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Dataset Title:  AdriaClim | ISPRA | SWChl EventPer InRange1 win projection 2022 2050 Subscribe RSS
Institution:  ISPRA   (Dataset ID: chl_1ed3_6cd8_487c)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
Graph Type:  ?
X Axis:  ?
Y Axis:  ?
Color:  ?
 
Dimensions ?    Start ?    Stop ?
time (UTC) ?     specify just 1 value →
   
< <
latitude (degrees_north) ?
    +
    -
< slider >
longitude (degrees_east) ?
    +
    -
< 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 2.524608e+9, 2.524608e+9;
    String axis "T";
    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";
    Float32 actual_range 43.98958, 45.01042;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 actual_range 12.0, 13.0;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  SWChl_EventPer_InRange1_win {
    Int32 _Fillvalue -999;
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Ocean Color";
    String long_name "Percentage of 5m mean Chlorophill-a is >= 0.2 mg/m^3 given WINTER measurments";
    Float32 missing_value -999.0;
    String units "percent";
  }
  NC_GLOBAL {
    String cdm_data_type "Grid";
    String Conventions "CF-1.6, COARDS, ACDD-1.3";
    String coordinates "Spherical WGS84";
    Float64 Easternmost_Easting 13.0;
    Float64 geospatial_lat_max 45.01042;
    Float64 geospatial_lat_min 43.98958;
    Float64 geospatial_lat_resolution 0.02083346938775524;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 13.0;
    Float64 geospatial_lon_min 12.0;
    Float64 geospatial_lon_resolution 0.020833333333333332;
    String geospatial_lon_units "degrees_east";
    String history 
"2024-09-18T10:12:29Z (local files)
2024-09-18T10:12:29Z https://erddap-adriaclim.cmcc-opa.eu/griddap/chl_1ed3_6cd8_487c.das";
    String infoUrl "https://www.isprambiente.gov.it";
    String institution "ISPRA";
    String keywords "2022-2050, analysis, annual, chla, chlorophill, chlorophill-a, chlorophyll, chlorophyll-a, color, data, given, m^3, mean, measurments, mg/m^3, ocean, ocean color, percentage, projection, seasonal, SWChl_EventPer_InRange1_win, winter";
    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 45.01042;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 43.98958;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "AdriaClim | ISPRA | SWChl EventPer InRange1 win projection 2022 2050";
    String time_coverage_end "2050-01-01T00:00:00Z";
    String time_coverage_start "2050-01-01T00:00:00Z";
    String title "AdriaClim | ISPRA | SWChl EventPer InRange1 win projection 2022 2050";
    Float64 Westernmost_Easting 12.0;
  }
}

 

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.

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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