Make A Graph

Dataset Title:	Data from a local source.
Institution:	??? (Dataset ID: SNAP_SEA_ICE_ATLAS)
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Data Access Form

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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 {
    UInt32 _ChunkSizes 1024;
    String _CoordinateAxisType "Time";
    Float64 actual_range -3.7855728e+9, 1.5448752e+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 {
    UInt32 _ChunkSizes 40;
    String _CoordinateAxisType "Lat";
    Float64 actual_range 40.125, 80.125;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    UInt32 _ChunkSizes 60;
    String _CoordinateAxisType "Lon";
    Float64 actual_range -179.875, -119.875;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  sic_con_pct {
    UInt32 _ChunkSizes 10, 40, 60;
    Float32 _FillValue -2.14748365e+9;
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String grid_mapping "crs";
    String ioos_category "Ice Distribution";
    String long_name "Sea Ice Concentration";
    String units "percent";
  }
  NC_GLOBAL {
    String _CoordSysBuilder "ucar.nc2.internal.dataset.conv.CF1Convention";
    String acknowledgement "The Historical Sea Ice Atlas was a joint project funded by the Alaska Ocean Observing System (AOOS), the Alaska Center for Climate Assessment and Policy (ACCAP) and the Scenarios Network for Alaska and Arctic Planning (SNAP). This work was funded by the National Ocean Service at the National Oceanic and Atmospheric Administration (NOAA) through AOOS grant #NA11NOS0120020. Work was performed at UAF by the International Arctic Research Center's ACCAP (funded by the NOAA Office of Oceanic and Atmospheric Research) and SNAP, with assistance from the University of Illinois, Urbana-Champaign.";
    String cdm_data_type "Grid";
    String Conventions "CF-1.6, COARDS, Unidata Dataset Discovery v1.0";
    String creator_email "takurkowski@alaska.edu";
    String creator_name "Tom Kurkowski";
    String creator_url "http://www.snap.uaf.edu/";
    String date_created "2023-02-03T17:09:00Z";
    String date_issued "2023-02-03T17:09:00Z";
    Float64 Easternmost_Easting -119.875;
    String GDAL "GDAL 2.2.1, released 2017/06/23";
    String GDAL_AREA_OR_POINT "Area";
    Float64 geospatial_lat_max 80.125;
    Float64 geospatial_lat_min 40.125;
    Float64 geospatial_lat_resolution 0.25;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -119.875;
    Float64 geospatial_lon_min -179.875;
    Float64 geospatial_lon_resolution 0.25;
    String geospatial_lon_units "degrees_east";
    String history 
"Fri Feb 03 12:01:28 2023: GDAL CreateCopy( nc/seaice_conc_sic_mean_pct_monthly_ak_1850_01.nc, ... )
2023-03-20T18:35:43Z (local files)
2023-03-20T18:35:43Z http://erddap.axiomdatascience.com/griddap/SNAP_SEA_ICE_ATLAS.das";
    String id "seaiceatlas";
    String infoUrl "???";
    String institution "???";
    String keywords "alaska, concentration, data, distribution, estimates, ice, ice distribution, local, monthly, observed, sea, source, waters";
    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 Metadata_Conventions "CF-1.6, COARDS, Unidata Dataset Discovery v1.0";
    String metadata_link "http://seaiceatlas.snap.uaf.edu/";
    String model "SNAP Sea Ice Atlas";
    String naming_authority "edu.uaf.snap";
    Float64 Northernmost_Northing 80.125;
    String processing_level "This data set includes weekly (January 1954 to December 2013) and monthly (January 1850 to May 2022) midpoint historical sea ice concentration (0 - 100%) estimates at 1/4 x 1/4 degree spatial resolution for the ocean region around the state of Alaska, USA. This value-added dataset was developed by compiling the below historical data sources into spatially and temporally standardized datasets. Gaps in temporal or spatial resolutions were filled in with spatial and temporal analog month approaches. Note the monthly values from January 1954 - December 2013 are the week 2 values from the weekly time series. They are provided in the monthly time series for ease of use in monthly midpoint analyses. The January 2014 - February 2017 monthly time series data have been regridded and processed to match the January 1954 - December 2013 series from the NSIDC 0051 Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1, which were accessed from the NSIDC website (https://nsidc.org/data/docs/daac/nsidc0051_gsfc_seaice.gd.html). The further post-processing and regridding of the NSIDC product to the extent / crs / origin / resolution of the existing data, was a non-standard process. It involved initially warping the polar stereographic data to a pacific centered WGS84 crs, converting the sea ice concentration values to points and performing a spline interpolation across the entire domain. This interpolated raster was then filled further around the land-sea divide where there was a mismatch between the NSIDC mask and the Sea Ice Atlas mask. The filling was performed by taking the average of the surrounding sea ice concentration pixels and filling the missing locations. These locations have been flagged in the source band (band 2) to keep track of what was modified from the NSIDC 0051 for this purpose. These data are a compilation of data from many sources integrated into a single gridded product. The sources of data for each grid cell have changed over the years from infrequent land/sea observations, to observationally derived charts, to satellite data for the most recent decades. Temporal and spatial gaps within observed data are filled with analog month approaches. Please note that large portions of the pre-1953, and almost all of the pre-1900 data, are either analog or interpolated data and the user is cautioned to use these data with care. The temporal and spatial inhomogeneities in the data sources that went into the construction of this dataset require that any historical analysis of the data is done with caution and an understanding of the limitations of the data. Methods of data compilation varied by data source, but included visual interpretation of hard copy map notation and legends, scanning, digitization, geo-rectification into digital geospatial products, reprojection, and also resampling into a common resolution. To standardize the data onto a common spatial grid, the resampling methodology utilized the centroid of the target 1/4 x 1/4 grid cell as the location to extract the value from the underlying data source.";
    String project "Historical Sea Ice Atlas";
    String publisher_email "janzen@aoos.org";
    String publisher_institution "Alaska Ocean Observing System";
    String publisher_name "Alaska Ocean Observing System";
    String publisher_url "http://www.aoos.org";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 40.125;
    String standard_name_vocabulary "CF-12";
    String summary "Data from a local source.";
    String time_coverage_end "2018-12-15T12:00:00Z";
    String time_coverage_start "1850-01-15T12:00:00Z";
    String title "Data from a local source.";
    Float64 Westernmost_Easting -179.875;
  }
}

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.