Make A Graph

Dataset Title:	Sea Surface Temperature, NOAA geopolar blended - Cumulative mean 2018 (2017 Reanalysis)
Institution:	Office of Satellite Products and Operations (Dataset ID: goes-poes-ghrsst-RAN-2018-clim)
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Data Access Form

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Graph Type:
X Axis:
Y Axis:
Color:

Dimensions

Start

Stop

time (UTC)

specify just 1 value →

latitude (degrees_north)

longitude (degrees_east)

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:

Web page authors can embed a graph of the latest data in a web page using HTML <img> tags.
Anyone can use ERDDAPs Slide Sorter to build a personal web page that displays graphs with the latest data (or other images or HTML content), each in its own, draggable slide.

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.

(easy) You can get data by using the dataset's Data Access Form. It makes the URL for you.
(easy) You can make a graph or map by using the dataset's Make A Graph form. It makes the URL for you.
(not hard) You can bypass the forms and get the data or make a graph or map by generating the URL by hand or with a computer program or script.

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.