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<xarray.Dataset>\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<chunksize=(720, 1440, 1), meta=np.ndarray>\n sss_empirical_uncertainty (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 962), meta=np.ndarray>\n sss_uncertainty (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 1), meta=np.ndarray>\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<chunksize=(720, 1440, 1), meta=np.ndarray>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<chunksize=(720, 1440, 962), meta=np.ndarray>\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<chunksize=(720, 1440, 1), meta=np.ndarray>\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<xarray.Dataset>\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<chunksize=(720, 1440, 1), meta=np.ndarray>\n sss_empirical_uncertainty (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 962), meta=np.ndarray>\n sss_uncertainty (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 1), meta=np.ndarray>\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<chunksize=(720, 1440, 1), meta=np.ndarray>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<chunksize=(720, 1440, 962), meta=np.ndarray>\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<chunksize=(720, 1440, 1), meta=np.ndarray>\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()"
},
{
"objectID": "notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html#sea-surface-temperature-modis",
diff --git a/sitemap.xml b/sitemap.xml
index 96bcfab2..b2c7347b 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,330 +2,330 @@
https://podaac.github.io/tutorials/notebooks/SWOT-EA-2021/Colocate_satellite_insitu_ocean.html
- 2023-06-29T20:06:31.164Z
+ 2023-06-29T21:43:56.617Z
https://podaac.github.io/tutorials/notebooks/GIS/SWOTsample_CSVconversion.html
- 2023-06-29T20:06:28.028Z
+ 2023-06-29T21:43:52.641Z
https://podaac.github.io/tutorials/notebooks/GIS/SWOTsample_GISshapefiles.html
- 2023-06-29T20:06:26.528Z
+ 2023-06-29T21:43:50.729Z
https://podaac.github.io/tutorials/notebooks/Podaac_CMR_Shapefile_Search.html
- 2023-06-29T20:06:24.064Z
+ 2023-06-29T21:43:47.561Z
https://podaac.github.io/tutorials/notebooks/Cloud L2SS subset and plot - JH.html
- 2023-06-29T20:06:22.020Z
+ 2023-06-29T21:43:45.032Z
https://podaac.github.io/tutorials/notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html
- 2023-06-29T20:06:20.772Z
+ 2023-06-29T21:43:43.480Z
https://podaac.github.io/tutorials/notebooks/l2-regridding/reprojection notebook.html
- 2023-06-29T20:06:19.308Z
+ 2023-06-29T21:43:41.664Z
https://podaac.github.io/tutorials/notebooks/HUC Feature Translation Service Examples.html
- 2023-06-29T20:06:17.956Z
+ 2023-06-29T21:43:39.980Z
https://podaac.github.io/tutorials/notebooks/harmony subsetting/Harmony L2 Subsetter.html
- 2023-06-29T20:06:16.716Z
+ 2023-06-29T21:43:38.464Z
https://podaac.github.io/tutorials/notebooks/Harmony API.html
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