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

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.

Armstrong, R. L., M. J. Brodzik, K. Knowles, and M. Savoie. 2005, updated 2008. Global Monthly EASE-Grid Snow Water Equivalent Climatology. Version 1.0. [indicate subset used]. Boulder, Colorado USA: National Snow and Ice Data Center.

Overview

Platforms	Nimbus-7, NOAA Polar Orbiting Environmental Satellites (POES)
Sensors	SMMR, SSM/I
Spatial Coverage	Northern and Southern Hemispheres
Spatial Resolution	Varies
Temporal Coverage	November 1978 – May 2007
Temporal Resolution	Monthly
Parameters	Snow water equivalent (SWE) Snow cover frequency of occurrence Number of days in the month with SWE data contributing to the average Monthly standard deviation of SWE
Data Format	Binary, 721x721 16-bit signed, little-endian (LSB) integers, by row
Metadata Access	View Metadata Record
Current Version	V1.0
Data Access	FTP | Browse Animations | Map Viewer

1. Contacts and Acknowledgments

Investigator(s) Name and Title

Richard L. Armstrong, Mary Jo Brodzik, Ken Knowles, Matt Savoie
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449
USA

Technical Contact

2. Detailed Data Description

Summary

This data set is comprised of global, monthly satellite-derived Snow Water Equivalent (SWE) climatologies from November 1978 through May 2007, with periodic updates released as resources permit. Global data are gridded to the Northern and Southern 25 km Equal-Area Scalable Earth Grids (EASE-Grids). Global snow water equivalent is derived from Scanning Multichannel Microwave Radiometer (SMMR) and selected Special Sensor Microwave/Imagers (SSM/I). Northern Hemisphere data are enhanced with snow cover frequencies derived from the Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2 data (these data were not produced for the Southern Hemisphere). These data are suitable for continental-scale time-series studies of snow cover and water equivalent.

Format

File and Directory Structure

File Naming Convention

File Size

Binary files are approximately 4 MB per month per hemisphere, and PNG files are approximately 100 KB.

Volume

The total uncompressed volume is 2.6 GB.

Spatial Coverage

This data set covers the Northern and Southern Hemispheres, as shown in Figures 1 and 2.

Spatial Coverage Map

Spatial Resolution

This data set is derived from multiple sources. While the climatology files are gridded at 25-kilometer spatial resolution, the actual resolution of the component data (SWE or frequency of occurrence) depends on the input remote sensing data. The satellite passive microwave sensors at the frequencies used for these algorithms have sampling resolutions of 25 km, but the -3dB footprints vary by sensor and frequency, ranging from SMMR 18GHz at 55 x 41 km and SMMR 37 GHz at 27 x 18 km to both of the SSM/I frequencies re-sampled to the 19 GHz footprint at 69 x 43 km. The spatial resolution of the snow cover frequency of occurrence data derived from the Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2 product ranges from 16,000 to 42,000 square kilometers.

Projection

These data are stored in the NL and SL EASE-Grids.

Grid Description

See All About EASE-Grid details for more information, including grid and projection.

Temporal Coverage

1978 - 2007 monthly, with additional dates provided as updates. Please see Data Set Release History.

Temporal Resolution

These data are provided as monthly composites.

Parameter

Parameters are snow water equivalent and snow cover frequency of occurrence, number of days in the month with SWE data contributing to the average, and the monthly standard deviation of SWE.

Parameter Description

Snow water equivalent (SWE) data was derived from the following data sets:

• Nimbus-7 SMMR Pathfinder Daily EASE-Grid Brightness Temperatures
• DMSP SSM/I-SSMIS Pathfinder Daily EASE-Grid Brightness Temperatures

The data were then enhanced with Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent, Version 2.

Sample Data Records

3. Data Access and Tools

Data Access

Data are available via FTP. Also see the Global Monthly SWE Climatology Browse animations and the Atlas of the Cryosphere map viewer.

4. Data Acquisition and Processing

Processing Steps

The data files, designated "NSIDC8", contain microwave-derived SWE enhanced with visible snow frequency for a given month, derived as follows:

1. Input data are daily, "cold" pass, SMMR or SSM/I EASE-Grid brightness temperatures.
2. Daily SWE is derived from the horizontally polarized difference algorithm appropriate for the time period (Chang, Foster and Hall, 1987; Armstrong and Brodzik, 2001):
   • SMMR: SWE (mm) = 4.77 (18H - 37H)
   • SSM/I: SWE (mm) = 4.77 (19H - 37H - 5)
3. Daily SWE is adjusted for surface forest cover (Chang, Foster and Hall, 1996) using the 25 km EASE-Grid version of BU-MODIS Land Cover:
   • Let forest_percent = { 0: no forest, 0.01-0.49: 1-49% total forest, 0.50: >= 50% total forest}
   • Then: Adjusted SWE (mm) = SWE / (1.00 - forest_percent)
4. SWE values less than 7.5 mm are considered unreliable and are set to zero.
   
5. In the Northern Hemisphere, false SWE signals from lower latitude features such as deserts are filtered using the frequency climatologies derived from the Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2 data (1966-2003); pixels where Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2 data never recorded snow in the given month are set to zero SWE.
   In the Southern Hemisphere, false SWE signals from tropical atmospheric phenomena are filtered using a monthly SWE frequency climatology derived from SSM/I. The SWE frequency climatology limits legitimate SWE data to the Andes Mountains region and New Zealand.
6. Areas with permanent ice (ice sheets, ice shelves, and large glaciers) are masked using a 50 percent threshold for permanent ice from the 25 km EASE-Grid version of the BU-MODIS Land Cover data.
7. (SSM/I, August 1987 and later, only): Noise in the SWE signal caused by temporary atmospheric phenomena (e.g. warm, precipitating weather fronts) are removed via a five-day persistence filter. A non-zero SWE signal on the third day is removed if and only if the maximum SWE of the surrounding days is zero. In those cases, the non-zero SWE value is considered too transient to be reliable.
8. Missing data caused by satellite swath coverage is interpolated from the surrounding days, using piece-wise linear interpolation across gaps of six missing days at the most.
9. Monthly average SWE is calculated from interpolated daily SWE. Number of days with data to average and standard deviation are saved in separate files.
10. Northern Hemisphere monthly average SWE is blended with Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2 data for the given month. (Similar data are not available for the Southern Hemisphere.) The maximum extent of visible snow cover for the month is considered the true boundary of snow extent. Within that boundary, save either a) the microwave-derived SWE, or b) the visible snow frequency of occurrence for the month, if no SWE was detected.
11. Fixed values are set for corner, ocean, and permanent ice pixels.

Ancillary Data

Version History

Notes and Limitations

This data set is intended for studies of continental- to hemispheric-scale seasonal fluctuations of snow cover and SWE. Due to the lack of in situ validation data for SWE at both the spatial scale and resolution of the microwave sensors, snow water equivalent derived from satellite passive microwave sensors should be considered with caution. The effective field of view of these passive microwave sensors yields radiometric information from an area that is larger than 625 square kilometers. In theory, the gridded value represents a mean SWE for this large area. Therefore, this value cannot capture localized maxima or minima. The large sensor footprint and other limitations of microwave sensors result in decreased confidence in the SWE reliability and possible undermeasure in the following circumstances:

1. Mountainous areas with large topographic variability return low SWE values. Samples from these areas contain a mixed signal from a large footprint that may include deep snow on north-facing slopes, snow-free south-facing slopes, wind-scoured Alpine areas, etc.
2. Forested areas return low mean SWE values, because the mixed signal includes emission from trees and the snow canopy as well as the underlying surface.
3. Areas near coastlines return low or no SWE values, because the mixed signal includes frozen and unfrozen water and land.
4. Areas containing melting snow or wet snow packs typical of maritime snow conditions return low or no SWE values, because the microwave emission from liquid water overwhelms scattering from the snow pack.
5. Shallow or intermittent snow during fall and early winter typically does not result in sufficient microwave scattering to reliably detect SWE.

Lower confidence in SWE reliability due to overmeasure may occur in areas with significant depth hoar formation. The conditions for depth hoar formation involve the combination of shallow snow exposed to strong temperature gradients driven by cold air temperatures over a period of weeks to months. This results in a snow cover with large grains that enhance the microwave scattering signal and cause overmeasure when a particular algorithm has been tuned to a smaller mean grain size. A typical region prone to this type of snow texture is Eastern Siberia. The seasonal snow cover consistently begins to form in this region as early as September, and then relatively shallow snow remains on the ground as air temperatures begin to approach the extremely cold conditions of winter. The SWE values in this climatology indicate greater values for Eastern Siberia than for Western Siberia, although some climate models indicate the opposite. There could be some degree of overmeasure by the microwave algorithm in this region due to the persistent presence of depth hoar. Unfortunately, the investigators currently do not possess sufficient surface measurements of sow depth to determine with any certainty which pattern is correct.

Lower confidence in SWE reliability due to overmeasure may also occur in areas of extremely high elevation. Current passive microwave snow retrieval algorithms are empirically based and were typically developed using data from lower elevations. The investigators are currently developing an atmospheric correction for regions of extremely high elevation on the Tibetan Plateau, which they expect to implement in the next revision of this data set (Armstrong, et al. 2004).

There is a persistent pattern of relatively high SWE values that develops during the winter season in a large portion of the Canadian Arctic, stretching roughly from the Western edge of Hudson's Bay to the North Slope of Alaska. Unfortunately, this is an area with few ground observing stations, although a large-scale field experiment begun in the 2003-2004 winter season by Derksen and others (C. Derksen, personal communication, May 2004) indicates that the SWE gradient across this area appears to be real and measurable, if not as large a gradient as the microwave algorithm indicates. This feature is still under investigation and should be treated with caution.

The investigators plan to release future versions of this data set with improved SWE algorithms that will address some of these problems.

The passive microwave sensors used for this data set (SMMR and various SSM/I sensors) are all conically-scanning passive microwave radiometers. However, there are enough differences between SMMR and the SSM/I sensors used for this data set to raise doubts as to the validity of time-series analysis of SWE across the sensor break (July to August of 1987). Current investigations at NSIDC include a cross-sensor calibration of the SWE algorithm, but until these are concluded, we recommend that users view the SWE data sets derived from SMMR (1978 to July 1987) and from SSM/I (August 1987 to 2007) as separate time series, with a potential discontinuity in the summer of 1987.

5. References and Related Publications

6. Document Information

Acronyms and Abbreviations

The acronyms and abbreviations used in this document are listed in Table 6.

Table 6. Acronyms and Abbreviations

Acronym	Description
DMSP	Defense Meteorological Satellite Program
IGBP	International Geosphere-Biosphere Programme
LSB	Least Significant Byte
SMMR	Scanning Multichannel Microwave Radiometer
SSM/I	Special Sensor Microwave/Imager
SWE	Snow Water Equivalent
Tb	Brightness Temperature

Document Creation Date

March 2003

Document Revision Date

October 2009

Document URL

http://nsidc.org/data/docs/daac/nsidc0271_ease_grid_swe_climatology.gd.html