Data Access Form

Dataset Title:	CMEMS SatCHL: cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D
Institution:	CNR-GOS (Dataset ID: last30DaysMerged_11e1_9291_5b2f)
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Files \| Make a graph

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Dimensions	Size	Spacing
time (UTC)	19	1 day (even)

latitude (degrees_north)	612	1.0 (even)

longitude (degrees_east)	350	1.0 (even)

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

CHL (milligram 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 {
  time {
    UInt32 _ChunkSizes 512;
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.7727552e+9, 1.7743104e+9;
    String axis "T";
    String calendar "gregorian";
    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";
    Int16 actual_range 0, 611;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Int16 actual_range 0, 349;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  CHL {
    Float32 _FillValue -999.0;
    Float64 colorBarMaximum 30.0;
    Float64 colorBarMinimum 0.03;
    String colorBarScale "Log";
    String ioos_category "Ocean Color";
    String long_name "Multi-sensor, multi water-type, interpolated Chlorophyll a concentration";
    Float32 missing_value -999.0;
    String standard_name "mass_concentration_of_chlorophyll_a_in_sea_water";
    String units "milligram m^-3";
  }
  NC_GLOBAL {
    String _NCProperties "version=2,netcdf=4.8.1,hdf5=1.10.7";
    String CDI "Climate Data Interface version 2.0.4 (https://mpimet.mpg.de/cdi)";
    String cdm_data_type "Grid";
    String comment "r=log10(max([rrs443,rrs490,rrs510])/rrs555) ; chlcase1=10^[0.4567-4.2484r+2.0171r^2+2.6202r^3-2.8978r^4] ; r=log10(rrs490/rrs555) ; chlcase2=10.^[0.091-2.620r-1.148r^2-4.949r^3]. Interpolation is made via iterative EOF using the climatology as first guess. WTM and SENSORMASK fields provide the original data coverage.";
    String contact "technical@gos.artov.isac.cnr.it";
    String Conventions "CF-1.10, COARDS, ACDD-1.3";
    String copernicusmarine_version "2.1.1";
    String creator_email "technical@gos.artov.isac.cnr.it";
    String creator_name "TECHNICAL";
    String creator_type "institution";
    String days_in_the_future "1";
    String days_in_the_past "10";
    Float64 Easternmost_Easting 349.0;
    Float64 geospatial_lat_max 611.0;
    Float64 geospatial_lat_min 0.0;
    Float64 geospatial_lat_resolution 1.0;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 349.0;
    Float64 geospatial_lon_min 0.0;
    Float64 geospatial_lon_resolution 1.0;
    String geospatial_lon_units "degrees_east";
    String grid_resolution "1 Km";
    String history 
"Fri Aug 11 11:53:29 2023: ncatted -a history,global,d,, /services/DataRollingCatalog/data/last30DaysMerged/SatCHL.nc
Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2)
Original Dataset = cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D; Translation Date = 2023-07-27T11:20:06.133Z
2026-04-06T23:20:22Z (local files)
2026-04-06T23:20:22Z https://data-omrg.research.um.edu.mt/erddap/griddap/last30DaysMerged_11e1_9291_5b2f.das";
    String infoUrl "???";
    String institution "CNR-GOS";
    String keywords "1km, bgc, bgc-plankton, chemistry, CHL, chlorophyll, cmems, cnr, cnr-gos, color, concentration, council, data, earth, Earth Science > Oceans > Ocean Chemistry > Chlorophyll, gapfree, gos, interpolated, l4-gapfree-multi-1km, mass, mass_concentration_of_chlorophyll_a_in_sea_water, med, multi, multi-sensor, national, near, nrt, obs, obs-oc, observations, ocean, ocean color, oceans, p1d, plankton, real, research, satchl, science, sea, seawater, sensor, time, type, water, water-type";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "See CMEMS Data License";
    String NCO "netCDF Operators version 5.0.6 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco)";
    Float64 Northernmost_Northing 611.0;
    String references "1) Volpe, G., Colella, S., Brando, V., Forneris, V., La Padula, F., Di Cicco, A., Sammartino, M., Bracaglia, M., Artuso, F., and Santoleri, R. (2019). The Mediterranean Ocean Colour Level 3 Operational Multi-Sensor Processing, Ocean Sci., 15, 127–146, https://doi.org/10.5194/os-15-127-2019. - 2) Berthon, J. F., and G. Zibordi (2004), Bio-optical relationships for the northern Adriatic Sea, International Journal of Remote Sensing, 25, 7-8, 1527-1532. - 3) Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintorè, and J. Verron, pp. 227–244";
    String source "surface observation";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 0.0;
    String st_location "0  0";
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "r=log10(max([rrs443,rrs490,rrs510])/rrs555) ; chlcase1=10^[0.4567-4.2484r+2.0171r^2+2.6202r^3-2.8978r^4] ; r=log10(rrs490/rrs555) ; chlcase2=10.^[0.091-2.620r-1.148r^2-4.949r^3]. Interpolation is made via iterative EOF using the climatology as first guess. WTM and SENSORMASK fields provide the original data coverage.";
    String testOutOfDate "now-5days";
    String time_coverage_end "2026-03-24T00:00:00Z";
    String time_coverage_start "2026-03-06T00:00:00Z";
    String title "CMEMS SatCHL: cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D";
    Float64 Westernmost_Easting 0.0;
  }
}

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[(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.