Make A Graph

Dataset Title:	AdriaClim Indicators \| MedCordex_IPSL \| csu \| hist, 1991-2020
Institution:	CMCC (Dataset ID: trends_be41_dfab_94ee)
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Data Access Form \| Files

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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 1.5778368e+9, 1.5778368e+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 _FillValue NaN;
    Float32 actual_range 37.28878, 46.88878;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String source_name "y";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 _FillValue NaN;
    Float32 actual_range 10.24039, 21.66346;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String source_name "x";
    String standard_name "longitude";
    String units "degrees_east";
  }
  signif {
    Float64 _FillValue NaN;
    Float64 colorBarMaximum 1.5;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Unknown";
    String long_name "Trends significance";
    Float64 missing_value NaN;
    String name "signif";
    Float64 valid_max 1.0;
    Float64 valid_min 0.0;
    Float64 valid_range 0.0, 1.0;
  }
  trend {
    Float64 _FillValue NaN;
    Float64 colorBarMaximum 2.0;
    Float64 colorBarMinimum -1.0;
    String ioos_category "Time";
    String long_name "Mean trend over the entire timeseries, Significance level 0.05";
    Float64 missing_value NaN;
    String name "trend";
    String references "Josué Martínez Moreno, & Navid C. Constantinou. (2021, January 23). josuemtzmo/xarrayMannKendall: Mann Kendall significance test implemented in xarray. (Version v.1.0.0). Zenodo. http://doi.org/10.5281/zenodo.4458777 https://github.com/josuemtzmo/xarrayMannKendall";
    String units "days year-1";
    Float64 valid_max 1.6093437152391543;
    Float64 valid_min -0.48120133481646266;
    Float64 valid_range -0.48120133481646266, 1.6093437152391543;
  }
  p {
    Float64 _FillValue NaN;
    String ioos_category "Unknown";
    String long_name "P";
  }
  std_error {
    Float64 _FillValue NaN;
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Std Error";
  }
  NC_GLOBAL {
    String adriaclim_dataset "model";
    String adriaclim_model "MedCordex_CNRS-IPSL";
    String adriaclim_scale "adriatic";
    String adriaclim_timeperiod "yearly";
    String adriaclim_type "trend";
    String Author "Giusy Fedele";
    String cdm_data_type "Grid";
    String contact "giusy.fedele@cmcc.it";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String Created_date "13/06/2023 10:30:42";
    String creator_email "giusy.fedele@cmcc.it";
    String creator_name "Giusy Fedele";
    String creator_type "person";
    String creator_url "https://www.ec.gc.ca/scitech/default.asp?lang=En&n=61B33C26-1#cmc";
    String Description "Trends, significance and uncertainties: Linear trends are calculated using a linear least-squares regression for spatially integrated time series. All the observed trends are assessed using a Theil–Sen estimator, while the statistical significance uses a modified Mann–Kendall test.";
    Float64 Easternmost_Easting 21.66346;
    Float64 geospatial_lat_max 46.88878;
    Float64 geospatial_lat_min 37.28878;
    Float64 geospatial_lat_resolution 0.2999999999999998;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 21.66346;
    Float64 geospatial_lon_min 10.24039;
    Float64 geospatial_lon_resolution 0.3461536363636364;
    String geospatial_lon_units "degrees_east";
    String history 
"2024-09-18T10:10:23Z (local files)
2024-09-18T10:10:23Z https://erddap-adriaclim.cmcc-opa.eu/griddap/trends_be41_dfab_94ee.das";
    String infoUrl "https://cmcc.it";
    String institution "CMCC";
    String keywords "cmcc, csu, data, depth, entire, error, level, mean, over, per, profiler, salinity, salinity-temperature-depth, sample, signif, significance, statistics, std, std_error, temperature, time, timeseries, trend, trends";
    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 46.88878;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 37.28878;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "AdriaClim Indicators | MedCordex_IPSL | csu | hist, 1991-2020";
    String time_coverage_end "2020-01-01T00:00:00Z";
    String time_coverage_start "2020-01-01T00:00:00Z";
    String title "AdriaClim Indicators | MedCordex_IPSL | csu | hist, 1991-2020";
    Float64 Westernmost_Easting 10.24039;
  }
}

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.