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Near-real-time SSM/I EASE-Grid Daily Global Ice Concentration and Snow Extent

Summary

The Near-real-time SSM/I 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 NISE product using passive microwave data from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I). 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

To broaden awareness of our services, NSIDC requests that you acknowledge the use of data sets distributed by NSIDC. Please refer to the citation below for the suggested form, or contact NSIDC User Services for further information. We also request that you send us one reprint of any publication that cites the use of data received from our Center. This helps us to determine the level of use of the data we distribute. Thank you.

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 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

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, CO 80309-0449

James Maslanik
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 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: a data grid and an age grid for 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 convention and as described in Table 1:

NISE_SSMIF##_yyyymmdd_N.GIF

Where:

Example: NISE_SSMIF13_20080501.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 to derive 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 3 lists data values and age used to derive 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

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 SSM/I because the vegetation canopy obscures snow on the ground. The best conditions for accurate snow extent mapping using SSM/I are in areas of little or no vegetation such as in the prairies and tundra.

Known problems typical of SSM/I data, which the Global Hydrology Resource Center (GHRC) attempts 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 GHRC when converting Temperature Data Records to brightness temperatures. NSIDC visually inspects daily NISE browse files. Registered users can receive e-mail notification of any quality problems, including instances when input SSM/I brightness temperatures are not available for a given day.

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.

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 DMSP F13 early morning (descending) orbits are used as input to the algorithm.

3. Data Access and Tools

Direct Data Access

Other Data Access Options

Users may also obtain NISE data by searching and ordering via the Data Pool, the Warehouse Inventory Search Tool (WIST), the Search 'N' Order Web Interface (SNOWI), and 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.

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 Products 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 Daily Polar Gridded Brightness Temperatures
• DMSP SSM/I 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 SSM/I is a seven channel, four frequency, orthogonally polarized, passive microwave radiometer. The instrument measures combined atmosphere and surface radiances at 19, 22, 37, and 85 GHz. See the SSM/I instrument description for more details.

Data Acquisition Methods

The Fleet Numerical Meteorology and Oceanography Center (FNMOC) first receives SSM/I raw antenna temperatures. Data are then sent to the Global Hydrology Resource Center (GHRC) at NASA Marshall Space Flight Center where they are processed into swath brightness temperatures (TBs). NSIDC obtains these swath TBs via FTP once per day from GHRC, 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 GHRC 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. Then the NISE product is 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.

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 NISE Product Version 2 is derived using the NASA Team total sea ice (first year ice plus multiyear ice) algorithm (Cavalieri et al. 1992). For sea ice, all SSM/I (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.

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, CO.

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.

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, CO.

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

Schwerdtfeger, W. 1976. Climate of Central and South America. In: 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.

6. Document Information

Acronyms and Abbreviations

Table 7 lists acronyms and abbreviations used in this document.

Document Creation Date

October 1998

Document Revision Date

September 2008

Document URL

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