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Dataset Title:  Sea Surface Temperature, NOAA geopolar blended - Cumulative mean 2018 (2017
Reanalysis)
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Institution:  Office of Satellite Products and Operations   (Dataset ID: goes-poes-ghrsst-RAN-2018-clim)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Data Access Form
 
Graph Type:  ?
X Axis:  ?
Y Axis:  ?
Color:  ?
 
Dimensions ?    Start ?    Stop ?
time (UTC) ?     specify just 1 value →
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< <
latitude (degrees_north) ?
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    -
< slider >
longitude (degrees_east) ?
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< slider >
 
Graph Settings
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Draw land mask: 
Y Axis Minimum:   Maximum:   
 
(Please be patient. It may take a while to get the data.)
 
Optional:
Then set the File Type: (File Type information)
and
or view the URL:
(Documentation / Bypass this form ? )
    Click on the map to specify a new center point. ?
Zoom:
[The graph you specified. Please be patient.]

 

Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.5198192e+9, 1.5198192e+9;
    String axis "T";
    String calendar "Gregorian";
    String comment "Nominal time of Level 4 analysis";
    String ioos_category "Time";
    String long_name "reference time of sst field";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 actual_range -89.975, 89.975;
    String axis "Y";
    String comment "equirectangular projection";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
    Float32 valid_max 90.0;
    Float32 valid_min -90.0;
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 actual_range 0.025, 359.975;
    String axis "X";
    String comment "equirectangular projection";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
    Float32 valid_max 360.0;
    Float32 valid_min 0.0;
  }
  analysed_sst {
    Float32 _FillValue NaN;
    Float64 colorBarMaximum 305.0;
    Float64 colorBarMinimum 273.0;
    String comment "nighttime analysed SST for each ocean grid point";
    String ioos_category "Temperature";
    String long_name "analysed sea surface temperature";
    String references "Fieguth,P.W. et al. \"Mapping Mediterranean altimeter data with a multiresolution optimal interpolation algorithm\", J. Atmos. Ocean Tech, 15 (2): 535-546, 1998.     Fieguth, P. Multiply-Rooted Multiscale Models for Large-Scale Estimation, IEEE Image Processing, 10(11), 1676-1686, 2001.     Khellah, F., P.W. Fieguth, M.J. Murray and M.R. Allen, \"Statistical Processing of Large Image Sequences\", IEEE Transactions on Geoscience and Remote Sensing, 12 (1), 80-93, 2005.";
    String source "OSPO-ACSPO_VIIRS, OSPO-ACSPO_METOPB_FRAC, OSPO-GOES13_SST_L3,OSPO-GOES15_SST_L3, OSPO-METEOSAT10_SST_L3, OSPO-MTSAT2_SST_L3";
    String standard_name "sea_surface_foundation_temperature";
    String units "degree_K";
    Float32 valid_max 3500.0;
    Float32 valid_min -200.0;
  }
  NC_GLOBAL {
    String acknowledgement "NOAA/NESDIS";
    String cdm_data_type "Grid";
    String comment "The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST. This analysis includes only nighttime data.";
    String Conventions "CF-1.6, Unidata Observation Dataset v1.0, COARDS, ACDD-1.3";
    String creator_email "john.sapper@noaa.gov";
    String creator_name "Office of Satellite Products and Operations";
    String creator_type "group";
    String creator_url "www.osdpd.nesdis.noaa.gov";
    String date_created "2018-03-01T13:06:20Z";
    Float64 Easternmost_Easting 359.975;
    String gds_version_id "2.0";
    Float64 geospatial_lat_max 89.975;
    Float64 geospatial_lat_min -89.975;
    Float64 geospatial_lat_resolution 0.049999999999999996;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 359.975;
    Float64 geospatial_lon_min 0.025;
    Float64 geospatial_lon_resolution 0.05000000000000001;
    String geospatial_lon_units "degrees_east";
    String history 
"Tue Feb  5 12:16:18 2019: ncatted -O -a easternmost_longitude,global,o,f,360.0 /mnt/r01/data/goes-poes_ghrsst/daily/20180228000000-OSPO-L4_GHRSST-SSTfnd-Geo_Polar_Blended_Night-GLOB-v02.0-fv01.0-0-360.nc
Tue Feb  5 12:16:18 2019: ncatted -O -a westernmost_longitude,global,o,f,0.0 /mnt/r01/data/goes-poes_ghrsst/daily/20180228000000-OSPO-L4_GHRSST-SSTfnd-Geo_Polar_Blended_Night-GLOB-v02.0-fv01.0-0-360.nc
Tue Feb  5 12:16:18 2019: ncatted -O -a valid_max,lon,o,f,360.0 /mnt/r01/data/goes-poes_ghrsst/daily/20180228000000-OSPO-L4_GHRSST-SSTfnd-Geo_Polar_Blended_Night-GLOB-v02.0-fv01.0-0-360.nc
Tue Feb  5 12:16:17 2019: ncatted -O -a valid_min,lon,o,f,0.0 /mnt/r01/data/goes-poes_ghrsst/daily/20180228000000-OSPO-L4_GHRSST-SSTfnd-Geo_Polar_Blended_Night-GLOB-v02.0-fv01.0-0-360.nc
Mon Jan 28 10:52:10 2019: ncap2 -O -s where(lon<0) lon=lon+360 20180228000000-OSPO-L4_GHRSST-SSTfnd-Geo_Polar_Blended_Night-GLOB-v02.0-fv01.0-0-360.nc 20180228000000-OSPO-L4_GHRSST-SSTfnd-Geo_Polar_Blended_Night-GLOB-v02.0-fv01.0-0-360.nc
Mon Jan 28 10:52:01 2019: ncks -O --msa_usr_rdr -d lon,0.0,180.0 -d lon,-180.0,0.0 20180228000000-OSPO-L4_GHRSST-SSTfnd-Geo_Polar_Blended_Night-GLOB-v02.0-fv01.0.nc 20180228000000-OSPO-L4_GHRSST-SSTfnd-Geo_Polar_Blended_Night-GLOB-v02.0-fv01.0-0-360.nc
NESDIS geo-SST L1 to L2 processor, NESDIS Advanced Clear-Sky Processor for Oceans (ACSPO), NESDIS Geo-Polar 1/20th degree Blended SST Analysis
2023-10-02T21:52:09Z (local files)
2023-10-02T21:52:09Z https://oceanwatch.pifsc.noaa.gov/griddap/goes-poes-ghrsst-RAN-2018-clim.das";
    String id "Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0";
    String infoUrl "https://podaac.jpl.nasa.gov/dataset/Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0";
    String institution "Office of Satellite Products and Operations";
    String keywords "analysed, analysed_sst, blended, data, earth, Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature, foundation, global, infrared, input, night, ocean, oceans, office, only, operations, over, products, satellite, science, sea, sea_surface_foundation_temperature, sst, surface, temperature, time, using";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "GHRSST protocol describes data use as free and open";
    String metadata_link "https://podaac.jpl.nasa.gov/ws/metadata/dataset?format=iso&shortName=Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0";
    String naming_authority "org.ghrsst";
    String NCO "4.3.7";
    Int32 nco_openmp_thread_number 1;
    Float64 Northernmost_Northing 89.975;
    String platform "Suomi NPP, MetOpB, GOESE (GOES-13), GOESW (GOES-15), mtsat-2";
    String processing_level "L4";
    String product_version "1.0";
    String project "Group for High Resolution Sea Surface Temperature";
    String publisher_email "ghrsst-po@nceo.ac.uk";
    String publisher_name "The GHRSST Project Office";
    String publisher_type "group";
    String publisher_url "https://www.ghrsst.org";
    String references "Fieguth,P.W. et al. \"Mapping Mediterranean altimeter data with a multiresolution optimal interpolation algorithm\", J. Atmos. Ocean Tech, 15 (2): 535-546, 1998.     Fieguth, P. Multiply-Rooted Multiscale Models for Large-Scale Estimation, IEEE Image Processing, 10(11), 1676-1686, 2001.     Khellah, F., P.W. Fieguth, M.J. Murray and M.R. Allen, \"Statistical Processing of Large Image Sequences\", IEEE Transactions on Geoscience and Remote Sensing, 12 (1), 80-93, 2005.";
    String sensor "VIIRS, AVHRR_FRAC, GOES_Imager, GOES_Imager, AHI";
    String source "OSPO-ACSPO_VIIRS, OSPO-ACSPO_METOPB_FRAC, OSPO-GOES13_SST_L3, OSPO-GOES15_SST_L3, OSPO-METEOSAT10_SST_L3, OSPO-MTSAT2_SST_L3";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing -89.975;
    String spatial_resolution "0.05 degree";
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary "Analysed blended sea surface temperature over the global ocean using night only input data. An SST estimation scheme which combines multi-satellite retrievals of sea surface temperature datasets available from polar orbiters, geostationary InfraRed (IR) and microwave sensors into a single global analysis. This global SST ananlysis provide a daily gap free map of the foundation sea surface temperature at 0.05o spatial resolution.";
    String time_coverage_end "2018-02-28T12:00:00Z";
    String time_coverage_start "2018-02-28T12:00:00Z";
    String title "Sea Surface Temperature, NOAA geopolar blended - Cumulative mean 2018 (2017 Reanalysis)";
    Float64 Westernmost_Easting 0.025;
  }
}

 

Using griddap to Request Data and Graphs from Gridded Datasets

griddap lets you request a data subset, graph, or map from a gridded dataset (for example, sea surface temperature data from a satellite), via a specially formed URL. griddap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its projection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

griddap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/griddap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.htmlTable?analysed_sst[(2015-06-09T09:00:00Z)][(-89.99):1000:(89.99)][(-179.99):1000:(180.0)]
Thus, the query is often a data variable name (e.g., analysed_sst), followed by [(start):stride:(stop)] (or a shorter variation of that) for each of the variable's dimensions (for example, [time][latitude][longitude]).

For details, see the griddap Documentation.


 
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