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

Summary

The Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent product (Near-real-time Ice and Snow Extent, NISE) provides daily, global near-real-time maps of sea ice concentrations and snow extent. The National Snow and Ice Data Center (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. 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. Sea ice concentration and snow extent maps are provided in two 25 km azimuthal, equal-area projections: the Southern Hemisphere 25 km low resolution (SL) and Northern Hemisphere 25 km low resolution (NL) Equal-Area Scalable Earth-Grids (EASE-Grids). 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 and are available via FTP.

Citing These Data

The following example shows how to cite the use of this data set in a publication. For more information, see our Use and Copyright Web page.

The following example shows how to cite the use of this data set in a publication. List the principal investigators, year of data set release, data set title and version number, dates of the data you used (for example, January to March 2004), publisher: NSIDC, and digital media.

Nolin, A., R. L. Armstrong, and J. Maslanik. 1998, updated daily. Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent, [list the dates of the data used]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.

Overview Table

Category	Description
Data format	HDF-EOS
Spatial coverage and resolution	Coverage is global. Resolution is 25 km.
Temporal coverage and resolution	Data are available for the previous two weeks. Users can also order HDF-EOS data files from 04 May 1995 through present. Resolution is daily.
Tools for accessing data	IDL, C, and Fortran are available for the conversion of grid cell locations to latitude and longitude values and vice versa. NSIDC's HDF-EOS Web pages provide information about the HDF-EOS format and tools for extracting HDF-EOS objects into binary or ASCII formats.
Data range	0-255
Grid type and size	Northern Hemisphere low resolution (25 km) EASE-Grid Southern Hemisphere low resolution (25 km) EASE-Grid Each grid is 721 rows by 721 columns.
File naming convention	NISE_SSMI(S)F##_yyyymmdd.HDFEOS NISE_SSMI(S)F##_yyyymmdd.HDF NISE_SSMI(S)F##_yyyymmdd.met NISE_SSMI(S)F##_yyyymmdd_N.GIF NISE_SSMI(S)F##_yyyymmdd_S.GIF
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
Parameter(s)	Snow extent, sea ice concentration, and age of input data used to derive a corresponding data grid value, in days relative to the date of the daily file
Procedures for obtaining data	Data are available via FTP.

 

Table of Contents

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

1. Contacts and Acknowledgments

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

Acknowledgements

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

2. Detailed Data Description

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.

Format

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 (from day of data acquisition to map production) in days relative to the date of the daily file. The data and age grids contain 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 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 Map

Spatial Resolution

Resolution of snow extent and sea ice concentration is 25 km.

Projection

The data set uses the NSIDC NL and SL EASE-Grids. See All About EASE-Grid for more information about the equal-area projections used for this product.

Grid Description

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

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 Record

The following image (NISE_SSMIF13-20020203_N.GIF) shows snow extent, sea ice concentration, and age layers for the Northern Hemisphere for 03 February 2002:

The following image (NISE_SSMIF13-20020203_S.GIF) shows snow extent, sea ice concentration, and age layers for the Southern Hemisphere for 03 February 2002:

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.

3. Data Access and Tools

Direct Data Access

Table 4. Direct Data Access Option

FTP

Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent (NISE) data are available via 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, users are encouraged to register for the data.

 

Other Data Access Options

Users may also obtain NISE data by searching and ordering via the Data Pool, Reverb, or via 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

Data Source Link	Data Source Information
Data Pool	The Data Pool gives access to the NSIDC EOS data holdings. It offers the HDF-EOS to GeoTIFF Conversion Tool (HEG) for reformatting, re-projecting, and performing subsetting operations on certain data sets.
Reverb	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.

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:

• Adjusted Monthly Precipitation, Snowfall and Rainfall for Canada (1874-1990)
• Aqua/AMSR-E Data at NSIDC: Data Summaries
• Central Asian Snow Cover from Hydrometeorological Surveys
• Estonian Mean Snow Depth and Duration (1891-1994)
• Former Soviet Union Hydrological Snow Surveys
• Global Monthly EASE-Grid Snow Water Equivalent Climatology
• Global Seasonal Snow Classification System
• Historical Daily Soviet Snow Depth Data Version 2
• MODIS Data at NSIDC: Data Summaries
• MODIS/Aqua Snow Cover 5-Min L2 Swath 500m
• MODIS/Terra Snow Cover 5-Min L2 Swath 500m
• MODIS/Aqua Snow Cover 8-Day L3 Global 500m Grid
• MODIS/Terra Snow Cover 8-Day L3 Global 500m ISIN Grid
• MODIS/Aqua Snow Cover Daily L3 Global 500m Grid
• MODIS/Terra Snow Cover Daily L3 Global 500m Grid
• Nimbus-7 SMMR Derived Monthly Global Snow Cover and Snow Depth
• Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent
• Program for Arctic Regional Climate Assessment (PARCA) Data
• Rand Corporation Mean Monthly Global Snow Depth
• Regional Survey of Greenland, 1988 - Snow Pit Data
• Western Italian Alps Monthly Snowfall and Snow Cover Duration

Microwave Brightness Temperatures:

• DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures
• DMSP SSM/I-SSMIS Pathfinder Daily EASE-Grid Brightness Temperatures.
• Nimbus-7 SMMR Polar Radiances and Arctic and Antarctic Sea Ice Concentrations

4. Data Acquisition and Processing

Sensor or Instrument Description

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 Source

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
May	June	July	August
September	October	November	December

 

Southern Hemisphere Monthly Snow Extent Climatologies

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

January	February	March	April
May	June	July	August
September	October	November	December

 

Northern Hemisphere Monthly Sea Ice Climatologies

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

January - June	July - December

 

Southern Hemisphere Monthly Sea Ice Climatologies

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

January - June	July - December

Processing Changes

Table 6 summarizes significant revisions to this data set.

Table 6. Descriptions of Significant Revisions

Date (yyyy-mm-dd)	Revisions
1997-10-31	Initial operational release
1997-12-31	Fixed 2-digit year problem in time for new year
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
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
1999-04-09	Fixed geolocation errors in NISE-to-HDFEOS program
1999-10-07	First modifications for managing data in the EOSDIS Core System (ECS)
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
2000-03-02	Changed metadata field for PRODUCTIONDATETIME from local time to UTC
2000-06-01	New ice climatologies through 1999
2002-03-20	New snow extent algorithm
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.
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.
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.
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.
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.
2008-10-06	Ported NISE processing system from SGI to linux. No significant changes in output.
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. Note: No NISE Version 3 product is available.
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.

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:

• 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.

5. 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.

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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
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