Make A Graph

Dataset Title:	X- Experimental Mean, standard deviation, and anomalies (relative to the 15- year baseline, 2006 to 2020) of 15day fractional sea ice and snow cover presence, 4s km Arctic
Institution:	NOAA CoastWatch PolarWatch (Dataset ID: ims4k_15d)
Information:	Summary \| License \| ISO 19115 \| Metadata \| Background \| Data Access Form \| Files

To work correctly, this web page requires that JavaScript be enabled in your browser. Please:
1) Enable JavaScript in your browser:
      • Chrome: "Settings : Advanced : Privacy and security : Site Settings : JavaScript"
      • Firefox: (it should be always on!)"
      • Opera: "Settings : Websites : JavaScript"
      • Safari: "Safari : Preferences : Security : Enable JavaScript"
2) Reload this web page.

Graph Type:
X Axis:
Y Axis:
Color:

Dimensions

Start

Stop

time (UTC)

specify just 1 value →

y (m)

x (m)

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 1.7639424e+9, 1.7639424e+9;
    String axis "T";
    String calendar "proleptic_gregorian";
    String ioos_category "Time";
    String long_name "Midpoint of 15-day period";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  y {
    Float32 _FillValue NaN;
    Float32 actual_range -1.2286e+7, 1.2286e+7;
    String axis "Y";
    String comment "y values are the centers of the grid cells";
    String ioos_category "Location";
    String long_name "y coordinate of projection";
    String standard_name "projection_y_coordinate";
    String units "m";
  }
  x {
    Float32 _FillValue NaN;
    Float32 actual_range -1.2286e+7, 1.2286e+7;
    String axis "X";
    String comment "x values are the centers of the grid cells";
    String ioos_category "Location";
    String long_name "x coordinate of projection";
    String standard_name "projection_x_coordinate";
    String units "m";
  }
  IMS_mean {
    UInt32 _ChunkSizes 1, 512, 512;
    Float32 _FillValue NaN;
    String cell_methods "time: mean";
    Float64 colorBarMaximum 1.0;
    Float64 colorBarMinimum 0.0;
    String colorBarPalette "KT_ice";
    String comment "fraction";
    String grid_mapping "projection";
    String ioos_category "Ice Distribution";
    String long_name "Mean of Snow Ice Presence Fraction";
    String units "1";
  }
  IMS_stdev {
    UInt32 _ChunkSizes 1, 512, 512;
    Float32 _FillValue NaN;
    String cell_methods "time: standard_deviation";
    Float64 colorBarMaximum 1.0;
    Float64 colorBarMinimum 0.0;
    String colorBarPalette "RedWhiteBlue";
    String comment "fraction";
    String grid_mapping "projection";
    String ioos_category "Ice Distribution";
    String long_name "Standard Deviation of Snow Ice Presence Franction";
    String units "1";
  }
  day_count {
    UInt32 _ChunkSizes 1, 512, 512;
    Float64 _FillValue 9223372036854775807;
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String grid_mapping "projection";
    String ioos_category "Statistics";
    String long_name "count of valid days in bi-monthly period";
    String units "1";
  }
  IMS_mean_anom {
    UInt32 _ChunkSizes 1, 512, 512;
    Float64 _FillValue NaN;
    Float64 colorBarMaximum 1.0;
    Float64 colorBarMinimum -1.0;
    String colorBarPalette "RedWhiteBlue";
    String comment "Baseline taken from bs for month-day 11-24, time=2013-11-24.";
    String grid_mapping "projection";
    String ioos_category "Statistics";
    String long_name "IMS_mean anomaly relative to bimonthly 15-year baseline";
    String units "1";
  }
  NC_GLOBAL {
    String _NCProperties "version=2,netcdf=4.9.2,hdf5=1.14.6";
    String acknowledgement "Produced by NOAA PolarWatch with support from NOAA NESIS STAR using USNIC IMS data.";
    String cdm_data_type "Grid";
    String contributor_name "PolarWatch, NOAA CoastWatch, NESDIS STAR, USNIC";
    String contributor_role "Producer, Publisher, Advisor, Originator";
    String Conventions "CF-1.8, COARDS, ACDD-1.3";
    String creator_email "coastwatch.info@noaa.gov";
    String creator_name "PolarWatch, NOAA CoastWatch, NESDIS, STAR, Southwest Fisheries Science Center";
    String creator_type "Institution";
    String creator_url "https://polarwatch.noaa.gov/";
    String date_created "2025-02-24T00:00:00Z";
    Float64 grid_mapping_false_easting 0.0;
    Float64 grid_mapping_false_northing 0.0;
    Float64 grid_mapping_latitude_of_projection_origin 90.0;
    String grid_mapping_name "polar_stereographic";
    String grid_mapping_proj4text "+proj=stere +lat_0=90 +lat_ts=60 +lon_0=-80 +k=1 +x_0=0 +y_0=0 +a=6378137 +b=6356257 +units=m +no_defs";
    Float64 grid_mapping_semi_major_axis 6378137.0;
    Float64 grid_mapping_semi_minor_axis 6356257.0;
    String grid_mapping_spatial_ref "PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"unknown\",SPHEROID[\"unknown\",6378137,291.505347349177]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Polar_Stereographic\"],PARAMETER[\"latitude_of_origin\",60],PARAMETER[\"central_meridian\",-80],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",SOUTH],AXIS[\"Northing\",SOUTH]]";
    Float64 grid_mapping_standard_parallel 60.0;
    Float64 grid_mapping_straight_vertical_longitude_from_pole -80.0;
    String grid_mapping_units "m";
    String history 
"15 Jan 2025: NetCDF files were generated from the IMS NetCDF files; 05 May 2021 20:11Z: NetCDF file generated from file NIC.IMS_v2_201200100_4km.asc
2026-04-24T17:35:21Z (local files)
2026-04-24T17:35:21Z https://polarwatch.noaa.gov/griddap/ims4k_15d.das";
    String infoUrl "https://polarwatch.noaa.gov/erddap/info/ims4k_15d/index.html";
    String institution "NOAA CoastWatch PolarWatch";
    String instrument "See https://nsidc.org/data/g02156 for a list of instruments";
    String keywords "Earth Science > Cryosphere > Sea Ice > Ice Extent, Earth Science > Cryosphere > Snow/Ice > Snow Cover, GEOGRAPHIC REGION > ARCTIC, GEOGRAPHIC REGION > NORTHERN HEMISPHERE, GEOGRAPHIC REGION > POLAR";
    String keywords_vocabulary "NASA Global Change Master Directory (GCMD) Keywords, Version 9.0";
    String license "CC-10";
    String metadata_link "https://polarwatch.noaa.gov/erddap/info/ims4k_15d/index.html";
    String naming_authority "gov.noaa";
    String ncei_template_version "NCEI_NetCDF_Grid_Template_v2.0";
    String platform "Earth Observation Satellites, In Situ Land-based Platforms, In Situ Ocean-based Platforms, Ground Stations, Maps/Charts/Photographs, Models/Analyses";
    String platform_vocabulary "NASA Global Change Master Directory (GCMD) Keywords, Version 9.0";
    String processing_level "NOAA Level 4";
    String product_version "Version 1";
    String project "15 year baseline data of the USNIC Snow and Ice Products";
    String publisher_email "coastwatch.info@noaa.gov";
    String publisher_institution "NOAA CoastWatch PolarWatch";
    String publisher_name "NOAA CoastWatch PolarWatch";
    String publisher_type "Institution";
    String publisher_url "https://polarwatch.noaa.gov";
    String references "https://polarwatch.noaa.gov/erddap/info/ims_snowice_presence_4k/index.html";
    String source "IMS Surface Values Dataset";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary 
"This dataset provides 4 km fractional snow/ice presence 15 day means, standard deviations, and anomalies over the Arctic region. All computations are performed on the native 4 km IMS grid and the mean, standard deviation, and anomaly values are then upscaled to a nominal 1 km grid.
The source data are the daily USNIC IMS 4 km snow and ice product (IMS_Surface_Value), with categorical values from 0 to 4 (0: outside coverage area, 1: open water, 2: land without snow, 3: sea ice or lake ice, 4: snow covered land). For this product, the categories were converted into binary presence values at 4 km resolution: 1 for snow/ice presence, 0 for no snow/ice, and NaN for all other or missing values.
For each year, 15 day composites were computed at 4 km resolution for two periods per month: days 1 - 15 and days 16 - end of month. The 15 year baseline (2006 - 2020) was then produced by calculating the weighted mean of these 15 day composites, accounting for the varying number of valid observations in each time step. Anomalies were computed at 4 km as the difference between each 15-day composite and the corresponding baseline composite.
Following the computation of 15-day means, standard deviations, and anomalies at 4 km resolution, each 4 km pixel was upscaled to 1 km resolution by nearest neighbor replication, where a single 4 km grid cell is expanded into a 4 by 4 block of 1 km pixels.
These data products are intended for preliminary analysis and research applications. For additional information on IMS, please refer to the documentation at: https://nsidc.org/data/g02156/versions/1.";
    String testOutOfDate "now-33days";
    String time_coverage_duration "P15D";
    String time_coverage_end "2025-11-24T00:00:00Z";
    String time_coverage_resolution "P15D";
    String time_coverage_start "2025-11-24T00:00:00Z";
    String title 
"X- Experimental Mean, standard deviation, and anomalies (relative to the 15-year baseline,
2006 to 2020) of 15day fractional sea ice and snow cover presence, 4s km Arctic";
  }
}

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.