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Global Monthly EASE-Grid Snow Water Equivalent Climatology

Summary

This data set comprises global, monthly satellite-derived snow water equivalent (SWE) climatologies from November 1978 through June 2003, 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.

The data are binary data files and PNG images, available via FTP.

Citing These Data

Armstrong, R.L., M.J. Brodzik, K. Knowles, and M. Savoie. 2005. Global monthly EASE-Grid snow water equivalent climatology. Boulder, CO: 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

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

Mary Jo Brodzik
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449
USA

Ken Knowles
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449
USA

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

Format

Data files contain flat, binary arrays of gridded values. Browse images of snow data in PNG format are also included. Many browsers and graphics applications display this file type.

Each monthly binary data file with the file extension ".NSIDC8" contains a flat, binary array of 16-bit signed, little-endian (LSB) integers, 721 columns by 721 rows (row-major order, i.e. the top row of the array comprises the first 721 values in the file, etc.).

Data values are:

Other data files have the file extensions ".num" and ".stdev".

• .num - These 16-bit integer files contain the number of days in the month with SWE data to average. If no SWE was available, the value is 0.
• .stdev - These files are 32-bit IEEE floating point values that contain the standard deviation of SWE. If no SWE was available, the value is 0.

Long-term monthly statistics files are also included in the stats subdirectory for each hemisphere and sensor. Statistics directories contain three binary files (.NSIDC8, .num, and .stdev) for each month. These files are the same format as the individual year/month (.NSIDC8, .num, and .stdev) files, except that the data values represent long-term averages for the enclosing span of years, rather than statistics for a single year.

File and Directory Structure

Directory Structure

The directory nsidc0271v01 contains two subdirectories named north and south for the hemispheres. Each of these directories contains three subdirectories; one that contains SMMR data from 1978-1987, one that contains SSM/I data from 1987-2003, and a third directory with SSM/I updates since 2003. Please see Notes and Limitations for details.

The following table shows the directory structure of the data.

File Naming Convention

This data set provides monthly data (time period) files and averages (statistics) over the years covered.

Time Period Files

The following conventions apply to the files of each type (NSIDC8, .num, and .stdev) contained in the time period directories (for example, 1978-1987).
An example file name is: "hLyyyymm.vxx.ext"
where:

PNG image files named *.BP_VIS35.PNG render the microwave-derived SWE in color (in greens-browns-blues), with additional locations (Northern Hemisphere only) where visible snow was detected for two or more weeks in the month (red). PNG image files named *.BP_PCENT20.PNG render the microwave-derived snow-covered area (in gray), with additional locations indicating visible snow frequency of occurrence (in blues). No *.BP_PCENT20.PNG files are included for the Southern Hemisphere.

Statistics Files

Statistics (stats) directories contain monthly average files of each type (NSIDC8, .num, and .stdev) for the periods 1978-1987 and 1987-2003. For example, there is one file that contains the average for the month of January for the entire time period of 1978 through 1987. The naming convention for these files is:

hL.mm.yyyymm-yyyymm.vxx.ext

where:

File Size

Binary files are approximately 4 MB per month per hemisphere,
PNG files vary, and are <100 Kb.

Volume

Total uncompressed volume is 2.6 Gb.

Spatial Coverage

The Northern and Southern Hemispheres.

Spatial Coverage Map

Northern Hemisphere Map		Southern Hemisphere 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 - 2003 monthly, with additional dates provided as updates. Please see Data Set Release History.

Temporal Resolution

These data are provided as monthly composites.

Parameter or Variable

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 Pathfinder Daily EASE-Grid Brightness Temperatures

And was enhanced with Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2 data.

Sample Data Records

Click on the following samples to view larger images. For the entire data set, please see the SWE Climatology browse animations.

Northern Hemisphere

Northern Hemisphere average snow water equivalent (mm) from passive microwave, with additional area indicated as snow by Northern Hemisphere EASE-Grid weekly snow cover in red, March, 2003.

Northern Hemisphere snow extent from passive microwave (gray), with snow-cover frequency for the month from Northern Hemisphere EASE-Grid weekly snow cover (blue shades), March, 2003.

Northern Hemisphere monthly long-term average snow water equivalent (mm) from SMMR, with additional area indicated as snow by Northern Hemisphere EASE-Grid weekly snow cover in red, (November 1978 - July 1987).

Northern Hemisphere monthly long-term average snow water equivalent (mm) from SSM/I, with additional area indicated as snow by Northern Hemisphere EASE-Grid weekly snow cover in red, (August 1987 - June 2003).

Southern Hemisphere

Southern Hemisphere average snow water equivalent (mm) from passive microwave, July, 2002.

Southern Hemisphere monthly long-term average snow water equivalent (mm) from SMMR, (November 1978 - July 1987).

Southern Hemisphere monthly long-term average snow water equivalent (mm) from SSM/I, (August 1987 - June 2003).

Data Set Release History

The following table describes the release history for this data set. New releases are added to the top of the table.

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 snow 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 present) as separate time series, with a potential discontinuity in the summer of 1987.

3. Data Access and Tools

Data Access

Data are available via FTP. Also see the SWE Climatology browse animations.

Related Data Collections

• Armstrong, R. L. and M. J. Brodzik. 2002. Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2. Boulder, CO, USA: National Snow and Ice Data Center. CD-ROM.
• Armstrong, R. L., K. W. Knowles, M. J. Brodzik and M. A. Hardman. 1994, updated 2005. DMSP SSM/I Pathfinder daily EASE-Grid brightness temperatures. Boulder, CO: National Snow and Ice Data Center. Digital media and CD-ROM.
• Knowles, K. W., E. G. Njoku, R. L. Armstrong, and M. J. Brodzik. 2002. Nimbus-7 SMMR Pathfinder daily EASE-Grid brightness temperatures. Boulder, CO: National Snow and Ice Data Center. Digital media and CD-ROM.
• 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.

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

Forest Percent Mask

The forest percent map is derived from NSIDC's EASE-Grid version of the BU-MODIS Land Cover data set. Data values represent the sum of the percent area classified as any of the International Geosphere-Biosphere Programme (IGBP) forest categories. These include:

1. EVERGREEN NEEDLELEAF FOREST
2. EVERGREEN BROADLEAF FOREST
3. DECIDUOUS NEEDLELEAF FOREST
4. DECIDUOUS BROADLEAF FOREST
5. MIXED FORESTS

Any pixels with forest percent higher than 50% are set to the 50% threshold, thereby bounding the forest "correction" of the SWE value to a maximum factor of 2.

Click on the following samples to view larger images.

Northern Hemisphere forest percent mask, derived from BU-MODIS Land Cover data

Southern Hemisphere forest percent mask, derived from BU-MODIS Land Cover data

Permanent Ice Masks

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.

Click on the following samples to view larger images.

Northern Hemisphere permanent ice mask, derived from BU-MODIS land cover data.

Southern Hemisphere permanent ice mask, derived from BU-MODIS land cover data.

Snow Frequency Climatologies

Snow frequency climatologies are shown below.

Long-term snow cover frequencies (1966-2003) derived from Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2 data, used for Processing Steps, Step 5.

Southern Hemisphere long-term snow cover frequencies (1987-2003) derived from SSM/I-derived SWE frequency of occurrence. Areas with "likely" snow are limited to the Andes Mountains region and New Zealand. Frequency thresholds are a function of month (20% for October through May, 7% for June through September). These frequency maps are used for Southern Hemisphere Processing Steps, Step 5.

5.References and Related Publications

Armstrong, R.L. and M.J. Brodzik. 2001. Recent Northern Hemisphere snow extent: a comparison of data derived from visible and microwave sensors. Geophysical Research Letters, 23(19):3673-3676.

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., M. J. Brodzik, J. R. Wang, M. H. Savoie, O. Frauenfeld, T. Zhang. 2004. Solutions to the Snow Cover Mapping Anomaly over the Tibetan Plateau (poster). Eos Trans. AGU, 85(47), Fall Meet. Suppl. Abstract C31A-0282.

Brodzik, M. J. and K. W. Knowles. 2002. "EASE-Grid: a versatile set of equal-area projections and grids" in M. Goodchild (Ed.) Discrete Global Grids. Santa Barbara, CA, USA: National Center for Geographic Information & Analysis. http://www.ncgia.ucsb.edu/globalgrids-book/ease_grid/.

Chang, A. T. C., J. L. Foster, D. K. Hall. 1987. Nimbus-7 SMMR Derived Global Snow Cover Parameters. Annals of Glaciology, 9:39-44.

Chang, A. T. C., J. L. Foster, D. K. Hall. 1996. Effects of Forest on the Snow Parameters Derived from Microwave Measurements during the BOREAS Winter Field Campaign. Hydrological Processes, 10:1565-1574.

6.Document Information

Acronyms and Abbreviations

The following acronyms and abbreviations are used in this document.

Document Creation Date

14 March 2003

Document Revision Date

September 2005

Document URL

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