Make A Graph

Dataset Title:	AdriaClim Indicators \| adriaclim_WRF \| monthly \| anomaly
Institution:	CMCC (Dataset ID: adriaclim_WRF_5e78_b419_ec8a)
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:
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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 6.94224e+8, 6.94224e+8;
    String axis "T";
    String calendar "proleptic_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";
    Float32 actual_range 37.00147, 46.97328;
    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 10.0168, 21.98158;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  consecutive_dry_days_index_per_time_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Consecutive dry days is the greatest number of consecutive days per time period with daily precipitation amount  below 1 mm. The time period should be defined by the bounds of the time coordinate.";
    Float32 missing_value NaN;
    String units "No";
  }
  number_of_cdd_periods_with_more_than_5days_per_time_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Number of cdd periods in given time period with more than 5 days. The time period should be defined by the bounds of the time coordinate.";
    Float32 missing_value NaN;
    String units "No";
  }
  consecutive_summer_days_index_per_time_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Consecutive summer days index is the greatest number of consecutive summer days in a given time period. Summer days is the number of days where maximum of temperature is above 25 degree Celsius. The time period should be defined by the bounds of the time";
    Float32 missing_value NaN;
  }
  number_of_csu_periods_with_more_than_5days_per_time_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Number of csu periods in given time period with more than 5 days. The time period should be defined by the bounds of the time coordinate.";
    Float32 missing_value NaN;
    String units "No";
  }
  consecutive_wet_days_index_per_time_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Consecutive wet days is the greatest number of consecutive days per time period with daily precipitation above 1 mm. The time period should be defined by the bounds of the time coordinate.";
    Float32 missing_value NaN;
    String units "No";
  }
  number_of_cwd_periods_with_more_than_5days_per_time_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Number of cwd periods in given time period with more than 5 days. The time period should be defined by the bounds of the time coordinate.";
    Float32 missing_value NaN;
    String units "No";
  }
  heat_wave_duration_index_wrt_mean_of_reference_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "This is the number of days per time period where in intervals of at least 6 consecutive days the daily maximum temperature is more than 5 degrees above a reference value. The reference value is calculated  as the mean of maximum temperatures of a five day";
    Float32 missing_value NaN;
    String units "No";
  }
  heat_waves_per_time_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Number of heat waves per time period. The time period should be defined by the bounds of the time coordinate.";
    Float32 missing_value NaN;
    String units "No";
  }
  very_wet_days_wrt_95th_percentile_of_reference_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Meteorology";
    String long_name "This is the percent of time per time period of wet days (daily sum at least 1 mm / day) where daily precipitation amount of a wet day is above a reference value. The reference value is calculated  as the 95th percentile of all wet days of a given 30 year";
    Float32 missing_value NaN;
    String units "Percent";
  }
  precipitation_percent_due_to_R95p_days {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 5.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Percentage of  total  precipitation amount per time period  due to  very_wet_days_wrt_95th_percentile_of_reference_period. The time period should be defined by the bounds of the time coordinate.";
    Float32 missing_value NaN;
    String units "Percent";
  }
  rx1day {
    Float32 _FillValue NaN;
    String cell_methods "Times: sum";
    String description "BIAS CORRECTED 6 HOUR CUMULATED TOTAL RAINFALL";
    String ioos_category "Meteorology";
    String long_name "Maximum of one day precipitation amount in a given time period.";
    Float32 missing_value NaN;
    String units "mm";
  }
  rx5day {
    Float32 _FillValue NaN;
    String cell_methods "Times: sum";
    String description "BIAS CORRECTED 6 HOUR CUMULATED TOTAL RAINFALL";
    String ioos_category "Meteorology";
    String long_name "Maximum of 5-days moving window precipitation amount in a given time period.";
    Float32 missing_value NaN;
    String units "mm";
  }
  simple_daily_intensity_index_per_time_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Simple daily intensity index is the mean of precipitation amount on wet days. A wet day is a day with precipitation sum of at least 1 mm. The time period should be defined by the bounds of the time coordinate.";
    Float32 missing_value NaN;
    String units "mm";
  }
  summer_days_index_per_time_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Summer days index is the number of days where maximum of temperature is above 25 degree Celsius. The time period should be defined by the bounds of the time coordinate.";
    Float32 missing_value NaN;
  }
  tg {
    Float32 _FillValue NaN;
    String cell_methods "Times: mean";
    Float64 colorBarMaximum 313.0;
    Float64 colorBarMinimum 263.0;
    String description "BIAS CORRECTED TEMP at 2 M";
    String ioos_category "Temperature";
    String long_name "mean of mean daily temperature over the period";
    Float32 missing_value NaN;
    String units "degree_K";
  }
  tropical_nights_index_per_time_period {
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String ioos_category "Statistics";
    String long_name "Tropical nights index is the number of days where minimum of temperature is above 20 degree Celsius. The time period should be defined by the bounds of the time coordinate.";
    Float32 missing_value NaN;
    String units "No";
  }
  txn {
    Float32 _FillValue NaN;
    String cell_methods "Times: maximum";
    Float64 colorBarMaximum 313.0;
    Float64 colorBarMinimum 263.0;
    String description "BIAS CORRECTED TEMP at 2 M";
    String ioos_category "Temperature";
    String long_name "minimum vale of daily maximum temperature over the period";
    Float32 missing_value NaN;
    String units "degree_K";
  }
  txx {
    Float32 _FillValue NaN;
    String cell_methods "Times: maximum";
    Float64 colorBarMaximum 313.0;
    Float64 colorBarMinimum 263.0;
    String description "BIAS CORRECTED TEMP at 2 M";
    String ioos_category "Temperature";
    String long_name "maximum value of daily maximum temperature over the period";
    Float32 missing_value NaN;
    String units "degree_K";
  }
  NC_GLOBAL {
    String adriaclim_dataset "indicator";
    String adriaclim_model "WRF - V3.5.1";
    String adriaclim_scale "adriatic";
    String adriaclim_timeperiod "monthly";
    String adriaclim_type "anomaly";
    String CDI "Climate Data Interface version 1.9.8 (https://mpimet.mpg.de/cdi)";
    String cdm_data_type "Grid";
    String CDO "Climate Data Operators version 1.9.8 (https://mpimet.mpg.de/cdo)";
    String Conventions "CF-1.6, COARDS, ACDD-1.3";
    Float64 Easternmost_Easting 21.98158;
    Float64 geospatial_lat_max 46.97328;
    Float64 geospatial_lat_min 37.00147;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 21.98158;
    Float64 geospatial_lon_min 10.0168;
    Float64 geospatial_lon_resolution 0.062316562500000006;
    String geospatial_lon_units "degrees_east";
    String history 
"2024-09-18T10:08:00Z (local files)
2024-09-18T10:08:00Z https://erddap-adriaclim.cmcc-opa.eu/griddap/adriaclim_WRF_5e78_b419_ec8a.das";
    String history_of_appended_files 
"Thu Dec 22 16:13:06 2022: Appended file lat1.nc had following \"history\" attribute:
Thu Dec 22 16:13:03 2022: ncrename -O -d south_north,lat lat1.nc
Thu Dec 22 16:13:03 2022: ncwa -a west_east lat.nc lat1.nc
Thu Dec 22 16:13:03 2022: ncks -v XLAT adriaclim_WRF_cdd_hist_monthly_1991_2020.nc lat.nc
Indicator provided by CMCC Foundation
Thu Dec 22 16:13:06 2022: Appended file lon1.nc had following \"history\" attribute:
Thu Dec 22 16:13:03 2022: ncrename -O -d west_east,lon lon1.nc
Thu Dec 22 16:13:03 2022: ncwa -a south_north lon.nc lon1.nc
Thu Dec 22 16:13:03 2022: ncks -v XLONG adriaclim_WRF_cdd_hist_monthly_1991_2020.nc lon.nc
Indicator provided by CMCC Foundation";
    String infoUrl "https://cmcc.it";
    String institution "CMCC";
    String keywords "5-days, 95th, above, all, amount, below, bounds, calculated, cdd, celsius, consecutive, consecutive_dry_days_index_per_time_period, consecutive_summer_days_index_per_time_period, consecutive_wet_days_index_per_time_period, coordinate, csu, cwd, daily, data, day, days, defined, degree, degrees, dry, due, five, given, greatest, heat, heat_wave_duration_index_wrt_mean_of_reference_period, heat_waves_per_time_period, index, intensity, intervals, least, local, maximum, mean, meteorology, minimum, more, moving, nights, number, number_of_cdd_periods_with_more_than_5days_per_time_period, number_of_csu_periods_with_more_than_5days_per_time_period, number_of_cwd_periods_with_more_than_5days_per_time_period, one, over, per, percent, percentage, percentile, period, periods, precipitation, precipitation_percent_due_to_R95p_days, rain, rainfall, reference, rx1day, rx5day, simple, simple_daily_intensity_index_per_time_period, source, statistics, sum, summer, summer_days_index_per_time_period, temperature, temperatures, than, time, total, tropical, tropical_nights_index_per_time_period, txn, txx, vale, value, very, very_wet_days_wrt_95th_percentile_of_reference_period, waves, wet, where, window, with, wrt, year";
    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 NCO "netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco)";
    Float64 Northernmost_Northing 46.97328;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 37.00147;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "AdriaClim Indicators | adriaclim_WRF | monthly | anomaly";
    String time_coverage_end "1992-01-01T00:00:00Z";
    String time_coverage_start "1992-01-01T00:00:00Z";
    String title "AdriaClim Indicators | adriaclim_WRF | monthly | anomaly";
    Float64 Westernmost_Easting 10.0168;
  }
}

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.