From d81f2649dd88397f6eeac2917e9df33238fe8fe7 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Thu, 29 Jun 2023 21:44:04 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                                     |   2 +-
 .../OISSS_L4_multimission_monthly_v1.html     |   2 +-
 ...-2438e8b8-d128-41b5-b06d-53fb2efee43a.png} | Bin
 ...-419d3300-7fdc-404c-afae-9e97155fb347.png} | Bin
 ...nload_AmazonRiver_Estuary_Exploration.html |  15 +-
 search.json                                   |   2 +-
 sitemap.xml                                   | 164 +++++++++---------
 7 files changed, 92 insertions(+), 93 deletions(-)
 rename notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/{2a8c142a-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png => 5df008cc-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png} (100%)
 rename notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/{2a8c142a-2-419d3300-7fdc-404c-afae-9e97155fb347.png => 5df008cc-2-419d3300-7fdc-404c-afae-9e97155fb347.png} (100%)

diff --git a/.nojekyll b/.nojekyll
index 876ee1ed..58e053f0 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
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diff --git a/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html b/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html
index 9e19c0ae..6334d54d 100644
--- a/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html
+++ b/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html
@@ -731,7 +731,7 @@ <h1 class="title">Direct S3 Data Access tutorial (Multi-Mission Optimally Interp
 </header>
 
 <p>This tutorial only works in a jupyterhub hosted at AWS US-WEST-2.</p>
-<p><img src="OISSS_L4_multimission_monthly_v1_files/figure-html/2a8c142a-2-419d3300-7fdc-404c-afae-9e97155fb347.png" class="img-fluid" alt="image.png"><img src="OISSS_L4_multimission_monthly_v1_files/figure-html/2a8c142a-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png" class="img-fluid" alt="image.png"></p>
+<p><img src="OISSS_L4_multimission_monthly_v1_files/figure-html/5df008cc-2-419d3300-7fdc-404c-afae-9e97155fb347.png" class="img-fluid" alt="image.png"><img src="OISSS_L4_multimission_monthly_v1_files/figure-html/5df008cc-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png" class="img-fluid" alt="image.png"></p>
 <ul>
 <li><strong>User guide</strong>: http://iprc.soest.hawaii.edu/users/oleg/oisss/GLB/OISSS_Product_Notes.pdf</li>
 <li><strong>DOI</strong> <a href="https://podaac.jpl.nasa.gov/dataset/OISSS_L4_multimission_monthly_v1?ids=&amp;values=&amp;search=oisss&amp;provider=POCLOUD">10.5067/SMP10-4UMCS</a></li>
diff --git a/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/2a8c142a-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png b/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/5df008cc-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png
similarity index 100%
rename from notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/2a8c142a-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png
rename to notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/5df008cc-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png
diff --git a/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/2a8c142a-2-419d3300-7fdc-404c-afae-9e97155fb347.png b/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/5df008cc-2-419d3300-7fdc-404c-afae-9e97155fb347.png
similarity index 100%
rename from notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/2a8c142a-2-419d3300-7fdc-404c-afae-9e97155fb347.png
rename to notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/5df008cc-2-419d3300-7fdc-404c-afae-9e97155fb347.png
diff --git a/notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html b/notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html
index e00653bb..e5860a65 100644
--- a/notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html
+++ b/notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html
@@ -4357,14 +4357,13 @@ <h2 class="anchored" data-anchor-id="sea-surface-salinity-multi-mission-smap-aqu
 <span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">for</span> pth, dirs, files <span class="kw">in</span> os.walk(<span class="st">"./sss_data"</span>) <span class="cf">for</span> f <span class="kw">in</span> files]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div class="cell" data-execution_count="14">
-<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="co">#open dataset but doesn't work for either method</span></span>
-<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>ds_sss <span class="op">=</span> xr.open_mfdataset(sss_path,</span>
-<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>                           combine<span class="op">=</span><span class="st">'by_coords'</span>,</span>
-<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a>                           mask_and_scale<span class="op">=</span><span class="va">True</span>,</span>
-<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a>                           decode_cf<span class="op">=</span><span class="va">True</span>,</span>
-<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a>                           chunks<span class="op">=</span><span class="st">'auto'</span>,</span>
-<span id="cb26-7"><a href="#cb26-7" aria-hidden="true" tabindex="-1"></a>                           engine<span class="op">=</span><span class="st">'h5netcdf'</span>)</span>
-<span id="cb26-8"><a href="#cb26-8" aria-hidden="true" tabindex="-1"></a>ds_sss</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>ds_sss <span class="op">=</span> xr.open_mfdataset(sss_path,</span>
+<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>                           combine<span class="op">=</span><span class="st">'by_coords'</span>,</span>
+<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>                           mask_and_scale<span class="op">=</span><span class="va">True</span>,</span>
+<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a>                           decode_cf<span class="op">=</span><span class="va">True</span>,</span>
+<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a>                           chunks<span class="op">=</span><span class="st">'auto'</span>,</span>
+<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a>                           engine<span class="op">=</span><span class="st">'h5netcdf'</span>)</span>
+<span id="cb26-7"><a href="#cb26-7" aria-hidden="true" tabindex="-1"></a>ds_sss</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="14">
 
 <div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
diff --git a/search.json b/search.json
index 829c935f..5e8f5f1b 100644
--- a/search.json
+++ b/search.json
@@ -2111,7 +2111,7 @@
     "href": "notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html#sea-surface-salinity-multi-mission-smap-aquarius-smos",
     "title": "Amazon Estuary Exploration:",
     "section": "Sea Surface Salinity (Multi-mission: SMAP, Aquarius, SMOS)",
-    "text": "Sea Surface Salinity (Multi-mission: SMAP, Aquarius, SMOS)\nThe shortname for this dataset is ‘OISSS_L4_multimission_7day_v1’. This dataset contains hundreds of granules, by using earthaccess search, we access 998 granules.\nSince this dataset has more than 1 granule that we want to open for visualization, we have to establish the full file path in a different way. For the previous datasets, we could list the exact file, but that would be difficult to do with hundreds of granules. Therefore, the extra step to recurse through the directory to access all files.\n\n#earthaccess search\nsss_results = earthaccess.search_data(short_name=\"OISSS_L4_multimission_7day_v1\")\n\nGranules found: 998\n\n\n\n#earthaccess download\nsss_files = earthaccess.download(sss_results, \"./sss_data\")\n\n Getting 998 granules, approx download size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n\n#ensures that all files are included in the path\nsss_path = [os.path.join(\"./sss_data\", f) \n    for pth, dirs, files in os.walk(\"./sss_data\") for f in files]\n\n\n#open dataset but doesn't work for either method\nds_sss = xr.open_mfdataset(sss_path,\n                           combine='by_coords',\n                           mask_and_scale=True,\n                           decode_cf=True,\n                           chunks='auto',\n                           engine='h5netcdf')\nds_sss\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n&lt;xarray.Dataset&gt;\nDimensions:                    (longitude: 1440, latitude: 720, time: 998)\nCoordinates:\n  * longitude                  (longitude) float32 -179.9 -179.6 ... 179.6 179.9\n  * latitude                   (latitude) float32 -89.88 -89.62 ... 89.62 89.88\n  * time                       (time) datetime64[ns] 2011-08-28 ... 2022-08-02\nData variables:\n    sss                        (latitude, longitude, time) float32 dask.array&lt;chunksize=(720, 1440, 1), meta=np.ndarray&gt;\n    sss_empirical_uncertainty  (latitude, longitude, time) float32 dask.array&lt;chunksize=(720, 1440, 962), meta=np.ndarray&gt;\n    sss_uncertainty            (latitude, longitude, time) float32 dask.array&lt;chunksize=(720, 1440, 1), meta=np.ndarray&gt;\nAttributes: (12/42)\n    Conventions:                   CF-1.8, ACDD-1.3\n    standard_name_vocabulary:      CF Standard Name Table v27\n    Title:                         Multi-Mission Optimally Interpolated Sea S...\n    Short_Name:                    OISSS_L4_multimission_7d_v1\n    Version:                       V1.0\n    Processing_Level:              Level 4\n    ...                            ...\n    geospatial_lat_resolution:     0.25\n    geospatial_lat_units:          degrees_north\n    geospatial_lon_min:            -180.0\n    geospatial_lon_max:            180.0\n    geospatial_lon_resolution:     0.25\n    geospatial_lon_units:          degrees_eastxarray.DatasetDimensions:longitude: 1440latitude: 720time: 998Coordinates: (3)longitude(longitude)float32-179.9 -179.6 ... 179.6 179.9long_name :longitudestandard_name :longitudeunits :degrees_eastaxis :Xvalid_min :-180.0valid_max :180.0coverage_content_type :coordinatearray([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n      dtype=float32)latitude(latitude)float32-89.88 -89.62 ... 89.62 89.88long_name :latitudestandard_name :latitudeunits :degrees_northaxis :Yvalid_min :-90.0valid_max :90.0coverage_content_type :coordinatearray([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n      dtype=float32)time(time)datetime64[ns]2011-08-28 ... 2022-08-02long_name :center day of a time period over which satellite Level 2 SSS data have been collected for OISSS analysisstandard_name :timeaxis :Tcoverage_content_type :coordinatearray(['2011-08-28T00:00:00.000000000', '2011-09-01T00:00:00.000000000',\n       '2011-09-05T00:00:00.000000000', ..., '2022-07-25T00:00:00.000000000',\n       '2022-07-29T00:00:00.000000000', '2022-08-02T00:00:00.000000000'],\n      dtype='datetime64[ns]')Data variables: (3)sss(latitude, longitude, time)float32dask.array&lt;chunksize=(720, 1440, 1), meta=np.ndarray&gt;long_name :sea surface salinitystandard_name :sea_surface_salinityunits :1e-3valid_min :0.0valid_max :45.0add_factor :0.0coverage_content_type :physicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n150.29 MiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 38)\n\n\nCount\n13461 Tasks\n961 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nsss_empirical_uncertainty\n\n\n(latitude, longitude, time)\n\n\nfloat32\n\n\ndask.array&lt;chunksize=(720, 1440, 962), meta=np.ndarray&gt;\n\n\n\n\nlong_name :\n\nestimated empirical uncertainty of multi-mission OISSS\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\nadd_factor :\n\n0.0\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n3.72 GiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 962)\n\n\nCount\n338 Tasks\n37 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_uncertainty\n\n\n(latitude, longitude, time)\n\n\nfloat32\n\n\ndask.array&lt;chunksize=(720, 1440, 1), meta=np.ndarray&gt;\n\n\n\n\nlong_name :\n\nestimated empirical uncertainty of multi-mission OISSS\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\nadd_factor :\n\n0.0\n\ncoordinates :\n\ntime longitude latudude\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n150.29 MiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 38)\n\n\nCount\n8654 Tasks\n961 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nIndexes: (3)longitudePandasIndexPandasIndex(Float64Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625,\n              -178.375, -178.125, -177.875, -177.625,\n              ...\n               177.625,  177.875,  178.125,  178.375,  178.625,  178.875,\n               179.125,  179.375,  179.625,  179.875],\n             dtype='float64', name='longitude', length=1440))latitudePandasIndexPandasIndex(Float64Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375,\n              -88.125, -87.875, -87.625,\n              ...\n               87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,\n               89.375,  89.625,  89.875],\n             dtype='float64', name='latitude', length=720))timePandasIndexPandasIndex(DatetimeIndex(['2011-08-28', '2011-09-01', '2011-09-05', '2011-09-09',\n               '2011-09-13', '2011-09-17', '2011-09-21', '2011-09-25',\n               '2011-09-29', '2011-10-03',\n               ...\n               '2022-06-27', '2022-07-01', '2022-07-05', '2022-07-09',\n               '2022-07-13', '2022-07-17', '2022-07-21', '2022-07-25',\n               '2022-07-29', '2022-08-02'],\n              dtype='datetime64[ns]', name='time', length=998, freq=None))Attributes: (42)Conventions :CF-1.8, ACDD-1.3standard_name_vocabulary :CF Standard Name Table v27Title :Multi-Mission Optimally Interpolated Sea Surface Salinity 7-Day Global Dataset V1.0Short_Name :OISSS_L4_multimission_7d_v1Version :V1.0Processing_Level :Level 4source :Aquarius V5.0 Level 2 SSS; SMAP RSS V4.0 Level 2 SSS_40km; SMOS Level 2 SSS L2OS version 662sourse_of_input_Aquarius_SSS :Aquarius Official Release Level 2 Sea Surface Salinity & Wind Speed Cal Data V5.0. Distributed by PO.DAAC at https://podaac.jpl.nasa.gov/dataset/AQUARIUS_L2_SSS_CAL_V5sourse_of_input_SMAP_SSS :Meissner, T., F. Wentz, A. Manaster, R. Lindsley, 2019. Remote Sensing Systems SMAP L2C Sea Surface Salinity, Version 4.0 Validated Release, Remote Sensing Systems, Santa Rosa, CA, USA, Available online at www.remss.com/missions/smap.sourse_of_input_SMOS_SSS :ESA SMOS online dissemination service at https://smos-diss.eo.esa.int/oads/accessplatform :Aquarius/SAC-D, SMAP, SMOSinstrument :Aquarius radiometer, SMAP radiometer, SMOS MIRASCreation_Date :2023-01-16T04:04:41ZCreator_Name :Oleg MelnichenkoCreator_Email :oleg@hawaii.eduCreator_URL :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLBProject :NASA Ocean SalinityKeywords :Sea Surface Salinity, SSS, Aquarius, SMAP, Optimum Interpolation, OISSSKeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science KeywordsInstitution :IPRC/SOEST, University of Hawaii, Honolulu, HI; Remote Sensing Systems (RSS), Santa Rosa, CAPublisher_Name :Oleg Melnichenko, Peter Hacker, James Potemra, Thomas Meissner, Frank WentzPublisher_Email :oleg@hawaii.edu.orgPublisher_URL :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLBDataset_Citation_Authors :Oleg Melnichenko, Peter Hacker, James Potemra, Thomas Meissner, Frank WentzDataset_Citation_Year :2021Dataset_Citation_Product :Aquarius/SMAP Sea Surface Salinity Optimum Interpolation AnalysisTechnical_Notes :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLB/OISSS_Product_Notes.pdfyear_of_observation :2022month_of_observation :3day_of_observation :11time_coverage_start :2022-03-07T12:00:00Ztime_coverage_end :2022-03-15T12:00:00Ztime_coverage_resolution :P7Dcdm_data_type :gridgeospatial_lat_min :-90.0geospatial_lat_max :90.0geospatial_lat_resolution :0.25geospatial_lat_units :degrees_northgeospatial_lon_min :-180.0geospatial_lon_max :180.0geospatial_lon_resolution :0.25geospatial_lon_units :degrees_east\n\n\n\nPlot a subset of the data\nUse the function xarray.DataSet.sel to select a subset of the data at the outlet of the Amazon to plot at time t=0 (August 28, 2011) with hvplot.\n\nlat_bnds, lon_bnds = [-2, 6], [-52, -44] \nds_sss_subset = ds_sss.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))\nds_sss_subset\n\nds_sss_subset.sss[:,:,0].hvplot()"
+    "text": "Sea Surface Salinity (Multi-mission: SMAP, Aquarius, SMOS)\nThe shortname for this dataset is ‘OISSS_L4_multimission_7day_v1’. This dataset contains hundreds of granules, by using earthaccess search, we access 998 granules.\nSince this dataset has more than 1 granule that we want to open for visualization, we have to establish the full file path in a different way. For the previous datasets, we could list the exact file, but that would be difficult to do with hundreds of granules. Therefore, the extra step to recurse through the directory to access all files.\n\n#earthaccess search\nsss_results = earthaccess.search_data(short_name=\"OISSS_L4_multimission_7day_v1\")\n\nGranules found: 998\n\n\n\n#earthaccess download\nsss_files = earthaccess.download(sss_results, \"./sss_data\")\n\n Getting 998 granules, approx download size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n\n#ensures that all files are included in the path\nsss_path = [os.path.join(\"./sss_data\", f) \n    for pth, dirs, files in os.walk(\"./sss_data\") for f in files]\n\n\nds_sss = xr.open_mfdataset(sss_path,\n                           combine='by_coords',\n                           mask_and_scale=True,\n                           decode_cf=True,\n                           chunks='auto',\n                           engine='h5netcdf')\nds_sss\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n&lt;xarray.Dataset&gt;\nDimensions:                    (longitude: 1440, latitude: 720, time: 998)\nCoordinates:\n  * longitude                  (longitude) float32 -179.9 -179.6 ... 179.6 179.9\n  * latitude                   (latitude) float32 -89.88 -89.62 ... 89.62 89.88\n  * time                       (time) datetime64[ns] 2011-08-28 ... 2022-08-02\nData variables:\n    sss                        (latitude, longitude, time) float32 dask.array&lt;chunksize=(720, 1440, 1), meta=np.ndarray&gt;\n    sss_empirical_uncertainty  (latitude, longitude, time) float32 dask.array&lt;chunksize=(720, 1440, 962), meta=np.ndarray&gt;\n    sss_uncertainty            (latitude, longitude, time) float32 dask.array&lt;chunksize=(720, 1440, 1), meta=np.ndarray&gt;\nAttributes: (12/42)\n    Conventions:                   CF-1.8, ACDD-1.3\n    standard_name_vocabulary:      CF Standard Name Table v27\n    Title:                         Multi-Mission Optimally Interpolated Sea S...\n    Short_Name:                    OISSS_L4_multimission_7d_v1\n    Version:                       V1.0\n    Processing_Level:              Level 4\n    ...                            ...\n    geospatial_lat_resolution:     0.25\n    geospatial_lat_units:          degrees_north\n    geospatial_lon_min:            -180.0\n    geospatial_lon_max:            180.0\n    geospatial_lon_resolution:     0.25\n    geospatial_lon_units:          degrees_eastxarray.DatasetDimensions:longitude: 1440latitude: 720time: 998Coordinates: (3)longitude(longitude)float32-179.9 -179.6 ... 179.6 179.9long_name :longitudestandard_name :longitudeunits :degrees_eastaxis :Xvalid_min :-180.0valid_max :180.0coverage_content_type :coordinatearray([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n      dtype=float32)latitude(latitude)float32-89.88 -89.62 ... 89.62 89.88long_name :latitudestandard_name :latitudeunits :degrees_northaxis :Yvalid_min :-90.0valid_max :90.0coverage_content_type :coordinatearray([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n      dtype=float32)time(time)datetime64[ns]2011-08-28 ... 2022-08-02long_name :center day of a time period over which satellite Level 2 SSS data have been collected for OISSS analysisstandard_name :timeaxis :Tcoverage_content_type :coordinatearray(['2011-08-28T00:00:00.000000000', '2011-09-01T00:00:00.000000000',\n       '2011-09-05T00:00:00.000000000', ..., '2022-07-25T00:00:00.000000000',\n       '2022-07-29T00:00:00.000000000', '2022-08-02T00:00:00.000000000'],\n      dtype='datetime64[ns]')Data variables: (3)sss(latitude, longitude, time)float32dask.array&lt;chunksize=(720, 1440, 1), meta=np.ndarray&gt;long_name :sea surface salinitystandard_name :sea_surface_salinityunits :1e-3valid_min :0.0valid_max :45.0add_factor :0.0coverage_content_type :physicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n150.29 MiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 38)\n\n\nCount\n13461 Tasks\n961 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nsss_empirical_uncertainty\n\n\n(latitude, longitude, time)\n\n\nfloat32\n\n\ndask.array&lt;chunksize=(720, 1440, 962), meta=np.ndarray&gt;\n\n\n\n\nlong_name :\n\nestimated empirical uncertainty of multi-mission OISSS\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\nadd_factor :\n\n0.0\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n3.72 GiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 962)\n\n\nCount\n338 Tasks\n37 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_uncertainty\n\n\n(latitude, longitude, time)\n\n\nfloat32\n\n\ndask.array&lt;chunksize=(720, 1440, 1), meta=np.ndarray&gt;\n\n\n\n\nlong_name :\n\nestimated empirical uncertainty of multi-mission OISSS\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\nadd_factor :\n\n0.0\n\ncoordinates :\n\ntime longitude latudude\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n150.29 MiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 38)\n\n\nCount\n8654 Tasks\n961 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nIndexes: (3)longitudePandasIndexPandasIndex(Float64Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625,\n              -178.375, -178.125, -177.875, -177.625,\n              ...\n               177.625,  177.875,  178.125,  178.375,  178.625,  178.875,\n               179.125,  179.375,  179.625,  179.875],\n             dtype='float64', name='longitude', length=1440))latitudePandasIndexPandasIndex(Float64Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375,\n              -88.125, -87.875, -87.625,\n              ...\n               87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,\n               89.375,  89.625,  89.875],\n             dtype='float64', name='latitude', length=720))timePandasIndexPandasIndex(DatetimeIndex(['2011-08-28', '2011-09-01', '2011-09-05', '2011-09-09',\n               '2011-09-13', '2011-09-17', '2011-09-21', '2011-09-25',\n               '2011-09-29', '2011-10-03',\n               ...\n               '2022-06-27', '2022-07-01', '2022-07-05', '2022-07-09',\n               '2022-07-13', '2022-07-17', '2022-07-21', '2022-07-25',\n               '2022-07-29', '2022-08-02'],\n              dtype='datetime64[ns]', name='time', length=998, freq=None))Attributes: (42)Conventions :CF-1.8, ACDD-1.3standard_name_vocabulary :CF Standard Name Table v27Title :Multi-Mission Optimally Interpolated Sea Surface Salinity 7-Day Global Dataset V1.0Short_Name :OISSS_L4_multimission_7d_v1Version :V1.0Processing_Level :Level 4source :Aquarius V5.0 Level 2 SSS; SMAP RSS V4.0 Level 2 SSS_40km; SMOS Level 2 SSS L2OS version 662sourse_of_input_Aquarius_SSS :Aquarius Official Release Level 2 Sea Surface Salinity & Wind Speed Cal Data V5.0. Distributed by PO.DAAC at https://podaac.jpl.nasa.gov/dataset/AQUARIUS_L2_SSS_CAL_V5sourse_of_input_SMAP_SSS :Meissner, T., F. Wentz, A. Manaster, R. Lindsley, 2019. Remote Sensing Systems SMAP L2C Sea Surface Salinity, Version 4.0 Validated Release, Remote Sensing Systems, Santa Rosa, CA, USA, Available online at www.remss.com/missions/smap.sourse_of_input_SMOS_SSS :ESA SMOS online dissemination service at https://smos-diss.eo.esa.int/oads/accessplatform :Aquarius/SAC-D, SMAP, SMOSinstrument :Aquarius radiometer, SMAP radiometer, SMOS MIRASCreation_Date :2023-01-16T04:04:41ZCreator_Name :Oleg MelnichenkoCreator_Email :oleg@hawaii.eduCreator_URL :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLBProject :NASA Ocean SalinityKeywords :Sea Surface Salinity, SSS, Aquarius, SMAP, Optimum Interpolation, OISSSKeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science KeywordsInstitution :IPRC/SOEST, University of Hawaii, Honolulu, HI; Remote Sensing Systems (RSS), Santa Rosa, CAPublisher_Name :Oleg Melnichenko, Peter Hacker, James Potemra, Thomas Meissner, Frank WentzPublisher_Email :oleg@hawaii.edu.orgPublisher_URL :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLBDataset_Citation_Authors :Oleg Melnichenko, Peter Hacker, James Potemra, Thomas Meissner, Frank WentzDataset_Citation_Year :2021Dataset_Citation_Product :Aquarius/SMAP Sea Surface Salinity Optimum Interpolation AnalysisTechnical_Notes :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLB/OISSS_Product_Notes.pdfyear_of_observation :2022month_of_observation :3day_of_observation :11time_coverage_start :2022-03-07T12:00:00Ztime_coverage_end :2022-03-15T12:00:00Ztime_coverage_resolution :P7Dcdm_data_type :gridgeospatial_lat_min :-90.0geospatial_lat_max :90.0geospatial_lat_resolution :0.25geospatial_lat_units :degrees_northgeospatial_lon_min :-180.0geospatial_lon_max :180.0geospatial_lon_resolution :0.25geospatial_lon_units :degrees_east\n\n\n\nPlot a subset of the data\nUse the function xarray.DataSet.sel to select a subset of the data at the outlet of the Amazon to plot at time t=0 (August 28, 2011) with hvplot.\n\nlat_bnds, lon_bnds = [-2, 6], [-52, -44] \nds_sss_subset = ds_sss.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))\nds_sss_subset\n\nds_sss_subset.sss[:,:,0].hvplot()"
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index 96bcfab2..b2c7347b 100644
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