;, &#x27;2023-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-02-28T00:00:00.000000000&#x27;, &#x27;2023-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-04-30T00:00:00.000000000&#x27;, &#x27;2023-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-06-30T00:00:00.000000000&#x27;, &#x27;2023-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-08-31T00:00:00.000000000&#x27;, &#x27;2023-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-10-31T00:00:00.000000000&#x27;, &#x27;2023-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-12-31T00:00:00.000000000&#x27;, &#x27;2024-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-02-29T00:00:00.000000000&#x27;, &#x27;2024-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-04-30T00:00:00.000000000&#x27;, &#x27;2024-05-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_bnds</span></div><div class='xr-var-dims'>(longitude, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>26.35 26.4 26.4 ... 42.35 42.4</div><input id='attrs-43af94b9-cff9-4634-b847-e93e590b5ce7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43af94b9-cff9-4634-b847-e93e590b5ce7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8e4fa09-3709-49ee-8af6-1f1160966681' class='xr-var-data-in' type='checkbox'><label for='data-c8e4fa09-3709-49ee-8af6-1f1160966681' 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([[26.35000038, 26.39999962],\n",
       "       [26.40000038, 26.44999963],\n",
       "       [26.45000038, 26.49999963],\n",
       "       [26.50000037, 26.54999962],\n",
       "       [26.55000037, 26.59999962],\n",
       "       [26.60000038, 26.64999962],\n",
       "       [26.65000038, 26.69999963],\n",
       "       [26.70000038, 26.74999963],\n",
       "       [26.75000037, 26.79999962],\n",
       "       [26.80000037, 26.84999962],\n",
       "       [26.85000038, 26.89999962],\n",
       "       [26.90000038, 26.94999963],\n",
       "       [26.95000038, 26.99999963],\n",
       "       [27.00000037, 27.04999962],\n",
       "       [27.05000037, 27.09999962],\n",
       "       [27.10000038, 27.14999962],\n",
       "       [27.15000038, 27.19999963],\n",
       "       [27.20000038, 27.24999963],\n",
       "       [27.25000037, 27.29999962],\n",
       "       [27.30000037, 27.34999962],\n",
       "...\n",
       "       [41.40000038, 41.44999963],\n",
       "       [41.45000038, 41.49999963],\n",
       "       [41.50000037, 41.54999962],\n",
       "       [41.55000038, 41.59999963],\n",
       "       [41.60000038, 41.64999962],\n",
       "       [41.65000038, 41.69999963],\n",
       "       [41.70000038, 41.74999963],\n",
       "       [41.75000037, 41.79999962],\n",
       "       [41.80000038, 41.84999963],\n",
       "       [41.85000038, 41.89999962],\n",
       "       [41.90000038, 41.94999963],\n",
       "       [41.95000038, 41.99999963],\n",
       "       [42.00000037, 42.04999962],\n",
       "       [42.05000038, 42.09999963],\n",
       "       [42.10000038, 42.14999962],\n",
       "       [42.15000038, 42.19999963],\n",
       "       [42.20000038, 42.24999963],\n",
       "       [42.25000037, 42.29999962],\n",
       "       [42.30000038, 42.34999963],\n",
       "       [42.35000038, 42.39999962]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_bnds</span></div><div class='xr-var-dims'>(latitude, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>38.7 38.75 38.75 ... 48.75 48.8</div><input id='attrs-4414e9f2-9f2f-40c9-965d-9e3c578a0ad4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4414e9f2-9f2f-40c9-965d-9e3c578a0ad4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a9f1dc6-fce7-449e-841c-962960df5928' class='xr-var-data-in' type='checkbox'><label for='data-9a9f1dc6-fce7-449e-841c-962960df5928' 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([[38.6997471 , 38.75024985],\n",
       "       [38.75024963, 38.80075238],\n",
       "       [38.80075215, 38.8512549 ],\n",
       "       [38.85125468, 38.90175743],\n",
       "       [38.90175721, 38.95225996],\n",
       "       [38.95225974, 39.00276249],\n",
       "       [39.00276227, 39.05326502],\n",
       "       [39.05326479, 39.10376754],\n",
       "       [39.10376732, 39.15427007],\n",
       "       [39.15426985, 39.2047726 ],\n",
       "       [39.20477238, 39.25527513],\n",
       "       [39.25527491, 39.30577766],\n",
       "       [39.30577743, 39.35628018],\n",
       "       [39.35627996, 39.40678271],\n",
       "       [39.40678249, 39.45728524],\n",
       "       [39.45728502, 39.50778777],\n",
       "       [39.50778755, 39.5582903 ],\n",
       "       [39.55829007, 39.60879282],\n",
       "       [39.6087926 , 39.65929535],\n",
       "       [39.65929513, 39.70979788],\n",
       "...\n",
       "       [47.79020212, 47.84070487],\n",
       "       [47.84070465, 47.8912074 ],\n",
       "       [47.89120718, 47.94170993],\n",
       "       [47.9417097 , 47.99221245],\n",
       "       [47.99221223, 48.04271498],\n",
       "       [48.04271476, 48.09321751],\n",
       "       [48.09321729, 48.14372004],\n",
       "       [48.14371982, 48.19422257],\n",
       "       [48.19422234, 48.24472509],\n",
       "       [48.24472487, 48.29522762],\n",
       "       [48.2952274 , 48.34573015],\n",
       "       [48.34572993, 48.39623268],\n",
       "       [48.39623246, 48.44673521],\n",
       "       [48.44673498, 48.49723773],\n",
       "       [48.49723751, 48.54774026],\n",
       "       [48.54774004, 48.59824279],\n",
       "       [48.59824257, 48.64874532],\n",
       "       [48.6487451 , 48.69924785],\n",
       "       [48.69924762, 48.74975037],\n",
       "       [48.74975015, 48.8002529 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58b8e6b3-a452-4a0f-bd7d-30e49411a085' class='xr-section-summary-in' type='checkbox'  checked><label for='section-58b8e6b3-a452-4a0f-bd7d-30e49411a085' class='xr-section-summary' >Data variables: <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>analysed_sst_mean</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;</div><input id='attrs-55dedd83-c90a-4e74-bd7a-6215312871e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55dedd83-c90a-4e74-bd7a-6215312871e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-114e1f2e-9ddb-4ce7-a3dd-2071715a42b8' class='xr-var-data-in' type='checkbox'><label for='data-114e1f2e-9ddb-4ce7-a3dd-2071715a42b8' 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>[1982-2018] Optimal interpolation (OI) SST measurements from ESA CCI SST v2.0, C3S v.2.0 and PFV53</dd><dt><span>long name :</span></dt><dd>Analysed Sea Surface Temperature</dd><dt><span>long_name :</span></dt><dd>Analysed Sea Surface Temperature</dd><dt><span>source :</span></dt><dd>[1982-2016] ESA CCI SST v.2.0 L3C product (SST at 0.2m); [2017-2018] C3S v.2.0 L3C product (SST at 0.2m); [1982-2014]Pathfinder-PFV5.3 L3C product (SST skin)</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>time_coverage_end :</span></dt><dd>20240508T190000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20160101T070000Z</dd><dt><span>type :</span></dt><dd>foundation</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>4500</dd><dt><span>valid_min :</span></dt><dd>-300</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> 24.98 MiB </td>\n",
       "                        <td> 250.78 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (102, 200, 321) </td>\n",
       "                        <td> (1, 200, 321) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 102 chunks in 614 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>other_sst</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;</div><input id='attrs-f576125c-50f4-4277-97ea-99bfdd4fb1ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f576125c-50f4-4277-97ea-99bfdd4fb1ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69c0467c-48f4-4c0c-aa4d-028c5db56960' class='xr-var-data-in' type='checkbox'><label for='data-69c0467c-48f4-4c0c-aa4d-028c5db56960' 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>grid_mapping :</span></dt><dd>crs</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 49.96 MiB </td>\n",
       "                        <td> 501.56 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (102, 200, 321) </td>\n",
       "                        <td> (1, 200, 321) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 102 chunks in 215 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-782b75f5-56fc-4110-85cd-be28522178b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-782b75f5-56fc-4110-85cd-be28522178b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-866a7ab1-6ec7-4aa0-9418-4e2ceda281a5' class='xr-var-data-in' type='checkbox'><label for='data-866a7ab1-6ec7-4aa0-9418-4e2ceda281a5' 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>crs_wkt :</span></dt><dd>GEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],CS[ellipsoidal,2],AXIS[&quot;geodetic latitude (Lat)&quot;,north,ORDER[1],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],AXIS[&quot;geodetic longitude (Lon)&quot;,east,ORDER[2],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],USAGE[SCOPE[&quot;Horizontal component of 3D system.&quot;],AREA[&quot;World.&quot;],BBOX[-90,-180,90,180]],ID[&quot;EPSG&quot;,4326]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984 ensemble</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8c003d6b-20f9-4053-9b08-e9ef192cd35e' class='xr-section-summary-in' type='checkbox'  ><label for='section-8c003d6b-20f9-4053-9b08-e9ef192cd35e' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e3ad9be6-55b6-4619-8aa4-6e693addb0f7' class='xr-index-data-in' type='checkbox'/><label for='index-e3ad9be6-55b6-4619-8aa4-6e693addb0f7' 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(Float64Index([38.724998474121094,   38.7755012512207,  38.82600402832031,\n",
       "               38.87650680541992,  38.92700958251953,  38.97751235961914,\n",
       "               39.02801513671875,  39.07851791381836,   39.1290168762207,\n",
       "               39.17951965332031,\n",
       "              ...\n",
       "               48.32048034667969,  48.37097930908203, 48.421485900878906,\n",
       "               48.47198486328125,  48.52248764038086,  48.57299041748047,\n",
       "               48.62349319458008,  48.67399597167969,   48.7244987487793,\n",
       "              48.775001525878906],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=200))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-702f9f26-4157-4547-b605-d3c08c21585a' class='xr-index-data-in' type='checkbox'/><label for='index-702f9f26-4157-4547-b605-d3c08c21585a' 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(Float64Index([            26.375, 26.424999237060547, 26.475000381469727,\n",
       "              26.524999618530273, 26.575000762939453,             26.625,\n",
       "              26.674999237060547, 26.725000381469727, 26.774999618530273,\n",
       "              26.825000762939453,\n",
       "              ...\n",
       "               41.92499923706055, 41.974998474121094, 42.025001525878906,\n",
       "               42.07500076293945,             42.125,  42.17499923706055,\n",
       "              42.224998474121094, 42.275001525878906,  42.32500076293945,\n",
       "                          42.375],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=321))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-240110ab-f550-4422-9020-5b0612517a53' class='xr-index-data-in' type='checkbox'/><label for='index-240110ab-f550-4422-9020-5b0612517a53' 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;2015-12-31&#x27;, &#x27;2016-01-31&#x27;, &#x27;2016-02-29&#x27;, &#x27;2016-03-31&#x27;,\n",
       "               &#x27;2016-04-30&#x27;, &#x27;2016-05-31&#x27;, &#x27;2016-06-30&#x27;, &#x27;2016-07-31&#x27;,\n",
       "               &#x27;2016-08-31&#x27;, &#x27;2016-09-30&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-08-31&#x27;, &#x27;2023-09-30&#x27;, &#x27;2023-10-31&#x27;, &#x27;2023-11-30&#x27;,\n",
       "               &#x27;2023-12-31&#x27;, &#x27;2024-01-31&#x27;, &#x27;2024-02-29&#x27;, &#x27;2024-03-31&#x27;,\n",
       "               &#x27;2024-04-30&#x27;, &#x27;2024-05-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=102, freq=&#x27;M&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70cb4ee2-4789-4c1d-93cf-ac698e04c035' class='xr-section-summary-in' type='checkbox'  ><label for='section-70cb4ee2-4789-4c1d-93cf-ac698e04c035' class='xr-section-summary' >Attributes: <span>(52)</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-1.4</dd><dt><span>DSD_entry_id :</span></dt><dd>-GOS-L4HRfnd-BLK</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Scaling_Equation :</span></dt><dd>(scale_factor*data) + add_offset</dd><dt><span>acknowledgment :</span></dt><dd>Please acknowledge the use of these data with the following statement: Generated/provided by Copernicus Marine Service and CNR - ISMAR ROME. We would also appreciate being informed of any publications.</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>comment :</span></dt><dd>WARNING: some applications are unable to properly handle byte values. If Values &gt;127 are encounterd, please subtract 256</dd><dt><span>copernicusmarine_version :</span></dt><dd>1.0.0</dd><dt><span>creator_email :</span></dt><dd>gsdk@isac.cnr.it</dd><dt><span>creator_name :</span></dt><dd>ISMAR - Institute of Marine Sciences (CNR - ISMAR - GOS - Rome)</dd><dt><span>creator_url :</span></dt><dd>http://gosweb.artov.isac.cnr.it/</dd><dt><span>date_created :</span></dt><dd>20200610T223736Z</dd><dt><span>easternmost_longitude :</span></dt><dd>42.375</dd><dt><span>end_time :</span></dt><dd>20231231T190000Z</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>gds_version_id :</span></dt><dd>v2.0.5</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>GOS-CMEMS processor V4: new version</dd><dt><span>id :</span></dt><dd> </dd><dt><span>institution :</span></dt><dd>GOS</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>free registration at Copernicus Marine Service (http://marine.copernicus.eu/web/56-user-registration-form.php)</dd><dt><span>metadata_link :</span></dt><dd>Link to collection metadata record at archive</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>netcdf_version_id :</span></dt><dd>4.1.1, build date: JUN 18 2010&quot;</dd><dt><span>northernmost_latitude :</span></dt><dd>48.775001525878906</dd><dt><span>platform :</span></dt><dd>NOAA AVHRR, Metop-A AVHRR, (A)ATSR, Sentinel-3A SLSTR series of sensors</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_version :</span></dt><dd>3.0</dd><dt><span>project :</span></dt><dd>Copernicus Marine Environment Monitoring Service (CMEMS)</dd><dt><span>publisher_email :</span></dt><dd>servicedesk.cmems@mercator-ocean.eu, gsdk@isac.cnr.it</dd><dt><span>publisher_name :</span></dt><dd>CNR ISMAR GOS - CMEMS SST-TAC</dd><dt><span>publisher_url :</span></dt><dd>http://marine.copernicus.eu/</dd><dt><span>references :</span></dt><dd>A. Pisano, B. Buongiorno Nardelli, C. Tronconi, R. Santoleri: The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982-2012). /Remote Sensing of Environment./ 176 (2016) 107-116, doi:10.1016/j.rse.2016.01.019; http://pathfinder.nodc.noaa.gov and Casey, K.S., T.B. Brandon, P. Cornillon, and R. Evans: The Past, Present and Future of the AVHRR Pathfinder SST Program, in Oceanography from Space: Revisited, eds. V. Barale, J.F.R. Gower, and L. Alberotanza, Springer, 2010. DOI: 10.1007/978-90-481-8681-5_16.</dd><dt><span>sensor :</span></dt><dd>[1981-2016] NOAA AVHRR series of sensors (NOAA-07/09/11/12/14/15/16/17/18/19), ATSR1, ATSR2, (A)ATSR, MetopA AVHRR; [2017-2018]  NOAA AVHRR-19, MetopA AVHRR, SLSTR-3A</dd><dt><span>software_version :</span></dt><dd>Copernicus Marine Service HR L4 Processor V.2</dd><dt><span>source :</span></dt><dd>[1982-2016] ESA CCI SST v.2.0 L3C product (SST at 0.2m); [2017-2018] C3S v.2.0 L3C product (SST at 0.2m); [1982-2014]  Pathfinder-PFV5.3 L3C product (SST skin)</dd><dt><span>southernmost_latitude :</span></dt><dd>38.724998474121094</dd><dt><span>spatial_resolution :</span></dt><dd>0.05 degree</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>start_time :</span></dt><dd>20160101T070000Z</dd><dt><span>stop_time :</span></dt><dd>19810825T070000Z</dd><dt><span>summary :</span></dt><dd>[1982-2018] Daily gap-free maps (L4) at 0.05deg. x 0.05deg. horizontal resolution over the Black Sea. The maps are obtained using nighttime data extracted from ESA CCI SST v.2.0, C3S v.2.0 and PFV53 data</dd><dt><span>time_coverage_end :</span></dt><dd>&lt;xarray.DataArray &#x27;time&#x27; ()&gt;\n",
       "array(&#x27;2024-06-11T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2024-06-11\n",
       "Attributes:\n",
       "    valid_max:  2024-05-08T00:00:00.000000000\n",
       "    valid_min:  2016-01-01T00:00:00.000000000</dd><dt><span>time_coverage_start :</span></dt><dd>&lt;xarray.DataArray &#x27;time&#x27; ()&gt;\n",
       "array(&#x27;2016-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2016-01-01\n",
       "Attributes:\n",
       "    valid_max:  2024-05-08T00:00:00.000000000\n",
       "    valid_min:  2016-01-01T00:00:00.000000000</dd><dt><span>title :</span></dt><dd>CMEMS SST Black Sea</dd><dt><span>uuid :</span></dt><dd> </dd><dt><span>westernmost_longitude :</span></dt><dd>26.375</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (time: 102, latitude: 200, longitude: 321, bnds: 2)\n",
       "Coordinates:\n",
       "  * latitude           (latitude) float32 38.72 38.78 38.83 ... 48.72 48.78\n",
       "  * longitude          (longitude) float32 26.38 26.42 26.48 ... 42.33 42.38\n",
       "  * time               (time) datetime64[ns] 2015-12-31 ... 2024-05-31\n",
       "    longitude_bnds     (longitude, bnds) float64 26.35 26.4 26.4 ... 42.35 42.4\n",
       "    latitude_bnds      (latitude, bnds) float64 38.7 38.75 38.75 ... 48.75 48.8\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    analysed_sst_mean  (time, latitude, longitude) float32 dask.array<chunksize=(1, 200, 321), meta=np.ndarray>\n",
       "    other_sst          (time, latitude, longitude) float64 dask.array<chunksize=(1, 200, 321), meta=np.ndarray>\n",
       "    crs                int64 0\n",
       "Attributes: (12/52)\n",
       "    Conventions:                CF-1.4\n",
       "    DSD_entry_id:               -GOS-L4HRfnd-BLK\n",
       "    Metadata_Conventions:       Unidata Dataset Discovery v1.0\n",
       "    Scaling_Equation:           (scale_factor*data) + add_offset\n",
       "    acknowledgment:             Please acknowledge the use of these data with...\n",
       "    cdm_data_type:              grid\n",
       "    ...                         ...\n",
       "    summary:                    [1982-2018] Daily gap-free maps (L4) at 0.05d...\n",
       "    time_coverage_end:          <xarray.DataArray 'time' ()>\\narray('2024-06-...\n",
       "    time_coverage_start:        <xarray.DataArray 'time' ()>\\narray('2016-01-...\n",
       "    title:                      CMEMS SST Black Sea\n",
       "    uuid:                        \n",
       "    westernmost_longitude:      26.375"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_sst_data = xr.merge([cmems_sst_monthly_dataset, other_resampled_sst_data])\n",
    "combined_sst_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0183260-da4e-42dc-bd76-2d278ce31fe4",
   "metadata": {},
   "source": [
    "... and there we have it, a single dataset. Of course, now we would really like to compare the values from the different variables. One easy way to do this is to simply build the diff:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "530c6448-a8d8-4e91-92dd-6b314a1070f0",
   "metadata": {},
   "outputs": [
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       "  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:            (time: 102, latitude: 200, longitude: 321, bnds: 2)\n",
       "Coordinates:\n",
       "  * latitude           (latitude) float32 38.72 38.78 38.83 ... 48.72 48.78\n",
       "  * longitude          (longitude) float32 26.38 26.42 26.48 ... 42.33 42.38\n",
       "  * time               (time) datetime64[ns] 2015-12-31 ... 2024-05-31\n",
       "    longitude_bnds     (longitude, bnds) float64 26.35 26.4 26.4 ... 42.35 42.4\n",
       "    latitude_bnds      (latitude, bnds) float64 38.7 38.75 38.75 ... 48.75 48.8\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    analysed_sst_mean  (time, latitude, longitude) float32 dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;\n",
       "    other_sst          (time, latitude, longitude) float64 dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;\n",
       "    crs                int64 0\n",
       "    sst_diff           (time, latitude, longitude) float64 dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;\n",
       "Attributes: (12/52)\n",
       "    Conventions:                CF-1.4\n",
       "    DSD_entry_id:               -GOS-L4HRfnd-BLK\n",
       "    Metadata_Conventions:       Unidata Dataset Discovery v1.0\n",
       "    Scaling_Equation:           (scale_factor*data) + add_offset\n",
       "    acknowledgment:             Please acknowledge the use of these data with...\n",
       "    cdm_data_type:              grid\n",
       "    ...                         ...\n",
       "    summary:                    [1982-2018] Daily gap-free maps (L4) at 0.05d...\n",
       "    time_coverage_end:          &lt;xarray.DataArray &#x27;time&#x27; ()&gt;\\narray(&#x27;2024-06-...\n",
       "    time_coverage_start:        &lt;xarray.DataArray &#x27;time&#x27; ()&gt;\\narray(&#x27;2016-01-...\n",
       "    title:                      CMEMS SST Black Sea\n",
       "    uuid:                        \n",
       "    westernmost_longitude:      26.375</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-0bc01414-8831-4355-b645-8f9ff80fcfd1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0bc01414-8831-4355-b645-8f9ff80fcfd1' 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>: 102</li><li><span class='xr-has-index'>latitude</span>: 200</li><li><span class='xr-has-index'>longitude</span>: 321</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7605fa9f-7d4e-4bad-9c8b-4ac32313dc73' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7605fa9f-7d4e-4bad-9c8b-4ac32313dc73' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>38.72 38.78 38.83 ... 48.72 48.78</div><input id='attrs-e11cdbc5-1bc9-4121-bf06-8f9b5205be48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e11cdbc5-1bc9-4121-bf06-8f9b5205be48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f31a129-9b68-4f1a-9683-5f9d612d8c5d' class='xr-var-data-in' type='checkbox'><label for='data-0f31a129-9b68-4f1a-9683-5f9d612d8c5d' 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>Y</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>48.775001525878906</dd><dt><span>valid_min :</span></dt><dd>38.724998474121094</dd></dl></div><div class='xr-var-data'><pre>array([38.725   , 38.7755  , 38.826004, 38.876507, 38.92701 , 38.977512,\n",
       "       39.028015, 39.078518, 39.129017, 39.17952 , 39.230022, 39.280525,\n",
       "       39.331028, 39.38153 , 39.432034, 39.482536, 39.53304 , 39.58354 ,\n",
       "       39.634045, 39.684547, 39.73505 , 39.785553, 39.836056, 39.88656 ,\n",
       "       39.937057, 39.98756 , 40.038063, 40.088566, 40.13907 , 40.18957 ,\n",
       "       40.240074, 40.290577, 40.34108 , 40.391582, 40.442085, 40.492588,\n",
       "       40.54309 , 40.593594, 40.644096, 40.694595, 40.7451  , 40.7956  ,\n",
       "       40.846104, 40.896606, 40.94711 , 40.997612, 41.048115, 41.098618,\n",
       "       41.14912 , 41.199623, 41.250126, 41.30063 , 41.35113 , 41.401634,\n",
       "       41.452133, 41.502636, 41.55314 , 41.60364 , 41.654144, 41.704647,\n",
       "       41.75515 , 41.805653, 41.856155, 41.90666 , 41.95716 , 42.007664,\n",
       "       42.058167, 42.10867 , 42.159172, 42.20967 , 42.260174, 42.310677,\n",
       "       42.36118 , 42.411682, 42.462185, 42.512688, 42.56319 , 42.613693,\n",
       "       42.664196, 42.7147  , 42.7652  , 42.815704, 42.866207, 42.91671 ,\n",
       "       42.96721 , 43.017715, 43.068214, 43.118717, 43.16922 , 43.219723,\n",
       "       43.270226, 43.32073 , 43.37123 , 43.421734, 43.472237, 43.52274 ,\n",
       "       43.573242, 43.623745, 43.674248, 43.724747, 43.775253, 43.825752,\n",
       "       43.876255, 43.926758, 43.97726 , 44.027763, 44.078266, 44.12877 ,\n",
       "       44.17927 , 44.229774, 44.280277, 44.33078 , 44.381283, 44.431786,\n",
       "       44.482285, 44.53279 , 44.58329 , 44.633793, 44.684296, 44.7348  ,\n",
       "       44.7853  , 44.835804, 44.886307, 44.93681 , 44.987312, 45.037815,\n",
       "       45.088318, 45.13882 , 45.189323, 45.239822, 45.29033 , 45.340828,\n",
       "       45.39133 , 45.441833, 45.492336, 45.54284 , 45.59334 , 45.643845,\n",
       "       45.694347, 45.74485 , 45.795353, 45.845856, 45.89636 , 45.94686 ,\n",
       "       45.997364, 46.047867, 46.098366, 46.14887 , 46.19937 , 46.249874,\n",
       "       46.300377, 46.35088 , 46.401382, 46.451885, 46.502388, 46.55289 ,\n",
       "       46.603394, 46.653896, 46.7044  , 46.754898, 46.805405, 46.855904,\n",
       "       46.90641 , 46.95691 , 47.007412, 47.057915, 47.108418, 47.15892 ,\n",
       "       47.209423, 47.259926, 47.31043 , 47.36093 , 47.411434, 47.461937,\n",
       "       47.512436, 47.562943, 47.61344 , 47.663948, 47.714447, 47.76495 ,\n",
       "       47.815453, 47.865955, 47.91646 , 47.96696 , 48.017464, 48.067966,\n",
       "       48.11847 , 48.168972, 48.219475, 48.269978, 48.32048 , 48.37098 ,\n",
       "       48.421486, 48.471985, 48.522488, 48.57299 , 48.623493, 48.673996,\n",
       "       48.7245  , 48.775   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>26.38 26.42 26.48 ... 42.33 42.38</div><input id='attrs-05082f7d-ea0a-4d35-8fd3-15ba9d812318' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05082f7d-ea0a-4d35-8fd3-15ba9d812318' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d87832dd-6597-4b2d-b131-85ef3816a3c3' class='xr-var-data-in' type='checkbox'><label for='data-d87832dd-6597-4b2d-b131-85ef3816a3c3' 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>X</dd><dt><span>comment :</span></dt><dd>geographical coordinates, WGS84 projection</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>42.375</dd><dt><span>valid_min :</span></dt><dd>26.375</dd></dl></div><div class='xr-var-data'><pre>array([26.375, 26.425, 26.475, ..., 42.275, 42.325, 42.375], dtype=float32)</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'>2015-12-31 ... 2024-05-31</div><input id='attrs-a1139646-bdac-4bd9-9dab-9098aad04b54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1139646-bdac-4bd9-9dab-9098aad04b54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56be9c8d-cc69-41eb-b0e9-6f163c292d95' class='xr-var-data-in' type='checkbox'><label for='data-56be9c8d-cc69-41eb-b0e9-6f163c292d95' 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>valid_max :</span></dt><dd>2024-05-08T00:00:00.000000000</dd><dt><span>valid_min :</span></dt><dd>2016-01-01T00:00:00.000000000</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-12-31T00:00:00.000000000&#x27;, &#x27;2016-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-02-29T00:00:00.000000000&#x27;, &#x27;2016-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-04-30T00:00:00.000000000&#x27;, &#x27;2016-05-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-30T00:00:00.000000000&#x27;, &#x27;2016-07-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-08-31T00:00:00.000000000&#x27;, &#x27;2016-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-10-31T00:00:00.000000000&#x27;, &#x27;2016-11-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-12-31T00:00:00.000000000&#x27;, &#x27;2017-01-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-02-28T00:00:00.000000000&#x27;, &#x27;2017-03-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-04-30T00:00:00.000000000&#x27;, &#x27;2017-05-31T00:00:00.000000000&#x27;,\n",
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       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_bnds</span></div><div class='xr-var-dims'>(longitude, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>26.35 26.4 26.4 ... 42.35 42.4</div><input id='attrs-885e1525-a393-42f6-ae75-0838766361bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-885e1525-a393-42f6-ae75-0838766361bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-980c194a-381a-42ed-ab9b-0b65943e9ddb' class='xr-var-data-in' type='checkbox'><label for='data-980c194a-381a-42ed-ab9b-0b65943e9ddb' 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([[26.35000038, 26.39999962],\n",
       "       [26.40000038, 26.44999963],\n",
       "       [26.45000038, 26.49999963],\n",
       "       [26.50000037, 26.54999962],\n",
       "       [26.55000037, 26.59999962],\n",
       "       [26.60000038, 26.64999962],\n",
       "       [26.65000038, 26.69999963],\n",
       "       [26.70000038, 26.74999963],\n",
       "       [26.75000037, 26.79999962],\n",
       "       [26.80000037, 26.84999962],\n",
       "       [26.85000038, 26.89999962],\n",
       "       [26.90000038, 26.94999963],\n",
       "       [26.95000038, 26.99999963],\n",
       "       [27.00000037, 27.04999962],\n",
       "       [27.05000037, 27.09999962],\n",
       "       [27.10000038, 27.14999962],\n",
       "       [27.15000038, 27.19999963],\n",
       "       [27.20000038, 27.24999963],\n",
       "       [27.25000037, 27.29999962],\n",
       "       [27.30000037, 27.34999962],\n",
       "...\n",
       "       [41.40000038, 41.44999963],\n",
       "       [41.45000038, 41.49999963],\n",
       "       [41.50000037, 41.54999962],\n",
       "       [41.55000038, 41.59999963],\n",
       "       [41.60000038, 41.64999962],\n",
       "       [41.65000038, 41.69999963],\n",
       "       [41.70000038, 41.74999963],\n",
       "       [41.75000037, 41.79999962],\n",
       "       [41.80000038, 41.84999963],\n",
       "       [41.85000038, 41.89999962],\n",
       "       [41.90000038, 41.94999963],\n",
       "       [41.95000038, 41.99999963],\n",
       "       [42.00000037, 42.04999962],\n",
       "       [42.05000038, 42.09999963],\n",
       "       [42.10000038, 42.14999962],\n",
       "       [42.15000038, 42.19999963],\n",
       "       [42.20000038, 42.24999963],\n",
       "       [42.25000037, 42.29999962],\n",
       "       [42.30000038, 42.34999963],\n",
       "       [42.35000038, 42.39999962]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_bnds</span></div><div class='xr-var-dims'>(latitude, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>38.7 38.75 38.75 ... 48.75 48.8</div><input id='attrs-59cb89ec-d5b9-45fb-93da-722eee73f632' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59cb89ec-d5b9-45fb-93da-722eee73f632' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d26e5bd-77eb-469e-8894-2741eba9e47b' class='xr-var-data-in' type='checkbox'><label for='data-2d26e5bd-77eb-469e-8894-2741eba9e47b' 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([[38.6997471 , 38.75024985],\n",
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       "       [39.15426985, 39.2047726 ],\n",
       "       [39.20477238, 39.25527513],\n",
       "       [39.25527491, 39.30577766],\n",
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       "       [39.40678249, 39.45728524],\n",
       "       [39.45728502, 39.50778777],\n",
       "       [39.50778755, 39.5582903 ],\n",
       "       [39.55829007, 39.60879282],\n",
       "       [39.6087926 , 39.65929535],\n",
       "       [39.65929513, 39.70979788],\n",
       "...\n",
       "       [47.79020212, 47.84070487],\n",
       "       [47.84070465, 47.8912074 ],\n",
       "       [47.89120718, 47.94170993],\n",
       "       [47.9417097 , 47.99221245],\n",
       "       [47.99221223, 48.04271498],\n",
       "       [48.04271476, 48.09321751],\n",
       "       [48.09321729, 48.14372004],\n",
       "       [48.14371982, 48.19422257],\n",
       "       [48.19422234, 48.24472509],\n",
       "       [48.24472487, 48.29522762],\n",
       "       [48.2952274 , 48.34573015],\n",
       "       [48.34572993, 48.39623268],\n",
       "       [48.39623246, 48.44673521],\n",
       "       [48.44673498, 48.49723773],\n",
       "       [48.49723751, 48.54774026],\n",
       "       [48.54774004, 48.59824279],\n",
       "       [48.59824257, 48.64874532],\n",
       "       [48.6487451 , 48.69924785],\n",
       "       [48.69924762, 48.74975037],\n",
       "       [48.74975015, 48.8002529 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-887983ac-01f4-4254-b4e5-ced1706e3661' class='xr-section-summary-in' type='checkbox'  checked><label for='section-887983ac-01f4-4254-b4e5-ced1706e3661' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>analysed_sst_mean</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;</div><input id='attrs-dd44d96f-fdbc-47bb-bdd2-7d4b95f34f10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd44d96f-fdbc-47bb-bdd2-7d4b95f34f10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bb65a18-efca-420c-a619-973995c3ecb0' class='xr-var-data-in' type='checkbox'><label for='data-8bb65a18-efca-420c-a619-973995c3ecb0' 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>[1982-2018] Optimal interpolation (OI) SST measurements from ESA CCI SST v2.0, C3S v.2.0 and PFV53</dd><dt><span>long name :</span></dt><dd>Analysed Sea Surface Temperature</dd><dt><span>long_name :</span></dt><dd>Analysed Sea Surface Temperature</dd><dt><span>source :</span></dt><dd>[1982-2016] ESA CCI SST v.2.0 L3C product (SST at 0.2m); [2017-2018] C3S v.2.0 L3C product (SST at 0.2m); [1982-2014]Pathfinder-PFV5.3 L3C product (SST skin)</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>time_coverage_end :</span></dt><dd>20240508T190000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20160101T070000Z</dd><dt><span>type :</span></dt><dd>foundation</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_max :</span></dt><dd>4500</dd><dt><span>valid_min :</span></dt><dd>-300</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
       "                    \n",
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       "                        <td> 24.98 MiB </td>\n",
       "                        <td> 250.78 kiB </td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>other_sst</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;</div><input id='attrs-9fc17b67-5e05-4c37-99b7-b61ef77cb393' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fc17b67-5e05-4c37-99b7-b61ef77cb393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7aa42e79-3b5d-4ee8-be74-0a138ffae163' class='xr-var-data-in' type='checkbox'><label for='data-7aa42e79-3b5d-4ee8-be74-0a138ffae163' 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>grid_mapping :</span></dt><dd>crs</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> 49.96 MiB </td>\n",
       "                        <td> 501.56 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (102, 200, 321) </td>\n",
       "                        <td> (1, 200, 321) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 102 chunks in 215 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=\"205\" height=\"150\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "\n",
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       "\n",
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       "  <text x=\"95.725780\" y=\"120.492136\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >321</text>\n",
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       "  <text x=\"12.862890\" y=\"107.629245\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.862890,107.629245)\">102</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-d3b6de67-7505-4f13-a999-b5b5568f3aba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3b6de67-7505-4f13-a999-b5b5568f3aba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf4dda73-4822-4c0b-ab94-c1ccdabcb1c1' class='xr-var-data-in' type='checkbox'><label for='data-cf4dda73-4822-4c0b-ab94-c1ccdabcb1c1' 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>crs_wkt :</span></dt><dd>GEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],CS[ellipsoidal,2],AXIS[&quot;geodetic latitude (Lat)&quot;,north,ORDER[1],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],AXIS[&quot;geodetic longitude (Lon)&quot;,east,ORDER[2],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],USAGE[SCOPE[&quot;Horizontal component of 3D system.&quot;],AREA[&quot;World.&quot;],BBOX[-90,-180,90,180]],ID[&quot;EPSG&quot;,4326]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984 ensemble</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst_diff</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 200, 321), meta=np.ndarray&gt;</div><input id='attrs-210abecb-aceb-4150-81ec-56335298873b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-210abecb-aceb-4150-81ec-56335298873b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cd812fd-b6c9-4bd4-a513-81e43f2d13d5' class='xr-var-data-in' type='checkbox'><label for='data-9cd812fd-b6c9-4bd4-a513-81e43f2d13d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 49.96 MiB </td>\n",
       "                        <td> 501.56 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (102, 200, 321) </td>\n",
       "                        <td> (1, 200, 321) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 102 chunks in 830 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",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "  <text x=\"12.862890\" y=\"107.629245\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.862890,107.629245)\">102</text>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-093e91ac-1cc7-4d7e-82c0-08c14f1d2d53' class='xr-section-summary-in' type='checkbox'  ><label for='section-093e91ac-1cc7-4d7e-82c0-08c14f1d2d53' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0a825ee3-9c79-4ca5-adf3-f8ea179f2612' class='xr-index-data-in' type='checkbox'/><label for='index-0a825ee3-9c79-4ca5-adf3-f8ea179f2612' 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(Float64Index([38.724998474121094,   38.7755012512207,  38.82600402832031,\n",
       "               38.87650680541992,  38.92700958251953,  38.97751235961914,\n",
       "               39.02801513671875,  39.07851791381836,   39.1290168762207,\n",
       "               39.17951965332031,\n",
       "              ...\n",
       "               48.32048034667969,  48.37097930908203, 48.421485900878906,\n",
       "               48.47198486328125,  48.52248764038086,  48.57299041748047,\n",
       "               48.62349319458008,  48.67399597167969,   48.7244987487793,\n",
       "              48.775001525878906],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=200))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8d803126-5e98-4163-a743-d4a69c039b8d' class='xr-index-data-in' type='checkbox'/><label for='index-8d803126-5e98-4163-a743-d4a69c039b8d' 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(Float64Index([            26.375, 26.424999237060547, 26.475000381469727,\n",
       "              26.524999618530273, 26.575000762939453,             26.625,\n",
       "              26.674999237060547, 26.725000381469727, 26.774999618530273,\n",
       "              26.825000762939453,\n",
       "              ...\n",
       "               41.92499923706055, 41.974998474121094, 42.025001525878906,\n",
       "               42.07500076293945,             42.125,  42.17499923706055,\n",
       "              42.224998474121094, 42.275001525878906,  42.32500076293945,\n",
       "                          42.375],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=321))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0b0ffa99-d3d0-4664-9d34-24c7dc25c07b' class='xr-index-data-in' type='checkbox'/><label for='index-0b0ffa99-d3d0-4664-9d34-24c7dc25c07b' 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;2015-12-31&#x27;, &#x27;2016-01-31&#x27;, &#x27;2016-02-29&#x27;, &#x27;2016-03-31&#x27;,\n",
       "               &#x27;2016-04-30&#x27;, &#x27;2016-05-31&#x27;, &#x27;2016-06-30&#x27;, &#x27;2016-07-31&#x27;,\n",
       "               &#x27;2016-08-31&#x27;, &#x27;2016-09-30&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-08-31&#x27;, &#x27;2023-09-30&#x27;, &#x27;2023-10-31&#x27;, &#x27;2023-11-30&#x27;,\n",
       "               &#x27;2023-12-31&#x27;, &#x27;2024-01-31&#x27;, &#x27;2024-02-29&#x27;, &#x27;2024-03-31&#x27;,\n",
       "               &#x27;2024-04-30&#x27;, &#x27;2024-05-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=102, freq=&#x27;M&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-60330f19-c141-46ac-a57b-d1649900c778' class='xr-section-summary-in' type='checkbox'  ><label for='section-60330f19-c141-46ac-a57b-d1649900c778' class='xr-section-summary' >Attributes: <span>(52)</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-1.4</dd><dt><span>DSD_entry_id :</span></dt><dd>-GOS-L4HRfnd-BLK</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>Scaling_Equation :</span></dt><dd>(scale_factor*data) + add_offset</dd><dt><span>acknowledgment :</span></dt><dd>Please acknowledge the use of these data with the following statement: Generated/provided by Copernicus Marine Service and CNR - ISMAR ROME. We would also appreciate being informed of any publications.</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>comment :</span></dt><dd>WARNING: some applications are unable to properly handle byte values. If Values &gt;127 are encounterd, please subtract 256</dd><dt><span>copernicusmarine_version :</span></dt><dd>1.0.0</dd><dt><span>creator_email :</span></dt><dd>gsdk@isac.cnr.it</dd><dt><span>creator_name :</span></dt><dd>ISMAR - Institute of Marine Sciences (CNR - ISMAR - GOS - Rome)</dd><dt><span>creator_url :</span></dt><dd>http://gosweb.artov.isac.cnr.it/</dd><dt><span>date_created :</span></dt><dd>20200610T223736Z</dd><dt><span>easternmost_longitude :</span></dt><dd>42.375</dd><dt><span>end_time :</span></dt><dd>20231231T190000Z</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>gds_version_id :</span></dt><dd>v2.0.5</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.05000000074505806</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>GOS-CMEMS processor V4: new version</dd><dt><span>id :</span></dt><dd> </dd><dt><span>institution :</span></dt><dd>GOS</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>free registration at Copernicus Marine Service (http://marine.copernicus.eu/web/56-user-registration-form.php)</dd><dt><span>metadata_link :</span></dt><dd>Link to collection metadata record at archive</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>netcdf_version_id :</span></dt><dd>4.1.1, build date: JUN 18 2010&quot;</dd><dt><span>northernmost_latitude :</span></dt><dd>48.775001525878906</dd><dt><span>platform :</span></dt><dd>NOAA AVHRR, Metop-A AVHRR, (A)ATSR, Sentinel-3A SLSTR series of sensors</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_version :</span></dt><dd>3.0</dd><dt><span>project :</span></dt><dd>Copernicus Marine Environment Monitoring Service (CMEMS)</dd><dt><span>publisher_email :</span></dt><dd>servicedesk.cmems@mercator-ocean.eu, gsdk@isac.cnr.it</dd><dt><span>publisher_name :</span></dt><dd>CNR ISMAR GOS - CMEMS SST-TAC</dd><dt><span>publisher_url :</span></dt><dd>http://marine.copernicus.eu/</dd><dt><span>references :</span></dt><dd>A. Pisano, B. Buongiorno Nardelli, C. Tronconi, R. Santoleri: The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982-2012). /Remote Sensing of Environment./ 176 (2016) 107-116, doi:10.1016/j.rse.2016.01.019; http://pathfinder.nodc.noaa.gov and Casey, K.S., T.B. Brandon, P. Cornillon, and R. Evans: The Past, Present and Future of the AVHRR Pathfinder SST Program, in Oceanography from Space: Revisited, eds. V. Barale, J.F.R. Gower, and L. Alberotanza, Springer, 2010. DOI: 10.1007/978-90-481-8681-5_16.</dd><dt><span>sensor :</span></dt><dd>[1981-2016] NOAA AVHRR series of sensors (NOAA-07/09/11/12/14/15/16/17/18/19), ATSR1, ATSR2, (A)ATSR, MetopA AVHRR; [2017-2018]  NOAA AVHRR-19, MetopA AVHRR, SLSTR-3A</dd><dt><span>software_version :</span></dt><dd>Copernicus Marine Service HR L4 Processor V.2</dd><dt><span>source :</span></dt><dd>[1982-2016] ESA CCI SST v.2.0 L3C product (SST at 0.2m); [2017-2018] C3S v.2.0 L3C product (SST at 0.2m); [1982-2014]  Pathfinder-PFV5.3 L3C product (SST skin)</dd><dt><span>southernmost_latitude :</span></dt><dd>38.724998474121094</dd><dt><span>spatial_resolution :</span></dt><dd>0.05 degree</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>start_time :</span></dt><dd>20160101T070000Z</dd><dt><span>stop_time :</span></dt><dd>19810825T070000Z</dd><dt><span>summary :</span></dt><dd>[1982-2018] Daily gap-free maps (L4) at 0.05deg. x 0.05deg. horizontal resolution over the Black Sea. The maps are obtained using nighttime data extracted from ESA CCI SST v.2.0, C3S v.2.0 and PFV53 data</dd><dt><span>time_coverage_end :</span></dt><dd>&lt;xarray.DataArray &#x27;time&#x27; ()&gt;\n",
       "array(&#x27;2024-06-11T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2024-06-11\n",
       "Attributes:\n",
       "    valid_max:  2024-05-08T00:00:00.000000000\n",
       "    valid_min:  2016-01-01T00:00:00.000000000</dd><dt><span>time_coverage_start :</span></dt><dd>&lt;xarray.DataArray &#x27;time&#x27; ()&gt;\n",
       "array(&#x27;2016-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2016-01-01\n",
       "Attributes:\n",
       "    valid_max:  2024-05-08T00:00:00.000000000\n",
       "    valid_min:  2016-01-01T00:00:00.000000000</dd><dt><span>title :</span></dt><dd>CMEMS SST Black Sea</dd><dt><span>uuid :</span></dt><dd> </dd><dt><span>westernmost_longitude :</span></dt><dd>26.375</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (time: 102, latitude: 200, longitude: 321, bnds: 2)\n",
       "Coordinates:\n",
       "  * latitude           (latitude) float32 38.72 38.78 38.83 ... 48.72 48.78\n",
       "  * longitude          (longitude) float32 26.38 26.42 26.48 ... 42.33 42.38\n",
       "  * time               (time) datetime64[ns] 2015-12-31 ... 2024-05-31\n",
       "    longitude_bnds     (longitude, bnds) float64 26.35 26.4 26.4 ... 42.35 42.4\n",
       "    latitude_bnds      (latitude, bnds) float64 38.7 38.75 38.75 ... 48.75 48.8\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    analysed_sst_mean  (time, latitude, longitude) float32 dask.array<chunksize=(1, 200, 321), meta=np.ndarray>\n",
       "    other_sst          (time, latitude, longitude) float64 dask.array<chunksize=(1, 200, 321), meta=np.ndarray>\n",
       "    crs                int64 0\n",
       "    sst_diff           (time, latitude, longitude) float64 dask.array<chunksize=(1, 200, 321), meta=np.ndarray>\n",
       "Attributes: (12/52)\n",
       "    Conventions:                CF-1.4\n",
       "    DSD_entry_id:               -GOS-L4HRfnd-BLK\n",
       "    Metadata_Conventions:       Unidata Dataset Discovery v1.0\n",
       "    Scaling_Equation:           (scale_factor*data) + add_offset\n",
       "    acknowledgment:             Please acknowledge the use of these data with...\n",
       "    cdm_data_type:              grid\n",
       "    ...                         ...\n",
       "    summary:                    [1982-2018] Daily gap-free maps (L4) at 0.05d...\n",
       "    time_coverage_end:          <xarray.DataArray 'time' ()>\\narray('2024-06-...\n",
       "    time_coverage_start:        <xarray.DataArray 'time' ()>\\narray('2016-01-...\n",
       "    title:                      CMEMS SST Black Sea\n",
       "    uuid:                        \n",
       "    westernmost_longitude:      26.375"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst_diff = combined_sst_data.analysed_sst_mean - combined_sst_data.other_sst\n",
    "combined_sst_data = combined_sst_data.assign({\"sst_diff\": sst_diff})\n",
    "combined_sst_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd89d177-6647-41fc-860d-7678be5f0285",
   "metadata": {},
   "source": [
    "And we can have a look at the resulting diffs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c601949a-ca64-48d2-9c11-c368fe2198fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/dask/core.py:121: RuntimeWarning: invalid value encountered in divide\n",
      "  return func(*(_execute_task(a, cache) for a in args))\n",
      "/opt/conda/lib/python3.11/site-packages/dask/array/chunk.py:138: RuntimeWarning: Mean of empty slice\n",
      "  return reduction(x.reshape(newshape), axis=tuple(range(1, x.ndim * 2, 2)), **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7fad90c9d710>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_sst_data[\"sst_diff\"].isel(time=slice(0, 102, 10)).plot.imshow(col=\"time\", col_wrap=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e35af9e-7023-4965-8075-848e61361d6f",
   "metadata": {},
   "source": [
    "Now, what about finding anomalies in the temperature data? For this, we need to compute the mean and then we can compute the diff for each month. This can be achieved like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aeeb8d7b-b262-4a36-a2ee-0184f5309d62",
   "metadata": {},
   "outputs": [],
   "source": [
    "months = [\"december\", \"january\", \"february\", \"march\", \"april\", \"may\", \"june\", \"july\", \"august\", \"september\", \"october\", \"november\"]\n",
    "\n",
    "monthly_sst = {}\n",
    "monthly_sst_means = {}\n",
    "monthly_sst_anomalies = {}\n",
    "for i, month in enumerate(months):\n",
    "    monthly_sst[month] = combined_sst_data.analysed_sst_mean[i::12]\n",
    "    monthly_sst_means[month] = monthly_sst[month].mean(dim=\"time\")\n",
    "    monthly_sst_anomalies[month] = monthly_sst[month] - monthly_sst_means[month]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11a3bd4b-4ebe-46f9-8c1d-eede845d12ad",
   "metadata": {},
   "source": [
    "Note that the outcome is not a dataset but a dictionary. We may now look at a monthly mean like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "127a256e-40e3-4bca-84cf-2676f1a6d57d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fad75977650>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_sst_means[\"april\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ea2553b-a5ce-4ebb-838e-0f17d05f7263",
   "metadata": {},
   "source": [
    "To see the anomalies for each month, we can come up with an auxiliary function that plots the annual anomalies for each month:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1ceeb8f-d93c-4dc8-9431-533bc5393cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_monthly_anomalies(month: str):\n",
    "    monthly_sst_anomalies[month].plot.imshow(col=\"time\", col_wrap=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4c109b18-7572-44f3-a192-ecb1a7d1aae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x900 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_monthly_anomalies(\"january\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}