Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent, Version 4

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.

Table of Contents

  1. Detailed Data Description
  2. Data Access and Tools
  3. Data Acquisition and Processing
  4. References and Related Publications
  5. Contacts and Acknowledgments
  6. Document Information

Citing These Data

We kindly request that you cite the use of this data set in a publication using the following citation example. For more information, see our Use and Copyright Web page.

Nolin, A., R. L. Armstrong, and J. Maslanik. 1998. Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent. Version 4. [indicate subset used]. Boulder, Colorado USA: NASA DAAC at the National Snow and Ice Data Center.

Overview

Platforms

DMSP-F13, -F17

Sensors

SSMIS

Spatial Coverage

Global

Spatial Resolution

25 km

Temporal Coverage

Approximately previous two weeks

Temporal Resolution

Daily

Parameters

Snow extent
Sea ice concentration
Age of input data
Coastal pixels

Data Format

HDF-EOS

Metadata Access

View Metadata Record

Version

V4. See the Version History section of this document for version information.

Get Data

FTP | Other Data Access Options

1. Detailed Data Description

Format

The NISE product is updated daily using the best available data from the past five days. Data are in Hierarchical Data Format - Earth Observing System (HDF-EOS) format and browse files are in GIF and HDF formats. HDF-EOS data files are available from 04 May 1995 through the present. Data in both formats are updated daily.

Daily data are provided in a single HDF-EOS file containing four grid objects: one data grid and one age grid each for both the Northern and Southern hemispheres. The data grids contain snow extent, sea ice concentration, and coastal (mixed) pixels. The age grids contain the age of input data in days (from day of data acquisition to map production) relative to the date of the daily file. Values are stored in the data and age grids as binary arrays of unsigned 1-byte (8-bit) data ranging in value from 0 to 255.

File Naming Convention

Files are named according to the following conventions and as described in Table 1:

NISE_SSMIF##_yyyymmdd_.ext
NISE_SSMISF##_yyyymmdd_.ext

Where:

Table 1. Description of File Name Variables
Variable Description
NISE Near-real-time Ice and Snow Extent
SSMI Special Sensor Microwave Imager: sensor
SSMIS Special Sensor Microwave Imager/Sounder: sensor
F## DMSP Platform: F13, F17
yyyy 4-digit year
mm 2-digit month of year
dd 2-digit day of month
.ext

.ext indicates file extension type where:

File Extension Description
H.GIF
Hemisphere-specific browse image in GIF format (N.GIF: Northern Hemisphere GIF Browse Image; S.GIF: Southern Hemisphere GIF Browse Image)
.HDF
Browse images in HDF format (includes both Northern and Southern Hemispheres)
.HDFEOS
Data file in HDF-EOS format
.met
Metadata file in Object Description Language (ODL)

Examples:
NISE_SSMIF13_20080101.HDFEOS
NISE_SSMISF17_20090801.HDFEOS

File Size

HDF-EOS data files: 2.1 MB each
HDF browse files: 156 KB each
Metadata files: 6 KB each
Northern Hemisphere GIF browse files: 49 KB each
Southern Hemisphere GIF browse files: 37 KB each

Spatial Coverage

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

Spatial Coverage Maps

Figure 3. Northern Hemisphere

Figure 4. Southern Hemisphere

Spatial Resolution

The spatial resolution for this data set is 25 km.

Projection and Grid Description

Sea ice concentration and snow extent maps are provided in two azimuthal, equal-area projections: the Southern Hemisphere 25 km low resolution (SL, indicating Southern Low) and Northern Hemisphere 25 km low resolution (NL, SL, indicating Northern Low) Equal-Area Scalable Earth-Grids (EASE-Grids). See All About EASE-Grid for more information about the equal-area projections used for this product. Grids are 721 columns by 721 rows. The respective pole is aligned with the center of the pixel at the center of the grid.

Temporal Coverage

For each 24-hour period, NISE is updated using the most recent data for a given grid cell. The frequency of updates varies as a function of latitude. Grid cells representing latitudes above 55 degrees or below -55 degrees, for which multiple satellite passes are available each day, are usually updated every 24 hours. Due to the orbital geometry of the DMSP satellite and the swath width of the SSM/I sensor, the time interval between successive observations at low-latitude locations (-20 to 20 degrees) can be up to five days (Hollinger et al. 1987). Problems are not anticipated with this low-update frequency given the absence of sea ice and very limited snow extent at these low latitudes. During occasional periods when input data are unavailable or unobtainable, the NISE product 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 (see Data Acquisition Methods).

Temporal Resolution

Each HDF-EOS file represents daily data comprising the last available snow extent or sea ice concentration data for each pixel.

Parameter or Variable

Parameter Description

Table 2 lists data values and the following parameters used in the data grids:

  • Snow extent: Presence or absence of snow
  • Sea ice concentration: Sea ice concentration (percent)
  • Coastal pixel: 100-km-wide area of grid cells comprising the mixed pixel regions along a coastline
Table 2. Data Grids
Data Value Parameter
0 Snow-free land
1-100 Sea ice concentration (%)
101 Permanent ice (Greenland, Antarctica)
102 Not used
103 Snow
104-251 Not used
252 Mixed pixels at coastlines
(unable to reliably apply microwave algorithms)
253 Suspect ice value
254 Corners (undefined)
255 Ocean

Table 3 lists data values and the following parameter used in the age grids:

  • Age: Age of input data used to derive a corresponding data grid value, in days relative to the date of the daily file
Table 3. Age Grids
Data Value Parameter
0-254 Age in days since date of file
255 Filler value for corners (off-Earth) and undetermined data pixels

Parameter Range

Cell values range from 0 to 255 for snow extent, sea ice concentration, and age grids.

Sample Data Records

Figures 1 and 2 show snow extent, sea ice concentration, and age layers for 03 February 2002 for the Northern and Southern Hemispeheres, respectively.

Image of snow extent, sea ice concentration, and age layers for the Northern Hemisphere for 03 February 2002.

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

Image of snow extent, sea ice concentration, and age layers for the Northern Hemisphere for 03 February 2002.

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

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.

The snow extent mapping algorithm maps a grid cell as snow-covered when it has a computed snow depth greater than 2.5 cm.

The presence of dense coniferous and deciduous forests presents problems for mapping snow extent with SSMIS because the vegetation canopy obscures snow on the ground. The best conditions for accurate snow extent mapping using SSMIS are in areas of little or no vegetation such as in the prairies and tundra.

For the NISE Version 2 data product, known problems typical of SSM/I data, which the Global Hydrology Resource Center (GHRC) attempted to correct, include occasional missing data, mislocated scan lines, and out-of-bounds data values.

Quality Assessment

Quality control for this product is performed by the National Environmental Satellite, Data, and Information System (NESDIS) when converting Temperature Data Records to brightness temperatures. NSIDC visually inspects daily NISE browse files.

Confidence Level/Accuracy Judgment

The accuracy of the sea ice concentration estimates is within approximately five percent in most areas during the majority of the year. For the sea ice component of the current NISE Version 4 product, NSIDC has done preliminary inter-calibration between F13 and F17 to correct for the sensor differences using an overlap period of 28 March 2008 through 28 March 2009. F17 sea ice estimates should be reasonably consistent with F13 estimates, although differences of 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 4 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. In addition, due to slight sensor differences, NISE Version 4 sometimes shows a higher number of pixels with a sea ice concentration value of 100 percent than the NISE Version 2 product. A one-year overlap period of NISE Version 4 data is available upon request for comparison with NISE Version 2 data. Please contact NSIDC User Services to access these data.

Armstrong and Brodzik (2001) demonstrate that the snow extent algorithm can provide daily global snow extent maps to an accuracy of approximately 50 km, except in areas of wet snow or dense forest cover. In the snow extent product, 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. The Version 4 snow extent algorithm uses F17 brightness temperatures that have been adjusted with a linear regression to F13 brightness temperatures over selected stable targets for the period of 28 March 2008 to 28 March 2009.

2. Data Access and Tools

Get Data

Direct Data Access

Table 4. Direct Data Access and Data Registration Details
Direct Access Data Registration Details
FTP Important: Whereas the Other Data Access Options listed in Table 5 either automatically register a user through the data ordering process or through the creation of a user account, accessing the data via FTP provides an Optional Registration Form for the user to complete. Since registered users receive e-mail notification regarding important product changes, data users are encouraged to register for the data.

Other Data Access Options

Users may also obtain NISE data by searching and ordering via Reverb, or by subscription. Table 5 lists the Web site link to each data source and also gives an explanation as to what each data source offers.

Table 5. Other Data Access Options
Other Options Description of Other Options
Reverb | ECHO Search and discover data from NSIDC and other Earth Observing System Data and Information System (EOSDIS) data centers. Designed to work with ECHO, the Earth Observing System (EOS) Clearing House, Reverb also offers functions such as parameter and spatial subsetting.
Subscription Subscribe to have new EOS data sets automatically sent via FTP to a local server, or staged on NSIDC's FTP site for FTP pull download.

Software and Tools

The EASE-Grid Geolocation Tools Web page provides files containing arrays of latitude and longitude values for each grid cell. Fortran and C source code are 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.

NSIDC's 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 Web page.

3. Data Acquisition and Processing

Sensor or Instrument Description

NSIDC creates the current NISE product using passive microwave data from the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F17 satellite. The SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument.

The SSMIS sensor is a conically-scanning passive microwave radiometer that harnesses the imaging and sounding capabilities of three previous DMSP microwave sensors, including the SSM/I, the SSM/T-1 temperature sounder, and the SSMI/T-2 moisture sounder. The SSMIS sensor measures microwave energy at 24 frequencies from 19 to 183 GHz with a swath width of 1700 km. Refer to the SSMIS Instrument Description Web page for more details.

Data Acquisition Methods

The Fleet Numerical Meteorology and Oceanography Center (FNMOC) first receives SSMIS raw antenna temperatures. Data are then sent to NESDIS where they are processed into swath brightness temperatures (TBs). NSIDC obtains these swath TBs via FTP once per day from the NESDIS Comprehensive Large Array-data Stewardship System (CLASS), typically within two to four days of the satellite overpass.

The NISE product provides a best estimate of current ice and snow conditions based on information and algorithms available to NSIDC at the time the product is created. If new input data from CLASS are unavailable or unobtainable, then NSIDC has no new information with which to update the NISE product. The current day's data layers will be identical to those of the previous day, and the age layers will be incremented by one day.

Problems obtaining the input data are usually resolved within one business day; however, if new input data are unavailable or unobtainable for five days in a row, NISE data production is halted until the problem is resolved. The NISE product is then reprocessed from the beginning of the interruption, and the new data are released.

Data Sources

NISE Version 2 data originate from the SSM/I sensor onboard the DMSP-F13 satellite, and NISE Version 4 data originate from SSMIS onboard the DMSP-F17 satellite. Note: No NISE Version 3 product is available.

Derivation Techniques and Algorithms

All algorithms are subject to change in order to provide the best possible snow and ice mapping capabilities. Snow extent derived from passive microwave satellite data is a product that is constantly undergoing revision and improvement at NSIDC.

Sea ice concentration percentage for the NISE Version 4 product is derived using the NASA Team total sea ice (first year ice plus multiyear ice) algorithm (Cavalieri et al. 1992). For sea ice, all SSMIS (19, 22, and 37) GHz data from a given 24-hour period are binned to the EASE-Grid using a "drop-in-the-bucket" interpolation method. For snow extent mapping, TB values from the satellite's morning pass only are used as input to the nearest neighbor interpolation. Regions for which the respective interpolation algorithm will be used are defined using a land/ocean/ice cap mask.

Snow extent is mapped separately 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 data as described in Armstrong and Brodzik (2001). NSIDC modified the snow extent mapping algorithm in March 2002, based primarily on a recent study by Armstrong and Brodzik (2002). One goal of this study was to determine when the differences between microwave algorithm output and the validation data are random and when they are systematic; therefore, this study made use of larger and more comprehensive validation data sets that could provide a full range of snow/climate conditions, rather than limited data that might only represent a snapshot in time and space.

Armstrong and Brodzik evaluated snow extent derived from passive microwave data through comparison with over ten years of the NOAA Northern Hemisphere snow charts, which are based on visible-band satellite data. Results clearly indicated time periods and geographic regions where the two techniques agreed and where they tended to consistently disagree. While not always an exact representation of the actual snow extent, the NOAA snow maps are highly accurate and are the product of a well-understood analysis procedure, and as such they were considered truth in these comparisons. The algorithms compared represented examples that included both mid- and high-frequency channels, vertical and horizontal polarizations, and polarization difference approaches, thus allowing an evaluation of the relative merits of these different approaches at the hemispheric scale.

This study demonstrated that both the new NSIDC NISE algorithm and the original Goodison (1989) algorithm underestimated snow extent in the presence of shallow snow; however, during winter and spring the Goodison algorithm tended to consistently overestimate the snow extent (both wet and dry snow) in various locations -- in particular, over such regions as the high-elevation deserts of Central Asia. Armstrong and Brodzik concluded that this was most likely due to an enhanced spectral gradient of the vertical polarization channel in the presence of frozen desert soils. Similarly, the 37 GHz polarization difference that drives the wet snow algorithm often responded to the soil types in this region such that it caused a false snow signal.

In summary, this study indicated that horizontal-polarization-based algorithms, while apparently underestimating snow extent during early winter, appear to 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.

Climatologies mask out spurious data caused primarily by weather effects on sea ice and snow extent passive microwave data. Currently the sea ice climatology is a monthly ocean mask derived from historical SMMR data (1979-1987) and SSM/I data (1987-2003).

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 from October 1966 to May 2005. Beginning 01 July 2005, the snow extent climatology for the Southern Hemisphere is a monthly mask derived from the SSM/I period data from 1987 to 2003, identifying by month those parts of South America and New Zealand that may be snow-covered during that month. The original Southern Hemisphere snow climatology was a static file (not monthly) that served as a spatial representation of the expected snow line in the Andes as a function of latitude and elevation to determine snow extent (Schwerdtfeger 1976). NISE files dated 30 June 2005 and earlier use the original mask.

Northern Hemisphere Monthly Snow Extent Climatologies

Click on any thumbnail to see the full-resolution image.
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

Click on any thumbnail to see the full-resolution image.
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

Click on any thumbnail to see the full-resolution image.
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

Click on any thumbnail to see the full-resolution image.
January - June July - December
January through June ice climatology of the Southern hemispere July through December ice climatology of the Southern hemispere

Version History

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

Table 6. Description of Significant Revisions
Version Date
(yyyy-mm-dd)
Description
V4 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 2008-10-06 Ported NISE processing system from SGI to linux. No significant changes in output.
V2 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 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 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 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 2002-03-20 New snow extent algorithm
V1 2000-06-01 New ice climatologies through 1999
V1 2000-03-02 Changed metadata field for PRODUCTIONDATETIME from local time to UTC
V1 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 1999-10-07 First modifications for managing data in the EOSDIS Core System (ECS)
V1 1999-04-09 Fixed geolocation errors in NISE-to-HDFEOS program
V1 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 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 1997-12-31 Fixed 2-digit year problem in time for new year
V1 1997-10-31 Initial operational release

Applications

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:

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.

4. References and Related Publications

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

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.

Armstrong, R. L., and M. J. Brodzik. 1995. An Earth-Gridded SSM/I Data Set for Cryospheric Studies and Global Change Monitoring. Advances in Space Research 16: 155-63.

Brodzik, M. J. 1998. The EASE-Grid, A Versatile Set of Equal-Area Projections and Grids. Unpublished report to the National Snow and Ice Data Center, Boulder, Colorado USA.

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.

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.

Cavalieri, D. J., P. Gloersen, and W. J. Campbell. 1984. Determination of Sea Ice Parameters With the Nimbus 7 SMMR. Journal of Geophysical Research 89(D4): 5355-69.

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.

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.

Knowles, K. 1992. Points, pixels, grids, and cells: A mapping and gridding primer. Unpublished report to the National Snow and Ice Data Center, Boulder, Colorado USA.

National Snow and Ice Data Center. 2005. EASE-Grid Version of BU-MODIS Land Cover Characterization. ftp://sidads.colorado.edu/pub/EASE/. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.

NSIDC User's Guide. 1996. DMSP SSM/I Brightness Temperatures and Sea Ice Concentration Grids for the Polar Regions. 2d ed. NSIDC/DAAC, Boulder, Colorado USA.

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

Swift, C. T., and D. J. Cavalieri. 1985. Passive Microwave Remote Sensing for Sea Ice Research. EOS 66(49): 1210-2.

Wentz, F. J. 1992. Production of SSM/I data sets. NASA Final Report. Contract NAS8-38075.

Related Data Collections

Sea Ice Products

Sea Ice Data at NSIDC offers a complete summary of sea ice data derived from passive microwave sensors and other sources. It is useful for users who want to compare characteristics of various sea ice products to understand their similarities and differences. This site also provides links to tools for passive microwave data and a list of other sea ice resources.

Snow Extent Products

Microwave Brightness Temperatures

5. Contacts and Acknowledgments

Acknowledgements

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

Investigator(s) Name and Title

Anne W. Nolin
Department of Geosciences
104 Wilkinson Hall
Oregon State University
Corvallis, OR 97331-5506

Richard L. Armstrong
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, Colorado USA 80309-0449

James Maslanik
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, Colorado USA 80309-0449

Technical Contact

NSIDC User Services
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449  USA
phone: +1 303.492.6199
fax: +1 303.492.2468
form: Contact NSIDC User Services
e-mail: nsidc@nsidc.org

6. Document Information

Acronyms and Abbreviations

Table 7 lists acronyms and abbreviations used in this document.

Table 7. Acronyms and Abbreviations
AMSR Advanced Microwave Scanning Radiometer
ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer
CERES Clouds and the Earth's Radiant Energy System
CLASS Comprehensive Large Array-data Stewardship System
DAAC Distributed Active Archive Center
DMSP Defense Meteorological Satellite Program
EASE-Grid Equal-Area Scalable Earth Grid
EOS Earth Observing System
FNMOC Fleet Numerical Meteorology and Oceanography Center
FTP File Transfer Protocol
HDF Hierarchical Data Format
HDF-EOS Hierarchical Data Format - Earth Observing System
IDL Interactive Data Language
LOCI Land-Ocean-Coastline-Ice
MISR Multi-angle Imaging Spectroradiometer
MODIS Moderate-resolution Imaging Spectroradiometer
MSFC Marshall Space Flight Center
N/A Not Applicable
NASA National Aeronautics and Space Administration
NESDIS National Environmental Satellite, Data, and Information Service
NISE Near-real-time Ice and Snow Extent
ODL Object Description Language
NL EASE-Grid Northern Hemisphere low resolution (25-km) Equal-Area Scalable Earth Grid
NOAA National Oceanic and Atmospheric Administration
NSIDC National Snow and Ice Data Center
SL EASE-Grid Southern Hemisphere low resolution (25-km) Equal-Area Scalable Earth Grid
SMMR Scanning Multichannel Microwave Radiometer
SSM/I Special Sensor Microwave/Imager
SSMIS Special Sensor Microwave Imager/Sounder
TB Brightness Temperatures

Document Creation Date

October 1998

Document Revision Date

August 2009

Document URL

http://nsidc.org/data/docs/daac/nise1_nise.gd.html