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Dataset Title:  Averages of VIIRS-NPP products at 9km 1-day resolution gapfilled using DinEOF Subscribe RSS
Institution:  University of Delaware   (Dataset ID: viirs_1km_1day_gapfilled)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Files | Make a graph
 
Dimensions ? Start ? Stride ? Stop ?  Size ?    Spacing ?
 time2 (days in between files) ?      2326    1.953118 (uneven)
  < slider >
 forecast_time (Days since time) ?      4    1.0 (even)
  < slider >
 latitude (degrees_north) ?      156    0.007999986 (uneven)
  < slider >
 longitude (degrees_east) ?      100    0.009999978 (uneven)
  < slider >
 
Grid Variables (which always also download all of the dimension variables) 
 forecast_sst (Sea Surface Temperature, Degree_C) ?
 forecast_a_443_qaa (m^-1) ?
 forecast_Rrs_551 (sr^-1) ?
 forecast_chl_oc3 (mg m^-3) ?

File type: (more information)

(Documentation / Bypass this form) ?
 
(Please be patient. It may take a while to get the data.)


 

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  time2 {
    Float64 actual_range 15709.0, 20250.0;
    String ioos_category "Time";
    String long_name "Time";
    String units "days in between files";
  }
  forecast_time {
    Int32 actual_range 0, 3;
    String ioos_category "Time";
    String long_name "Forecast Time";
    String units "Days since time";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 actual_range 38.45600128173828, 39.69599914550781;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 actual_range -75.69999694824219, -74.70999908447266;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  forecast_sst {
    Float32 _FillValue NaN;
    Float64 colorBarMaximum 32.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Temperature";
    String long_name "Sea Surface Temperature";
    String standard_name "sea_surface_temperature";
    String units "Degree_C";
  }
  forecast_a_443_qaa {
    Float32 _FillValue NaN;
    Float64 colorBarMaximum 150.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Quality";
    String long_name "Forecast A 443 Qaa";
    String units "m^-1";
  }
  forecast_Rrs_551 {
    Float32 _FillValue NaN;
    String ioos_category "Optical Properties";
    String long_name "Forecast Rrs 551";
    String units "sr^-1";
  }
  forecast_chl_oc3 {
    Float32 _FillValue NaN;
    String ioos_category "Unknown";
    String long_name "Forecast Chl Oc3";
    String units "mg m^-3";
  }
  NC_GLOBAL {
    String cdm_data_type "Grid";
    String Conventions "COARDS, CF-1.10, ACDD-1.3";
    Float64 Easternmost_Easting -74.70999908447266;
    Float64 geospatial_lat_max 39.69599914550781;
    Float64 geospatial_lat_min 38.45600128173828;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -74.70999908447266;
    Float64 geospatial_lon_min -75.69999694824219;
    String geospatial_lon_units "degrees_east";
    String history 
"2025-09-11T08:04:33Z (local files)
2025-09-11T08:04:33Z http://ocean-data.ceoe.udel.edu/erddap/griddap/viirs_1km_1day_gapfilled.das";
    String infoUrl "???";
    String institution "University of Delaware";
    String keywords "chl, chlorophyll, data, earth, Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature, forecast, forecast_a_443_qaa, forecast_chl_oc3, forecast_Rrs_551, forecast_sst, forecast_time, local, oc3, ocean, oceans, optical, optical properties, properties, qaa, quality, rrs, science, sea, sea_surface_temperature, source, surface, temperature, time";
    String keywords_vocabulary "GCMD Science Keywords";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    Float64 Northernmost_Northing 39.69599914550781;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 38.45600128173828;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "Averages of VIIRS-NPP products at 9km 1-day resolution gapfilled using DinEOF";
    String title "Averages of VIIRS-NPP products at 9km 1-day resolution gapfilled using DinEOF";
    Float64 Westernmost_Easting -75.69999694824219;
  }
}

 

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[(2002-06-01T09:00:00Z)][(-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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