ERDDAP > griddap > Data Access Form

Dataset Title:	Average monthly chlorophyll-a concentrations for Lake Ontario (2012-2019), generated using the dynamically parameterized Color Producing Agents Algorithm (CPA-A).
Institution:	CoastWatch Great Lakes Node   (Dataset ID: LO_CHL_VIIRS_Monthly_Avg_1)
Information:	Summary | License | FGDC | ISO 19115 | Metadata | Background | Files | Make a graph

 

Dimensions	Start	Stride	Stop	Size	Spacing
time (UTC)				56	49 days 19h 25m 5s (uneven)
latitude (degrees_north)				200	0.0075 (even)
longitude (degrees_east)				533	0.0075 (even)

Grid Variables (which always also download all of the dimension variables)

Chl_a (Monthly Average Chlorophyll-a, SNPP VIIRS, EXPERIMENTAL, mg m-3)

File type: (more information) .asc - View OPeNDAP-style ISO-8859-1 comma-separated text. .csv - Download a ISO-8859-1 comma-separated text table (line 1: names; line 2: units; ISO 8601 times). .csvp - Download a ISO-8859-1 .csv file with line 1: name (units). Times are ISO 8601 strings. .csv0 - Download a ISO-8859-1 .csv file without column names or units. Times are ISO 8601 strings. .das - View the dataset's metadata via an ISO-8859-1 OPeNDAP Dataset Attribute Structure (DAS). .dds - View the dataset's structure via an ISO-8859-1 OPeNDAP Dataset Descriptor Structure (DDS). .dods - OPeNDAP clients use this to download the data in the DODS binary format. .esriAscii - Download an ISO-8859-1 ESRI ASCII file (latitude longitude data only; longitude must be all below or all above 180). .fgdc - View the dataset's UTF-8 FGDC .xml metadata. .graph - View a Make A Graph web page. .help - View a web page with a description of griddap. .html - View an OPeNDAP-style HTML Data Access Form. .htmlTable - View a UTF-8 .html web page with the data in a table. Times are ISO 8601 strings. .iso19115 - View the dataset's ISO 19115-2/19139 UTF-8 .xml metadata. .itx - Download an ISO-8859-1 Igor Text File. Each axis variable and each data variable becomes a wave. .json - View a table-like UTF-8 JSON file (missing value = 'null'; times are ISO 8601 strings). .jsonlCSV1 - View a UTF-8 JSON Lines CSV file with column names on line 1 (mv = 'null'; times are ISO 8601 strings). .jsonlCSV - View a UTF-8 JSON Lines CSV file without column names (mv = 'null'; times are ISO 8601 strings). .jsonlKVP - View a UTF-8 JSON Lines file with Key:Value pairs (missing value = 'null'; times are ISO 8601 strings). .mat - Download a MATLAB binary file. .nc - Download a NetCDF-3 binary file with COARDS/CF/ACDD metadata. .ncHeader - View the UTF-8 header (the metadata) for the NetCDF-3 .nc file. .ncml - View the dataset's structure and metadata as a UTF-8 NCML .xml file. .nccsv - Download a NetCDF-3-like 7-bit ASCII NCCSV .csv file with COARDS/CF/ACDD metadata. .nccsvMetadata - View the dataset's metadata as the top half of a 7-bit ASCII NCCSV .csv file. .ncoJson - Download a UTF-8 NCO lvl=2 JSON file with COARDS/CF/ACDD metadata. .odvTxt - Download time,latitude,longitude,otherVariables as an ODV Generic Spreadsheet File (.txt). .timeGaps - View a UTF-8 list of gaps in the time values which are larger than the median gap. .tsv - Download a ISO-8859-1 tab-separated text table (line 1: names; line 2: units; ISO 8601 times). .tsvp - Download a ISO-8859-1 .tsv file with line 1: name (units). Times are ISO 8601 strings. .tsv0 - Download a ISO-8859-1 .tsv file without column names or units. Times are ISO 8601 strings. .wav - Download a .wav audio file. All columns must be numeric and of the same type. .xhtml - View a UTF-8 XHTML (XML) file with the data in a table. Times are ISO 8601 strings. .geotif - View a grayscale GeoTIFF .tif file (for latitude longitude data; longitude must be all below or all above 180). .kml - View a Google Earth .kml file (for latitude, longitude data only). .smallPdf - View a small .pdf image file with a graph or map. .pdf - View a standard, medium-sized .pdf image file with a graph or map. .largePdf - View a large .pdf image file with a graph or map. .smallPng - View a small .png image file with a graph or map. .png - View a standard, medium-sized .png image file with a graph or map. .largePng - View a large .png image file with a graph or map. .transparentPng - View a .png image file (just the data, without axes, landmask, or legend).

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.3345488e+9, 1.5712416e+9;
    String axis "T";
    Int32 fraction_digits 0;
    String ioos_category "Time";
    String long_name "Time";
    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";
    Float64 actual_range 43.10375, 44.59625;
    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 -79.99625, -76.00625;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  Chl_a {
    Float32 _FillValue NaN;
    String ioos_category "Ocean Color";
    String long_name "Monthly Average Chlorophyll-a, SNPP VIIRS, EXPERIMENTAL";
    String standard_name "chlorophyll-a";
    String units "mg m-3";
  }
  NC_GLOBAL {
    String cdm_data_type "Grid";
    String contact_email "mjsayers@mtu.edu";
    String contact_name "Michael Sayers";
    String Conventions "CF-1.10, COARDS, ACDD-1.3";
    String date_created "2024-07-30T19:54:22.659558Z";
    String description "Average monthly chlorophyll-a concentrations for Lake Ontario in October 2019, generated using the dynamically parameterized Color Producing Agents Algorithm (CPA-A).";
    Float64 Easternmost_Easting -76.00625;
    String GDAL "GDAL 3.3.2, released 2021/09/01";
    String GDAL_AREA_OR_POINT "Area";
    Float64 geospatial_lat_max 44.59625;
    Float64 geospatial_lat_min 43.10375;
    Float64 geospatial_lat_resolution 0.007499999999999999;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -76.00625;
    Float64 geospatial_lon_min -79.99625;
    Float64 geospatial_lon_resolution 0.007500000000000017;
    String geospatial_lon_units "degrees_east";
    String grid_mapping_crs_wkt "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]";
    String grid_mapping_GeoTransform "-80 0.0075 0 44.6 0 -0.0075";
    Float64 grid_mapping_inverse_flattening 298.257223563;
    String grid_mapping_long_name "CRS definition";
    Float64 grid_mapping_longitude_of_prime_meridian 0.0;
    String grid_mapping_name "latitude_longitude";
    Float64 grid_mapping_semi_major_axis 6378137.0;
    String grid_mapping_spatial_ref "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]";
    String history 
"Tue Sep 24 20:50:14 2024: ncatted -O -a sensor,global,c,c,VIIRS /mnt/cwdata/p/p/cdc/2019/LO_2019_10_16_chl_Dynamic-CPAA.nc
Tue Sep 24 20:50:14 2024: ncatted -O -a institution,global,c,c,Michigan Tech Research Institute /mnt/cwdata/p/p/cdc/2019/LO_2019_10_16_chl_Dynamic-CPAA.nc
Tue Jul 30 15:54:22 2024: ncecat -O -D 2 -u time /ws3/gis_lab/project/GLNPO_Monitoring/DataProducts/NESDIS_VIIRS/CoastWatch_Files/Ontario_Monthly_NC/LO_October_2019_chl_Dynamic-CPAA.nc /ws3/gis_lab/project/GLNPO_Monitoring/DataProducts/NESDIS_VIIRS/CoastWatch_Files/Ontario_Monthly_NC/LO_October_2019_chl_Dynamic-CPAA.nc
Tue Jul 30 15:54:22 2024: GDAL CreateCopy( /ws3/gis_lab/project/GLNPO_Monitoring/DataProducts/NESDIS_VIIRS/CoastWatch_Files/Ontario_Monthly_NC/LO_October_2019_chl_Dynamic-CPAA.nc, ... )
2024-12-26T21:38:19Z (local files)
2024-12-26T21:38:19Z https://apps.glerl.noaa.gov/erddap/griddap/LO_CHL_VIIRS_Monthly_Avg_1.das";
    String infoUrl "https://coastwatch.glerl.noaa.gov/";
    String institution "CoastWatch Great Lakes Node";
    String instrument "VIIRS";
    String keywords "agents, algorithm, average, Chl-a, chla, chlorophyll, chlorophyll-a, color, concentrations, data, dynamically, experimental, generated, imager, imager/radiometer, imaging, infrared, lake, month, monthly, ocean, ocean color, ontario, parameterized, producing, radiometer, snpp, suite, time, using, viirs, visible";
    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.";
    String LongName "Monthly Average Lake Ontario Chlorophyll-a - October 2019";
    String NCO "netCDF Operators version 4.9.1 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco)";
    Float64 Northernmost_Northing 44.59625;
    String platform "SNPP";
    String ProductionDateTime "2024-07-30T19:54:22.658808Z";
    String sensor "VIIRS";
    String ShortName "LakeOntarioCHL_October2019";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 43.10375;
    String spatialResolution "0.0075 degrees";
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "Average monthly chlorophyll-a concentrations for Lake Ontario (2012-2019), generated using the dynamically parameterized Color Producing Agents Algorithm (CPA-A).";
    String time_coverage_end "2019-10-16T16:00:00Z";
    String time_coverage_start "2012-04-16T04:00:00Z";
    String title "Average monthly chlorophyll-a concentrations for Lake Ontario (2012-2019), generated using the dynamically parameterized Color Producing Agents Algorithm (CPA-A).";
    Float64 Westernmost_Easting -79.99625;
  }
}

 

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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 Data Access Protocol (DAP) and its projection constraints.

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[(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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