Make A Graph

Dataset Title:	2 days 1.1 km resolution forecast over Liguria (01)
Institution:	UNIGE-DICCA (Dataset ID: unige-dicca_forecast_nep_1_1km_01)
Information:	Summary \| License \| 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 →

y (km)

x (km)

Graph Settings
Color Bar:	Continuity:	Scale:
Minimum:	Maximum:	N Sections:
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

)

[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 1.7750916e+9, 1.7752608e+9;
    String axis "T";
    String calendar "proleptic_gregorian";
    String ioos_category "Time";
    String long_name "GRIB forecast or observation time";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  y {
    String _CoordinateAxisType "GeoY";
    Float32 actual_range 448.7373, 537.6172;
    String long_name "Projection Y Coordinate";
    String standard_name "projection_y_coordinate";
    String units "km";
  }
  x {
    String _CoordinateAxisType "GeoX";
    Float32 actual_range -434.2693, -322.0583;
    String long_name "Projection X Coordinate";
    String standard_name "projection_x_coordinate";
    String units "km";
  }
  Convective_precipitation_surface_Mixed_intervals_Accumulation {
    String abbreviation "ACPCP";
    Float64 colorBarMaximum 200.0;
    Float64 colorBarMinimum 0.0;
    String Grib2_Generating_Process_Type "Forecast";
    String Grib2_Level_Desc "Ground or water surface";
    Int32 Grib2_Level_Type 1;
    Int32 Grib2_Parameter 0, 1, 10;
    String Grib2_Parameter_Category "Moisture";
    String Grib2_Parameter_Discipline "Meteorological products";
    String Grib2_Parameter_Name "Convective precipitation";
    String Grib_Statistical_Interval_Type "Accumulation";
    String Grib_Variable_Id "VAR_0-1-10_L1_Imixed_S1";
    String long_name "Convective precipitation (Mixed_intervals Accumulation) @ Ground or water surface";
    Float32 missing_value NaN;
    String units "kg.m-2";
  }
  Evaporation_surface_Mixed_intervals_Accumulation {
    String abbreviation "EVP";
    Float64 colorBarMaximum 1.0e-4;
    Float64 colorBarMinimum -1.0e-4;
    String Grib2_Generating_Process_Type "Forecast";
    String Grib2_Level_Desc "Ground or water surface";
    Int32 Grib2_Level_Type 1;
    Int32 Grib2_Parameter 0, 1, 6;
    String Grib2_Parameter_Category "Moisture";
    String Grib2_Parameter_Discipline "Meteorological products";
    String Grib2_Parameter_Name "Evaporation";
    String Grib_Statistical_Interval_Type "Accumulation";
    String Grib_Variable_Id "VAR_0-1-6_L1_Imixed_S1";
    String long_name "Evaporation (Mixed_intervals Accumulation) @ Ground or water surface";
    Float32 missing_value NaN;
    String units "kg.m-2";
  }
  Total_precipitation_surface_Mixed_intervals_Accumulation {
    String abbreviation "APCP";
    Float64 colorBarMaximum 200.0;
    Float64 colorBarMinimum 0.0;
    String Grib2_Generating_Process_Type "Forecast";
    String Grib2_Level_Desc "Ground or water surface";
    Int32 Grib2_Level_Type 1;
    Int32 Grib2_Parameter 0, 1, 8;
    String Grib2_Parameter_Category "Moisture";
    String Grib2_Parameter_Discipline "Meteorological products";
    String Grib2_Parameter_Name "Total precipitation";
    String Grib_Statistical_Interval_Type "Accumulation";
    String Grib_Variable_Id "VAR_0-1-8_L1_Imixed_S1";
    String long_name "Total precipitation (Mixed_intervals Accumulation) @ Ground or water surface";
    Float32 missing_value NaN;
    String units "kg.m-2";
  }
  NC_GLOBAL {
    String cdm_data_type "Grid";
    String contributors_email "meteocean.unige@gmail.com";
    String contributors_name "MeteOcean research group";
    String contributors_role "Research management";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "francesco.ferrari@unige.it";
    String creator_name "Ferrari, Francesco";
    String creator_orcid "0000-0002-8752-993X";
    String creator_type "person";
    String creator_url "https://forecast.meteocean.science";
    String data_doi "10.5281/zenodo.17572152";
    String data_format_original "grib2";
    String data_version "3.9.1";
    String distribution "https://s4raise.it/dssmare/distributed_monitoring_system/, https://s4raise.it/dssmare/metocean_models/";
    String documentation "https://s4raise.it/dssmare/distributed_monitoring/, https://s4raise.it/dssmare/metocean_models/, https://raise-spoke3.s4raise.it/project01";
    String grid_mapping__CoordinateAxisTypes "GeoX GeoY";
    String grid_mapping__CoordinateTransformType "Projection";
    String grid_mapping_earth_radius "6367470.0";
    String grid_mapping_latitude_of_projection_origin "40.0";
    String grid_mapping_longitude_of_central_meridian "14.0";
    String grid_mapping_name "lambert_conformal_conic";
    String grid_mapping_standard_parallel "40.0";
    String history 
"2026-04-03T04:35:19Z (local files)
2026-04-03T04:35:19Z https://erddap.s4raise.it/griddap/unige-dicca_forecast_nep_1_1km_01.das";
    String infoUrl "https://www.raiseliguria.it/spoke-3/";
    String inspire "Oceanographic geographical features";
    String institution "UNIGE-DICCA";
    String institution_country "IT";
    String keywords "Vorticity (858a80ff-5aa4-4590-b2e2-e88a802a6ee4)";
    String keywords_vocabulary "GCMD Science Keywords";
    String language "XML";
    String license "CC-BY 4.0";
    String maintainer "ETT S.p.A.";
    String model_realtime "true";
    String naming_authority "RAISE, Sindbad 2.0";
    String owner "UNIGE-DICCA";
    String owner_url "https://meteocean.science/";
    String project_code "RAISE, Sindbad 2.0";
    String project_id "ECS00000035,";
    String project_name "Robotics and AI for Socio-economic Empowerment, Servizi per la Navigazione e Attività Marittime";
    String project_statement "RAISE: Robotics and AI for Socio-economic Empowerment is an innovation ecosystem funded by the Ministry of University and Research under the National Recovery and Resilience Plan (NRRP, Mission 4, Component 2, Investment 1.5). Sindbad 2.0: Servizi per la Navigazione e Attività Marittime (Services for Navigation and Maritime Activities)";
    String project_url "https://www.raiseliguria.it/, https://www.sindbad-liguria.it/";
    String publisher "ETT S.p.A.";
    String references "https://doi.org/10.1016/j.atmosres.2015.05.010, https://doi.org/10.1002/2016GL068265";
    String source "WRF simulations initialized with GFS global model simulation";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "Hourly 3-dimensional atmospheric gridded data, with a temporal coverage of 48 hours and a spatial resolution of 1.1 km. Data cover Central and Eastern Liguria.";
    String testOutOfDate "now+1day";
    String theme_eu_data "http://publications.europa.eu/resource/authority/data-theme/ENVI";
    String theme_eurovoc "http://publications.europa.eu/resource/authority/eurovoc/100224";
    String time_coverage_end "2026-04-04T00:00:00Z";
    String time_coverage_resolution "automatically taken from data";
    String time_coverage_start "2026-04-02T01:00:00Z";
    String title "2 days 1.1 km resolution forecast over Liguria (01)";
    String visibility "public";
  }
}

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.