Documentation for MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Weekly 100km EASE-Grid 2.0, Version 1

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Detailed Data Description

This data set consists of weekly 100 km snow cover extent for the Northern Hemisphere. The presence of snow is determined from two sources:

Separate variables report whether the SCE CDR and MW record indicate the presence of snow. A third variable merges the other two into a single representation of snow cover by using codes to specify whether none, one, or both the SCE CDR and MW record indicate snow.


Data files are formatted in Network Common Data Form, Version 4 (NetCDF-4) (.nc) following version 1.6 of the Climate and Forecast (CF) metadata conventions. For more information about working with NetCDF formatted data, visit the UCAR Unidata Network Common Data Form Web site.

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File Naming Convention

This section explains the file naming convention used for this data set with an example.

Example File Name:

Refer to Table 1 for the valid values for the file name variables listed above.

Table 1. File Naming Convention Description
Variable Description
nhts Northern Hemisphere Terrestrial Snow
w Weekly
xxx Resolution (km)
e2 EASE-Grid 2.0
YYYYMMDD First day of week (year, month, and day)
yyyymmdd Last day of week (year, month, and day)
v01r01 Version 1.1
.nc netCDF-formatted file
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File Size

Data files are approximately 160 KB. The entire data set is approximately 375 MB.

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Spatial Coverage

Northern Hemisphere:

Southernmost Latitude: 0.0°
Northernmost Latitude: 90.0°
Westernmost Longitude: -180.0°
Easternmost Longitude: 180.0°

Spatial Resolution

100 km

Projection and Grid Description

Data are provided at 100 km resolution in the Northern Hemisphere Equal Area Scalable Earth Grid 2.0 (EASE-Grid 2.0). Grid dimensions are 180 x 180. For a complete description of EASE-Grid 2.0, visit NSIDC's EASE-Grid 2.0 Format Description page.

This data set also utilizes a corresponding EASE-Grid 2.0, 100 km Land-Ocean-Coast-Ice (LOCI) mask that is derived from the Boston University MOD12Q1 V004 Land Cover Product (BU-MODIS) and distributed by NSIDC. To view documentation and obtain the BU-MODIS LOCI 100 km mask, visit the EASE-Grid 2.0 Land-Ocean-Coastline-Ice Masks Derived from Boston University MODIS/Terra Land Cover Data Web page.

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Temporal Coverage

This data set extends from 3 October 1966 to 31 December 2012; however, users should note that the MW data record begins later, on 1 January 1979. Prior to this date, land cells in the MW gap-filled snow cover variable are filled with a value of 90 (missing).

Temporal Resolution

This data set consists of weekly snow cover. Each week runs from Tuesday through the following Monday.

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Parameter or Variable

Snow cover extent, the parameter of interest in this data set, is represented by three different NetCDF variables. Data files contain additional variables that specify the latitude and longitude at the center of each EASE-Grid 2.0 cell, grid and projection parameters, and the number of days since 3 October 1966. Table 2 lists this data set's variable names with brief descriptions:

Table 2. Variable Names and Descriptions
Variable Name Description Dimensions Data Type
weekly_climate_data_record_snow_cover_extent Snow cover derived from NOAA/NCDC SCE CDR 180 x 180 byte (signed)
passive_microwave_gap_filled_snow_cover_extent Snow cover derived from MW 180 x 180 byte (signed)
merged_snow_cover_extent Codes indicating whether the SCE CDR and/or MW report snow 180 x 180 byte (signed)
latitude¹ Latitude at the center of each 25 km EASE-Grid 2.0 cell 180 x 180 float²
longitude¹ Longitude at the center of each 25 km EASE-Grid 2.0 cell 180 x 180 float²
cols x coordinate, center of 100 km EASE-Grid 2.0 cell (m from origin) 1 x 180 int
rows y coordinate, center of 100 km EASE-Grid 2.0 cell (m from origin) 180 x 1 int
coord_system EASE-Grid 2.0 grid and projection parameters char
time Days since 10/03/1966 int
¹Fill value for grid corners: -999
²32-bit single precision floating point

Parameter Range

The two snow cover variables derived from individual sources indicate the location of snow and, where applicable, where the original data were changed to reconcile the source data's land-cover mask with the BU-MODIS LOCI 100 km mask described in the Projection and Grid Description section. In the merged snow cover extent variable, land and water areas, as well as permanent ice values masking out most of Greenland and some other areas, correspond to the BU-MODIS LOCI 100 km mask.

Refer to the following tables for keys to the values stored in the snow cover extent variables.

Table 3. Key for weekly_climate_data_record_snow_cover_extent
Value Description
10 Snow covered land
11 Ocean converted to snow covered land
20 Snow free land
21 Ocean converted to snow free land
40 Ocean
41 Snow covered land converted to ocean
42 Snow free land converted to ocean
-99 Fill value for grid corners
Table 4. Key for passive_microwave_gap_filled_snow_cover_extent
Value Description
10 Snow covered land
20 Snow free land
30 Permanent ice covered land
40 Ocean
90 Missing
-99 Fill value for grid corners

Table 5. Key for merged_snow_cover_extent
Value Description
10 CDR and passive microwave report snow
11 CDR only reports snow
12 Passive microwave only reports snow
20 Snow free land
30 Permanent ice covered land
40 Ocean
-99 Fill value for grid corners

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Software and Tools

Software and Tools

Unidata at the University Corporation for Atmospheric Research maintains an extensive list of freely available Software for Manipulating or Displaying NetCDF Data.

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Data Acquisition and Processing

The following sections detail the source data and snow cover extent variables utilized by this data set.

NOAA/NCDC Northern Hemisphere Snow Cover Extent Climate Data Record

The weekly NOAA/NCDC Northern Hemisphere Snow Cover Extent (SCE) Climate Data Record (CDR) combines two snow cover products into a continuous SCE record that spans October 1966 to present.

Prior to May 1999, the SCE CDR is based on weekly, satellite-derived Northern Hemisphere snow cover extent maps. These maps were initially produced by trained NOAA meteorologists visually interpretting photographic copies of shortwave imagery, and as time progressed, new sources of imagery as they became available. This portion of the SCE CDR was previously re-processed for the NOAA Northern Hemisphere weekly snow cover extent product developed at Rutgers University.

From June 1999 onward, the SCE CDR is derived from National Ice Center (NIC) Interactive Multisensor Snow and Ice Mapping System (IMS) 24 km snow cover maps. The IMS maps are reformatted to correspond to the spatial and temporal resolution of the earlier NOAA product (pre-June 1999) and appended to the data record to produce the full SCE CDR.

IMS daily 24 km snow maps are produced by trained analysts at NIC using an interactive workstation application to incorporate a wide variety of satellite imagery, derived mapped products, and surface observations. Data sources include:

Visible and Infrared Spectral Data

  • Advanced Very High Resolution Radiometer (AVHRR) Channels 1 & 3
  • Geostationary Operational Environmental Satellite (GOES) East and West
  • Meteosat 7
  • Meteosat Second Generation (MSG)
  • Moderate Resolution Imaging Spectroradiometer (MODIS) Channel 8
  • Multi-functional Transport Satellite (MTSAT)
  • Suomi National Polar-Orbiting Partnership

Ancillary Data

  • Surface Observations (METAR)

Derived Data

  • Air Force Weather Agency (AFWA) Snow Depth
  • Advanced Microwave Scanning Radiometer for EOS (AMSR-E)
  • Advanced Microwave Sounding Unit (derived snow, ice, and rain)
  • Advanced Scatterometer (ASCAT)
  • Envisat Advanced Synthetic Aperture Radar (ASAR) operating in Global Monitoring Mode
  • SSMI/S (Derived snow, ice and rain)
  • NASA Quick Scatterometer (QuikSCAT)
  • NOAA Office of Satellite Data Processing and Distribution (OSDPD) Automated Multisensor Snow and Ice
  • National Operational Hydrologic Remote Sensing Center (NOHRSC) Snow Data Assimilation System (SNODAS)
  • United States Air Force Snow and Ice Analysis Product

For a complete description of the algorithm used to create the SCE CDR, see the Climate Algorithm Theoretical Basis Document (C-ATBD): Northern Hemisphere Snow Cover Extent (pdf).

Data Acquisition

The SCE CDR was downloaded via FTP from the Climate Data Record Program at the National Climatic Data Center (listed under Terrestrial CDRs).

Processing Steps

The SCE CDR was first re-gridded to EASE-Grid 2.0 with Mapx and then compared with the BU-MODIS LOCI 100 km mask. In cases where an ocean pixel was converted to land, the discrepancy was reconciled by using a 3x3 moving box filter. With the converted cell in the center, surrounding land cells were examined and if more than 50 percent of the original surrounding land cells were snow covered, the cell was assigned a snow cover value. Otherwise the cell was determined to be snow-free land. Once reconciled with the BU-MODIS LOCI 100 km mask, the data were written to the weekly_climate_data_record_snow_cover_extent variable.

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Passive Microwave Brightness Temperature Gap-Filled Snow Cover

Data Acquisition

Passive microwave brightness temperatures were obtained from two data sets distributed by NSIDC: Nimbus-7 SMMR Pathfinder Daily EASE-Grid Brightness Temperatures and DMSP SSM/I-SSMIS Pathfinder Daily EASE-Grid Brightness Temperatures, Version 2. These data were acquired over a 34-year span by the SMMR instrument on board Nimbus-7 and the SSM/I and SSMIS instruments carried by Defense Meteorological Satellites Program (DMSP) satellites F8, F11, F13, and F17.

Table 6 lists the satellites, instruments, and date ranges used for this data set. Use the links to view detailed descriptions of each satellite's mission.

Table 6. Satellite and Instrument Date Ranges
Satellite Instrument Dates
Nimbus 7 SMMR 1 January 1979 – 30 June 1987
F8 SSM/I 1 July 1987 – 14 December 1991
F11 SSM/I 15 December 1991 – 31 December 1995
F13 SSM/I 1 January 1996 – 31 December 2007
F17 SSMIS 1 January 2008 – 31 December 2012

Processing Steps

The following steps were used to convert brightness temperatures into snow cover. SSM/I F13 was used as the standard sensor to ensure a consistent data record:

  1. SMMR brightness temperatures were converted to SSM/I F08 using the linear regression described in Jezek et al., 1991.
  2. F08 values (including SMMR values from Step 1) were converted to F11 based on Abdalati et al., 1995. F11 brightness temperatures were then converted to F13 using regression equations from Stroeve et al., 1998.
  3. SSMIS F17 brightness temperatures were converted to SSM/I F13 based on the methodology in Meier et al., 2011. Ascending and descending passes were averaged into daily average brightness temperatures.
  4. The presence of snow was identified where the 19/37 GHz and 22/85 GHz (91 GHz for SSMIS) vertical polarization frequency gradients exceed minimum thresholds. Additionally, the brightness temperature for the 37 GHz channels (horizontal and vertical polarization) and 85GHz (vertical polarization) must not exceed a maximum threshold. The approach is conceptually similar and uses the same frequency gradients and channels as the AMSR-E Snow Water Equivalent Algorithm.  Refer to the text by Chang and Rango, 2000 for further information. Thresholds were set to achieve a 60 percent probability of detecting snow, determined by: hemispheric comparison with IMS snow; snow depth observations from available stations including the Global Historical Climatology Network, cooperative stations, and the Meteorological Service of Canada; and daily average brightness temperatures during the 2005-2006 snow season. The thresholds are listed in Table 7.
    Table 7. Criteria for Identifying Snow
    Channel Threshold
    19V–37V > 7 K
    22V–85V (22V–91V) > 8 K
    37V < 256 K
    37H < 243 K
    85V < 253 K
  5. At locations where the elevation exceeds 1500 meters above mean sea level, the 19/37 GHz and 22/85 GHz frequency gradients were reduced by 0.001 K and 0.002 K per meter elevation, respectively. This correction was applied because passive microwave snow retrievals overestimate snow-covered area in high elevation locations, and in particular the Tibetan Plateau, because the decrease in atmospheric thickness increases the spectral gradient.  Refere to the text by Savoie et al., 2009 for further information. The coefficients were determined by a comparison with high elevation (>1500 m) locations in the same manner discussed in Step 2.
  6. In regions with a maximum snow-covered albedo ≤58 percent, the 19/37 GHz and 22/85 GHz frequency gradients were increased by 3 K and 4 K, respectively. Because snow that falls through the canopy may not be apparent when on the ground (Foster et al., 1991), passive microwave snow retrievals underestimate snow covered area in densely forested locations such as the boreal forests. To account for this effect, a maximum snow covered albedo data set (Robinson et al., 1985) was re-gridded to EASE Grid 2.0 and used to identify regions in which snow cover is spectrally masked by vegetation. Coefficients were determined by comparing areas with maximum snow covered albedo ≤58% in the same manner discussed in step 2.
  7. The criteria described in Step 2, as modified in steps 3 and 4, were applied to the daily average brightness temperatures to determine the presence or absence of snow.
  8. Areas of missing data were reduced by employing a six-day gap-fill method. If data were missing for a given date, the previous day's data was retained as the observation for the current day for up to five days prior.
  9. The resulting daily gap-filled snow cover map was re-gridded to 25 km EASE-Grid 2.0 using Mapx. A maximum snow cover extent mask was also applied based on all cells with IMS data that indicated snow on any date during a given month.
  10. Intermediate weekly files were generated for the MW-derived snow variable by selecting Monday's snow cover (the data week runs from Tuesday through the following Monday) as the representative value for each cell, each week. If Monday's data was missing, Sunday's was used instead. If neither day was available, the weekly snow cover for that cell was filled as missing. This approach was adopted primarily to match the weekly SCE CDR product, which is generated using Monday's IMS data.
  11. Weekly snow cover files were re-gridded to 100 km EASE-Grid 2.0 using Mapx.
  12. Areas of permanent ice and ocean were masked using the BU-MODIS LOCI 100 km mask. Discrepencies arising from land mask mismatches were reconciled using the 3x3 box filter approach described in the Processing Steps for the SCE CDR.
  13. The final weekly data were written to the passive_microwave_gap_filled_snow_cover_extent variable.
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Merged Snow Cover Extent

Processing Steps

Once source snow cover variables were re-gridded and reconciled with the BU-MODIS LOCI 100 km mask, the merged snow cover extent variable was generated as follows:

  1. BU-MODIS LOCI 100 km ocean and land pixels were written to an array.
  2. Values for snow covered land and ocean converted to snow covered land were written to the array using the coding scheme in Table 5.
  3. BU-MODIS LOCI 100 km permanent ice values were written to the array.
  4. The data were written to the merged_snow_cover_extent variable.

Version History

Version 1.1 was released in July, 2015. Refer to Table 9 for this data set's version history:

Table 8. Version History
Version Description
V1.1 (Jul, 2015)
  • Added 1D arrays named cols and rows that contain x and y coordinates (meters from origin) of the projection.
  • v01r01 appended to data file names.
V1 (Dec, 2014) Initial version

Error Sources

Missing data represents the biggest impact to data quality, as all the input products lack some daily files during the period of record.

The SCE CDR snow cover maps were not constructed according to a formal algorithm, but instead relied on the expertise of trained analysts. Because the IMS workstation software at NIC is continually being evaulated and improved, changes in mapping methodologies have occurred over time.

For the passive microwave derived snow cover, errors in the input data may carry through to the output. Potential errors in passive microwave brightness temperatures are described in the Error Sources sections of the Nimbus-7 SMMR Pathfinder Daily EASE-Grid Brightness Temperatures and DMSP SSM/I-SSMIS Pathfinder Daily EASE-Grid Brightness Temperatures documentation.

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Sensor or Instrument Description

The Nimbus-7 Scanning Multichannel Microwave Radiometer observed Earth at five frequencies, in both horizontal and vertical polarization, from 26 October 1978 to 21 August 1987. For a detailed description, see NSIDC's Scanning Multi-channel Microwave Radiometer (SMMR) Web page.

The Special Sensor Microwave/Imager is a seven-channel, four frequency, orthogonally polarized passive microwave radiometric system flown aboard DMSP satellites F8, F10, F11, F12, F13, and F15. It's successor, the Special Sensor Microwave Imager/Sounder, is a 24-channel passive microwave radiometer carried by DMSP F17. For detailed descriptions of these instruments, see NSIDC's Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) Web pages.

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References and Related Publications

Contacts and Acknowledgments


David A. Robinson
Rutgers University
Department of Geography
54 Joyce Kilmer Avenue
Piscataway, NJ 08854

Thomas L. Mote
University of Georgia
Department of Geography
210 Field Street
Athens, GA 30602


These data were generated with support from Award NNX08AP34A from the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program.

The investigators wish to thank:

  • Thomas W. Estilow, Research Analyst, Center for Environmental Prediction, Rutgers University
  • Gina R. Henderson, Consultant, Oceanography Department, United States Naval Academy.

Special thanks go to: Mary Jo Brodzik at NSIDC for technical assistance, gridding and software support, and advice regarding the EASE Grid-2.0 format; Donna Scott at NSIDC for technical support, documentation and metadata support, advice and comments, and for serving as this project's liaison; and members of the Northern Hemisphere Snow and Ice MEaSUREs team, including Mark Anderson, Angela Bliss, Dorothy Hall, and Mark Tschudi.

Document Information


December 2014


July 2015