Data Set ID:
NISE

Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent, Version 5

The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation.

This is the most recent version of these data.

Version Summary:
  • The NISE snow and sea ice algorithm both use near-real-time brightness temperature observations from the SSMIS instrument on DMSP-F18.
  • The coefficients for the NISE snow algorithm were updated to better match results from DMSP-F13 as a result of an inter-calibration between F13 and F17 during an overlap period from 3/27/2008-3/26/2009 and between F17 and F18 during an overlap period from 7/1/2014-6/30/2015.
  • ESDT metadata was updated to reflect the change in data set version number.

The NISE Version 5 data record begins 12/01/2016. A two-month overlap with NISE Version 4 is planned: 12/01/2016-01/31/2017

COMPREHENSIVE Level of Service

Data: Data integrity and usability verified; data customization services available for select data

Documentation: Key metadata and comprehensive user guide available

User Support: Assistance with data access and usage; guidance on use of data in tools and data customization services

See All Level of Service Details

Parameter(s):
  • SEA ICE > SEA ICE CONCENTRATION
  • SNOW/ICE > SNOW COVER
Data Format(s):
  • HDF-EOS
Spatial Coverage:
N: 90, 
S: -90, 
E: 180, 
W: -180
Platform(s):DMSP 5D-2/F13, DMSP 5D-3/F17, DMSP 5D-3/F18
Spatial Resolution:
  • 25 km x 25 km
Sensor(s):SSM/I, SSMIS
Temporal Coverage:
  • 1 December 2016
Version(s):V5
Temporal Resolution1 dayMetadata XML:View Metadata Record
Data Contributor(s):J Stewart, Mary Brodzik

Geographic Coverage

Other Access Options

Other Access Options

Close

As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.

Brodzik, M. J. and J. S. Stewart. 2016. Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent, Version 5. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/3KB2JPLFPK3R. [Date Accessed].
Created: 
8 November 2019
Last modified: 
3 December 2019
This documentation applies to all major versions of the Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent (NISE) product. Major versions correspond to different Defense Meteorological Satellite Program (DMSP) missions and unique platform / sensor combinations:
NISE Version 2 data derive from the Special Sensor Microwave Imager (SSM/I) sensor aboard the DMSP-F13 satellite.
NISE Version 4 data derive from the Special Sensor Microwave Imager/Sounder (SSMIS) sensor aboard the DMSP-F17 satellite.
NISE Version 5 data derive from the Special Sensor Microwave Imager/Sounder (SSMIS) sensor aboard the DMSP-F18 satellite.

Data Description

Parameters

The main parameters for this data set are snow extent (the presence or absence of snow) and sea ice concentration (measured as a percentage).

File Information

Format

Data are provided in HDF-EOS format. HDF-EOS (Hierarchical Data Format - Earth Observing System) is a self-describing file format based on HDF that was developed specifically for distributing and archiving data collected by NASA EOS satellites. For more information, visit the HDF-EOS Tools and Information Center.

Two JPEG browse images, one for the Northern Hemisphere and one for the Southern Hemisphere, are available for each day. 

Extensible Markup Language (.xml) files with associated metadata are also provided.

File Contents

Daily data are provided in a single HDF-EOS file containing two data fields, extent and age, for both the Northern and Southern Hemispheres (Table 1). Extent and age values are stored as binary arrays of unsigned 1-byte (8-bit) data ranging in value from 0 to 255. 

Table 1. HDF-EOS File Descriptions
Data Field Description Possible Values
Extent The snow coverage and sea ice concentration of all pixels in the study areas; coastal pixels are also identified

0: Snow-free land
1-100: Sea ice concentration (%)
101: Permanent ice coverage (Greenland, Antarctica)
102: Not used
103: Pixel has snow
104-251: Not used
252: Coastal pixel (unable to reliably apply microwave algorithms)
253: Pixel suspected of having ice
254: Corner points (undefined)
255: Ocean

Age The age of the input data relative to the data file; the difference between the day of acquisition for the input data and the day of production for the HDF-EOS file 0-254: age (in days) before the date of the data file
255: Filler value for corner points (off-Earth) and undetermined data pixels

Sample Data

Figures 1 and 2 depict data from 03 February 2002 for the Northern and Southern Hemispheres, respectively.

Figure 1. Sample data record for NISE_SSMIF13-20020203_N.GIF.
Figure 2. Sample data record for NISE_SSMIF13-20020203_S.GIF.

Directory Structure

NISE Version 2 data can be found at the following location: https://n5eil01u.ecs.nsidc.org/OTHR/NISE.002/

NISE Version 4 data can be found at the following location: https://n5eil01u.ecs.nsidc.org/OTHR/NISE.004/

NISE Version 5 data can be found at the following location: https://n5eil01u.ecs.nsidc.org/OTHR/NISE.005/

Naming Convention

Files are named according to the following conventions and as described in Table 2.
NISE_SSMIF##_yyyymmdd.h.ext
NISE_SSMISF##_yyyymmdd.ext
NISE_SSMISF##_yyyymmdd.ext.xml

Table 2. Description of File Name Variables
Variable
Description
NISE
Near-real-time Ice and Snow Extent
SSMIS
Special Sensor Microwave Imager/Sounder: sensor 
F##
DMSP Platform: F18
yyyy
4-digit year
mm
2-digit month of year
dd
2-digit day of month
h
Hemisphere (1: Northern, 2: Southern)
.ext
.ext indicates file extension type:
File Extension
Description
.jpg
Browse images of the northern and southern hemispheres
.HDFEOS
Data file in HDF-EOS format
.xml
Granule metadata file in Extensible Markup Language (XML)

Example file names:
NISE_SSMISF18_20161223.1.jpg
NISE_SSMISF18_20161223.2.jpg
NISE_SSMISF18_20161223.HDFEOS
NISE_SSMISF18_20161223.HDFEOS.xml

Spatial Information

Coverage

Spatial coverage is global except for a gap of three degrees latitude (87 to 90 degrees) from each pole. The application of monthly-varying masks limits the mapped extent of snow and sea ice in both hemispheres (see the Data Acquisition and Processing section of this document for more details). Spatial coverage is shown in Figures 3 and 4.

Figure 3. Northern Hemisphere
Figure 4. Southern Hemisphere

Spatial Resolution

25 km

Geolocation

Sea ice concentration and snow extent maps are provided in the 25 km Northern Hemisphere and Southern Hemisphere Equal-Area Scalable Earth Grids (EASE-Grid North and EASE-Grid South). Tables 3 and 4 provide more details. 

Table 3. Geolocation Details
Geographic coordinate system
Unspecified datum based upon the International 1924 Authalic Sphere
Unspecified datum based upon the International 1924 Authalic Sphere
Projected coordinate system
NSIDC EASE-Grid North
NSIDC EASE-Grid South
Longitude of true origin 0 0
Latitude of true origin 90 90
Scale factor at longitude of true origin N/A N/A
Datum Not specified based on the International 1924 Authalic Sphere Not specified based on the International 1924 Authalic Sphere
Ellipsoid/spheroid
International 1924 Authalic Sphere
International 1924 Authalic Sphere
Units meter meter
False easting 0 0
False northing 0 0
EPSG code 3408 3409
PROJ4 string
+proj=laea +lat_0=90 +lon_0=0 +x_0=0 +y_0=0 +a=6371228 +b=6371228 +units=m +no_defs 
+proj=laea +lat_0=-90 +lon_0=0 +x_0=0 +y_0=0 +a=6371228 +b=6371228 +units=m +no_defs 
Reference http://epsg.io/3408 http://epsg.io/3409

Table 4. Grid Details
Projectioned Coorindate System NSIDC EASE-Grid North NSIDC EASE-Grid South
Grid cell size (x, y pixel dimensions) 25,067.53 m x 25,067.53 m 25,067.53 m x 25,067.53 m
Number of rows 721 721
Number of columns 721 721
Geolocated lower left point in grid N/A, off the Earth N/A, off the Earth
Nominal gridded resolution 25 km 25 km
Grid rotation N/A N/A
ulxmap – x-axis map coordinate of the center of the upper-left pixel (XLLCORNER for ASCII data) -9036842.76 -9036842.76
ulymap – y-axis map coordinate of the center of the upper-left pixel (YLLCORNER for ASCII data) 9036842.76 9036842.76

Temporal Information

Coverage

For each 24-hour period, NISE is updated using the most recent input data for a given grid cell. The frequency of input updates varies as a function of latitude. Grid cells representing latitudes above 55° N or below 55° S, for which multiple satellite passes are available each day, are usually updated every 24 hours. Due to the orbital geometry of the DMSP satellites and the swath width of the SSM/I and SSMIS sensors, the time interval between successive observations at low-latitude locations (20° S to 20° N) can be up to five days (Hollinger et al. 1987). Given the absence of sea ice and very limited snow extent at these low latitudes, the infrequent updates were deemed acceptable. 

Occassionally, input data are unavailable or unobtainable. When this happens, the age values at any location may be older than five days. An age grid indicates the number of days since each grid cell was last updated. 

Resolution

Daily. 

Data Acquisition and Processing

Background

NSIDC modified the snow extent mapping algorithm in March 2002, based primarily on a study by Armstrong and Brodzik (2002). This study indicated that horizontal-polarization-based algorithms underestimate snow extent during early winter but provide the best overall estimates of snow extent at the continental to hemispheric scale through the period of maximum snow extent and into the melt season. Vertical-polarization-based algorithms (Goodison, 1989) provide similar results but with a consistent tendency to falsely identify snow-free desert soils and/or frozen ground as snow-covered.

Acquisition

Table 5. Input Data Sources
NISE Version Sensor Satellite
5 Special Sensor Microwave Imager/Sounder (SSMIS) DMSP-F18
4 Special Sensor Microwave Imager/Sounder (SSMIS) DMSP-F17
3 N/A, no product available
2 Special Sensor Microwave Imager (SSM/I) DMSP-F13
1 N/A, no product available

Processing

The process to derive snow extent from passive microwave satellite data is constantly undergoing revision and improvement at NSIDC. All algorithms are subject to change without warning. Changes are intended to improve the snow and ice mapping capabilities of NSIDC. 

Sea ice concentrations are derived from all SSM/I or SSMIS data from a given 24-hour period, binned to the EASE-Grid using a "drop-in-the-bucket" interpolation method. Derivations use the NASA Team Sea Ice (first-year ice plus multi-year ice) Algorithm, as described in Cavalieri et al. (1992). For more details, see the NASA Team Sea Ice Algorithm web page.

Snow extent is also derived from passive microwave satellite data. Snow extent is mapped using an algorithm developed for Scanning Multichannel Microwave Radiometer (SMMR) data, as described in Chang, Foster, and Hall (1987), and modified for use with SSM/I and SSMIS data, as described in Armstrong and Brodzik (2001). Snow extent derivations use Brightness Temperature (Tb) measurements from the satellite's morning pass as inputs and rely on nearest neighbor interpolation.

A land/ocean/ice cap mask is used to determine which interpolation algorithm (sea ice concentration or snow extent) is used for each pixel. Additional climatology masks are used to identify spurious data, primarily due to weather affecting passive microwave data collection. Currently, the sea ice climatology is a monthly ocean mask derived from historical SMMR  (1979-1987) and SSM/I (1987-2003) data. The snow extent climatology for the Northern Hemisphere is a monthly mask derived from the Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent (October 1966 to May 2005) data. The original Southern Hemisphere snow climatology was a static file that served as a spatial representation of the expected snow line in the Andes, relying on a function of latitude and elevation to determine snow extent (Schwerdtfeger, 1976); NISE files dated before 30 June 2005 use the original mask. Beginning 01 July 2005, the snow extent climatology for the Southern Hemisphere is a monthly mask derived from SSM/I period (1987 to 2003) data.

Northern Hemisphere Monthly Snow Extent Climatologies

January February March April
January snow climatology of the Northern hemispere February snow climatology of the Northern hemispere March snow climatology of the Northern hemispere April snow climatology of the Northern hemispere
May June July August
May snow climatology of the Northern hemispere June snow climatology of the Northern hemispere July snow climatology of the Northern hemispere August snow climatology of the Northern hemispere
September October November December
September snow climatology of the Northern hemispere October snow climatology of the Northern hemispere November snow climatology of the Northern hemispere December snow climatology of the Northern hemispere

Southern Hemisphere Monthly Snow Extent Climatologies

January February March April
January snow climatology of the Southern hemispere February snow climatology of the Southern hemispere March snow climatology of the Southern hemispere April snow climatology of the Southern hemispere
May June July August
May snow climatology of the Southern hemispere June snow climatology of the Southern hemispere July snow climatology of the Southern hemispere August snow climatology of the Southern hemispere
September October November December
September snow climatology of the Southern hemispere October snow climatology of the Southern hemispere November snow climatology of the Southern hemispere December snow climatology of the Southern hemispere

Northern Hemisphere Monthly Sea Ice Climatologies

January - June July - December
January through June ice climatology of the Northern hemispere July through December ice climatology of the Northern hemispere

Southern Hemisphere Monthly Sea Ice Climatologies

January - June July - December
January through June ice climatology of the Southern hemispere July through December ice climatology of the Southern hemispere

Quality, Errors, and Limitations

Quality Control

Quality control for this product is performed by the National Environmental Satellite, Data, and Information System (NESDIS) when converting temperature data records to Tb. NSIDC visually inspects daily browse images.

Error Sources

Physical conditions affecting the accuracy of the sea ice concentration algorithm include atmospheric water content, ocean roughening and spray, presence of thin ice, and formation of melt ponds on the sea ice. Errors become greatest during mid- to late-summer, resulting primarily from melt ponds on the ice surface, and also from atmospheric- and weather-related effects over open ocean. To minimize the error over open ocean, a filter is applied to detect these atmospheric effects. False ice concentration estimates may also occur along coastlines due to mixed pixels. Mixed pixels contain signals from both land and water in unknown proportions. For the NISE product, such errors are minimized by designating these pixels as coastal pixels (see Table 1 for more details).

The presence of dense coniferous and deciduous forests presents problems for mapping snow extent because the vegetation canopy obscures snow on the ground. The best conditions for accurate snow extent mapping are in areas of little or no vegetation, such as prairies and tundra. In all areas, the snow extent mapping algorithm only identifies a grid cell as snow-covered when it has a computed snow depth greater than 2.5 cm.

Known problems with SSM/I and SSMIS data include occasional missing data, mislocated scan lines, and out-of-bounds data values.  Such errors usually result in no new NISE observations at affected locations on those days, so the most recent observation at that location is used instead.  The age field indicates when the pixel's latest observation was made.

Errors may also be introduced when the Tb data that the sea ice concentration and snow extent algorithms use become unreliable. For instance, on 5 April 2016 a solar panel on board the DMSP F-17 satellite changed position, and the integrity of the vertically polarized 37 GHz channel (37V) channel of the SSMIS sensor was affected. Since this is one of the primary channels used in the NISE processing, NISE Version 4 data from this time onward should be used with caution. Largely as a result of this incident, NISE began using data from DMSP-F18 on Nov 1, 2016 (start of Version 5).

Confidence Level/Accuracy Judgment

Sea ice concentration estimates are accurate to within approximately 5% in most areas for the most of the year. Armstrong and Brodzik (2001) demonstrated that the snow extent algorithm can provide daily global snow extent maps with an accuracy of approximately 50 km, except in areas of wet snow or dense forest cover. When the snow is wet -- when liquid water is present on the snow grain surface -- the snow pack becomes predominantly an emitter and much of the scattered portion of the ground signal is lost, greatly limiting algorithm accuracy. To reduce the frequency of observations over wet snow, only data from the early morning (descending) orbits are used as input to the algorithm.

NSIDC performed some inter-calibration between F17 and F18 data (NISE Version 4 and NISE Version 5) and found that the sea ice algorithm coefficients yield similar ice extents during an overlap period between 01 March 2015 and 29 February 2016. Thus, F18 sea ice estimates should be reasonably consistent with F17 estimates, although differences of up to approximately 28,000 sq km may be possible in daily total extents. The differences are primarily near the ice edge, where shifts of one to two grid cells (25-50 km) may be seen.

Version 5 of NISE incorporates a spillover correction (Cavalieri et al., 1999) that reduces or eliminates sea ice concentrations near coastlines when there is open water present. Due to the relatively large microwave footprint size, passive microwave emissions from adjacent land masses contaminate the signal for coastal pixels, producing spurious sea ice extents in coastal regions that are actually void of sea ice, especially during summer months.

Applications and Limitations

This data set was originally designed to provide NASA EOS researchers with near-real-time daily, global snow extent and sea ice concentration data. The following NASA EOS instrument teams use the NISE data to generate their products:

  • Multi-angle Imaging SpectroRadiometer (MISR). The MISR instrument is part of a suite of sensors on NASA's EOS Terra satellite. The NISE product will be used as ancillary data for the MISR Top-of-Atmosphere/Cloud product, which requires near-real-time daily, global snow and sea ice extent data.
  • Clouds and the Earth's Radiant Energy System (CERES). CERES requires both daily and monthly averaged global snow and ice extent maps for several of their Earth radiation budget products. CERES is currently part of the Tropical Rainfall Measuring Mission (TRMM) aboard the EOS Terra platform.
  • Moderate Resolution Imaging Spectroradiometer (MODIS). The MODIS Atmosphere Discipline Group uses NISE data to produce their cloud mask.

NSIDC anticipates additional use of the NISE product to:

  • Produce sea surface temperature maps from the MODIS instrument.
  • Provide ancillary data to the Global Land Ice Monitoring from Space project, which uses the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).
  • Compare with Advanced Microwave Scanning Radiometer (AMSR) sea ice concentrations and snow extent maps.

Instrumentation

Description

The NISE Version 5 and NISE Version 4 products use passive microwave data from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program (DMSP) F18 and F17 satellites, respectively.

The NISE Version 2 product uses passive microwave data from the Special Sensor Microwave Imager (SSM/I) onboard the DMSP-F13 satellite. 

The SSM/I instrument is a seven-channel, four-frequency, orthogonally polarized, passive microwave radiometric system. The instrument measures combined atmosphere and surface radiances at 19.3 GHz, 22.2 GHz, 37.0 GHz, and 85.5 GHz frequencies. Horizontal and vertical polarization measurements are available.

The SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument. It is a conically-scanning passive microwave radiometer that harnesses the imaging capabilities of SSM/I (with coincident channels except that 91.7 GHz replaces 85.5 GHz) and the sounding capabilities of the DMSP SSM/T-1 temperature and SSM/T-2 water vapor sounders. The SSMIS sensor has a swath width of 1700 km. 

Refer to the SMMR, SSM/I, and SSMIS Sensors page for more details.

Software and Tools

The EASE Grid Data Tools page provides files containing latitude and longitude values for each EASE-Grid North and EASE-Grid South grid cell. Fortran and C source code are also available for converting grid cell locations to latitude and longitude values, and vice-versa. An Interactive Data Language (IDL) program is available for converting latitude and longitude values to grid column and row coordinates.

The NSIDC Hierarchical Data Format - Earth Observing System (HDF-EOS) web site provides information about the HDF-EOS format, tools that extract HDF-EOS objects into ASCII or flat binary formats, and links to other HDF-EOS resources. In addition, example code for access and visualization of NISE data in NCL, Matlab, and IDL is provided on the HDF-EOS Comprehensive Examples page.

Version History

Table 6 outlines the processing and algorithm history for this product.

Table 6. Description of Significant Revisions
Version Date Implemented
(yyyy-mm-dd)
Description
V5
2016-12-02
  • The NISE snow and sea ice algorithms both use near-real-time brightness temperature observations from the SSMIS instrument on DMSP-F18.
  • The coefficients for the NISE snow algorithm were updated to better match results from DMSP-F13 as a result of an inter-calibration between F13 and F17 during an overlap period from 3/27/2008-3/26/2009 and between F17 and F18 during an overlap period from 7/1/2014-6/30/2015.
  • ESDT metadata was updated to reflect the change in the data set version.
  • The NISE Version 5 data record begins 12/01/2016.  A two-month overlap with NISE Version 4 is planned: 12/01/2016-01/31/2017
V4.1
2009-10-12
  • Updated metadata field values for PGEVERSION (collection level remains Version 4)
  • Removed 15% sea ice concentration threshold that assigned pixels with a sea ice concentration of <15% as ocean (data value of 255)
  • Reprocessed NISE Version 4 to include sea ice concentration values of 1-14%
This revision is an update to NISE Version 4. All Version 4 data (17 August 2009 - present) have been reprocessed with this system.
V4
2009-08-28
  • Changed input processing stream from the SSM/I instrument on board the DMSP-F13 satellite to the SSMIS instrument on DMSP-F17
  • Changed input processing stream from NASA GHRC to NOAA CLASS
  • Conducted inter-calibration between F13 and F17 to correct for sensor differences using an overlap period of 28 March 2008 - 28 March 2009; adjusted tie points for the sea ice component of NISE; adjusted the snow extent algorithm component of NISE.
  • Updated metadata field values for VERSIONID, LOCALVERSIONID and PGEVERSION
  • Changed definition of one day from orbit boundaries to UTC time; thus, changed algorithm to determine one day of input data using midnight to midnight UTC, rather than orbit boundaries. Previous versions of NISE determined the beginning of a day with the first complete orbit past midnight, and completed the day with the last orbit prior to midnight.
  • Implemented a land-to-ocean spillover correction to reduce spurious ice near shorelines (Cavalieri et al., 1999)
This revision is designated NISE Version 4. All data from 17 August 2009 to the present have been processed with this system. The NISE Version 2 product from F13 has been produced through 31 August 2009. (No NISE Version 3 product is available).
V3
N/A
NA; no NISE Version 3 product is available.
V2.3
2008-10-06
Ported NISE processing system from SGI to linux. No significant changes in output.
V2.2
2006-04-27
New Northern Hemisphere snow climatologies with data from 1966-2005, and new Northern and Southern Hemisphere ice climatologies with data from 1979-2003.
V2.1
2005-07-01
Static Southern Hemisphere snow climatology limiting possible snow to the Andes region was replaced with a monthly climatology that now includes the Andes and New Zealand.
V2
2005-06-10
Data from the start of the SSM/I F13 mission (04 May 1995) to 31 December 1999 were processed to NISE Version 2.
V1.11
2005-04-25
  • A new LOCI mask was used, based on the Boston University (BU)-MODIS land cover data set
  • Updated HDF libraries from HDF 4.1r1 to 4.1r3
  • Updated metadata field values for VERSIONID, LOCALVERSIONID and PGEVERSION
  • Corrected error in browse images that was painting pixels blue at the edge of the snow pack
This revision is designated NISE Version 2. All data from 01 January 2000 to present have been reprocessed with this system. NISE Version 1 files will be deleted at a future date.
V1.10
2003-09-25
  • New, improved LOCI (land-ocean-coastline-ice) masks
  • Brightness temperature interpolation method for land areas (snow algorithm) changed from nearest neighbor to inverse distance squared.
V1.9
2002-03-20
New snow extent algorithm
V1.8
2000-06-01
New ice climatologies through 1999
V1.7
2000-03-02
Changed metadata field for PRODUCTIONDATETIME from local time to UTC
V1.6
2000-01-07
  • Second modifications for managing data in the EOSDIS Core System (ECS)
  • Reprocessed files beginning with 1999-12-29 to be Y2K compatible
  • Created HDF browse file
  • Added metadata to HDF-EOS files, starting with 1999-12-29
V1.5
1999-10-07
First modifications for managing data in the EOSDIS Core System (ECS)
V1.4
1999-04-09
Fixed geolocation errors in NISE-to-HDFEOS program
V1.3
1998-08-06
  • New ice climatologies developed at NSIDC that use SMMR data from 1979-87 and SSM/I data through 1996
  • New land-ocean-coastlines-ice (LOCI) masks that fix isolated coastline pixel problems
V1.2
1998-02-01
  • New ice climatologies developed at NSIDC instead of GSFC
  • New Southern Hemisphere snow climatologies derived from altitude and latitude freeze-line information
V1.1
1997-12-31
Fixed 2-digit year problem in time for new year
V1
1997-10-31
Initial operational release

Related Data Sets

Sea Ice Products

Sea Ice Data at NSIDC

Snow Extent Data at NSIDC

MODIS Snow Cover Data at NSIDC

Contacts and Acknowledgments

Contacts

Mary Jo Brodzik
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, Colorado USA 80309-0449

J. Scott Stewart
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, Colorado USA 80309-0449

Acknowledgments

NASA provided funding for the production of this data set through the NSIDC Distributed Active Archive Center (DAAC).

References

Armstrong, R. L. and M. J. Brodzik. 2001. Recent Northern Hemisphere Snow Extent: A Comparison of Data Derived from Visible and Microwave Satellite Sensors. Geophysical Research Letters 28(19): 3673-3676. https://doi.org/10.1029/2000GL012556

Armstrong, R. L. and M. J. Brodzik. 2002. Hemispheric-Scale Comparison and Evaluation of Passive-Microwave Snow Algorithms. Annals of Glaciology 34: 38-44. https://doi.org/10.3189/172756402781817428

Cavalieri, D. J., C. l. Parkinson, P. Gloersen, J. C. Comiso, and H. J. Zwally. 1999. Deriving Long-Term Time Series of Sea Ice Cover from Satellite Passive-Microwave Multisensor Data Sets. Journal of Geophysical Research 104(7): 15,803-15, 814. https://doi.org/10.1029/1999JC900081

Cavalieri, D. J., J. Crawford, M. Drinkwater, W. J. Emery, D. T. Eppler, L. D. Farmer, M. Goodberlet, R. Jentz, A. Milman, C. Morris, R. Onstott, A. Schweiger, R. Shuchman, K. Steffen, C. T. Swift, C. Wackerman, and R. L. Weaver. 1992. NASA Sea Ice Validation Program for the DMSP SSM/I: Final Report. NASA Technical Memorandum 104559. National Aeronautics and Space Administration, Washington, D. C. 126 pages.

Chang, A. T. C., J. L. Foster, and D. K. Hall. 1987. Nimbus-7 SMMR Derived Global Snow Cover Parameters. Annals of Glaciology 9: 39-44. https://doi.org/10.3189/S0260305500200736

Goodison, B. E. 1989. Determination of Areal Snow Water Equivalent on the Canadian Prairies Using Passive Microwave Satellite Data. IGARRS '89 Proceedings 3:1243-6.

Hollinger, J . P., R. C. Lo, G . A. Poe, R. Savage, and J . L. Peirce, 1987. Special Sensor Microwave/Imager User's Guide. Washington, D.C.: Naval Research Laboratory.

Schwerdtfeger, W. 1976. Climate of Central and South America. World Survey of Climatology 12. New York: Elsevier Scientific Publishing.

No technical references available for this data set.

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Do you have code for reading HDF data files into MATLAB, IDL, Python, or NCL?
The HDF Group has example code for access and visualization of MODIS, GLAS HDF5, AMSR-E, and NISE data in MATLAB, IDL, Python, and NCL.

How To

How do I access data using OPeNDAP?
Data can be programmatically accessed using NSIDC’s OPeNDAP Hyrax server, allowing you to reformat and subset data based on parameter and array index. For more information on OPeNDAP, including supported data sets and known issues, please see our OPeNDAP documentation: ... read more
Programmatically access data using spatial and temporal filters
This article provides a step-by-step getting started guide to utilizing an Application Programming Interface, or API, for programmatic access to data from the NSIDC Distributed Active Archive Center (DAAC) based on spatial and temporal filters. Programmatic access is provided via an HTTPS URL... read more