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These Level-3 Snow Water Equivalent (SWE) data sets contain SWE data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).
This document pertains to the following AMSR-E data sets:
|Short Name||Long Name|
|AE_DySno||AMSR-E/Aqua Daily L3 Global Snow Water Equivalent EASE-Grids|
|AE_5DSno||AMSR-E/Aqua 5-Day L3 Global Snow Water Equivalent EASE-Grids|
|AE_MoSno||AMSR-E/Aqua Monthly L3 Global Snow Water Equivalent EASE-Grids|
As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.
Tedesco, M., R. Kelly, J. L. Foster, and A. T.C. Chang. 2004. AMSR-E/Aqua Daily L3 Global Snow Water Equivalent EASE-Grids. Version 2. [indicate subset used]. Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: 10.5067/AMSR-E/AE_DYSNO.002.
|Spatial Coverage and Resolution||Northern and Southern Hemispheres at 25 km resolution|
|Temporal Coverage and Resolution||Temporal coverage:
AE_DySno - 19 June 2002 to 3 Oct 2011
Temporal resolutions span a day, five days, or a month.
|Tools for Accessing Data||For tools that work with AMSR-E data, see the Tools for AMSR-E Data Web page.
For general tools that work with HDF-EOS data, refer to the NSIDC: Hierarchical Data Format - Earth Observing System (HDF-EOS) Web site.
|Grid Type and Size||Full Northern and Southern Hemisphere EASE-Grid projections (721 rows x 721 columns)|
|File Naming Convention||AMSR_E_L3_5DaySnow_X##_yyyymmdd.hdf
|File Size||Each daily, 5-day, and monthly granule is 2.1 MB.|
|Parameter||Snow Water Equivalent (mm) Note: Multiply data values by 2.|
|Get Data|| FTP: AE_DySno; AE_5DSno; AE_MoSno
Reverb | ECHO: AE_DySno; AE_5DSno; AE_MoSno
Department of Earth and Atmosphere Sciences
City University of New York and NASA GSFC
New York, NY 10031
Department of Geography
University of Waterloo
Waterloo, Ontario N2L 3G1
NASA Goddard Space Flight Center
Greenbelt, MD 20771
NSIDC User Services
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449 USA
phone: +1 303.492.6199
fax: +1 303.492.2468
form: Contact NSIDC User Services
Data are stored in Hierarchical Data Format - Earth Observing System (HDF-EOS) format. See NSIDC's HDF-EOS Web page for more information about this format. Files contain core metadata, product-specific attributes, and 721 rows x 721 columns pixel data fields in 1-byte unsigned integer format. Table 1 through Table 3 describe the data fields and the pixel values for the SWE and QA flags.
Note: Actual SWE values are scaled down by a factor of 2 for storing in the HDF-EOS file, resulting in a stored data range of 0-240. Users must multiply the SWE values in the file by a factor of 2 to scale the data up to the correct range of 0-480 mm.
|0-240||SWE divided by 2 (mm)|
|247||incorrect spacecraft attitude|
|252||land or snow impossible|
|252||land or snow impossible|
This section explains the file naming convention used for this product with an example. The date in the file name corresponds to the first scan of the granule.
Note: Refer to Table 4 for the values of the file name variables listed above.
|Product Maturity Code (Refer to Table 5 for valid values.)|
|file version number|
|HDF-EOS data format|
|Preliminary - refers to non-standard, near-real-time data available from NSIDC. These data are only available for a limited time until the corresponding standard product is ingested at NSIDC.|
|Beta - indicates a developing algorithm with updates anticipated.|
|Transitional - period between beta and validated where the product is past the beta stage, but not quite ready for validation. This is where the algorithm matures and stabilizes.|
|Validated - products are upgraded to Validated once the algorithm is verified by the algorithm team and validated by the validation teams. Validated products have an associated validation stage. Refer to Table 6 for a description of the stages.|
|Product accuracy is estimated using a small number of independent measurements obtained from selected locations, time periods, and ground-truth/field program efforts.|
|Product accuracy is assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts.|
|Product accuracy is assessed, and the uncertainties in the product are well-established via independent measurements made in a systematic and statistically robust way that represents global conditions.|
Table 7 provides examples of file name extensions for related files that further describe or supplement data files.
|Extensions for Related Files||Description|
|.qa||Quality assurance information|
|.ph||Product history data|
Each daily, 5-day, and monthly granule is 2.1 MB.
SWE data are available for the full Northern and Southern Hemispheres.
Spatial resolution is 25 km.
Data are provided in Northern and Southern Hemisphere EASE-Grid projections. For details, please see NSIDC's EASE-Grid: A Versatile Set of Equal-Area Projections and Grids Web page.
Grids are 721 rows x 721 columns. For more information, including details about the EASE-Grid projections plus related products and tools, see NSIDC's All About EASE-Grid Web site.
For this EASE-Grid product, the tar files Nl_geolocation.tar.gz and Sl_geolocation.tar.gz contain geolocation tools. These tools include map projection parameters (.mpp files), grid parameter definitions(.gpd files), latitude/longitude binary files, and conversion software such as C, FORTRAN (FORmula TRANslation), and IDL (Interactive Data Language). These tar files are available via FTP.
AE_DySno - 19 June 2002 to 3 Oct 2011
AE_5DSno - 19 June 2002 to 3 Oct 2011
AE_MoSno - 19 June 2002 to 1 Oct 2011
See AMSR-E Data Versions for a summary of temporal coverage for different AMSR-E products and algorithms.
Daily, 5-day maximum, and monthly mean SWE are available. During leap years, the last 5-day period of February actually has six days.
Snow Water Equivalent (SWE)
Data are available via FTP and Reverb | ECHO, the NASA search and order tool for subsetting, reprojecting, and reformatting data. See Table 8 for access to the data.
|Access Method||Access Link|
|FTP||AE_DySno; AE_5DSno; AE_MoSno|
|Reverb | ECHO||AE_DySno; AE_5DSno; AE_MoSno|
Each daily, 5-day, and monthly granule is 2.1 MB.
See NSIDC's HDF-EOS Web page for tools that work with HDF-EOS data.
Space-borne sensors measure microwave brightness temperatures from radiation released from the underlying surface, the snowpack, and the atmosphere. The atmospheric contribution is usually small; thus, it does not need to be considered when measuring snowpack parameters over snow covered areas. Snow crystals are effective scatterers of microwave radiation. The deeper the snowpack, the more snow crystals there are available to scatter microwave energy away from the sensor. Hence, microwave brightness temperatures are generally lower for deep snowpacks (more scatterers) than they are for shallow snowpacks (fewer scatterers) (Matzler 1987) and (Foster et al. 1991). Based on this fact, SWE retrieval algorithms were developed (Kunzi et al. 1982), (Chang et al.1982), (Hallikainen and Jolma 1986), (Goodison et al. 1990), and (Rott et al.1991).
The intensity of microwave radiation emitted from a snow pack is determined through radiative transfer computation based on the physical temperature, grain size, density, and underlying conditions of the snow pack. Several factors affect the microwave brightness temperature emitted from a snow pack, including the freeze/thaw states of the underlying soil, crystal size, temperature and density profiles, and the layering structure (Chang and Rango 2000).
See NSIDC's Instrument Description: Advanced Microwave Scanning Radiometer (AMSR-E) for information about the AMSR-E instrument.
See NSIDC's Instrument Description: Advanced Microwave Scanning Radiometer (AMSR-E) for more information.
The original data source is the AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures data set.
The original baseline SWE algorithm is based on methods described in Chang, Foster, and Hall (1987) and Chang et al. (1997). This algorithm identifies land regions that are historically affected by snow; it retrieves the SWE using the simple brightness temperature difference approach described in Chang, Foster, and Hall (1987). Enhancements have been made to the original baseline algorithm including improved SWE retrieval methods (Kelly and Foster 2005) and (Kelly, Foster, and Hall 2005), and advancements will continue with ongoing algorithm updates .
Ancillary data is used for:
Two high spatial resolution MODIS land data sets are used to correct for forest attenuation:
A fractional forest cover ancillary file is derived from the original International Geosphere-Biosphere Programme (IGBP) classification, were each data point is the forest fraction of 0 -100 percent. For each ~1 km pixel, forest fraction in percent is obtained and a matching forest density is found. Both 1 km forest estimated variables (fraction and density) are circular smoothed to a 15 km diameter and regridded to a global 1 km.
A 25 km EASE-grid snow density climatology file for Northern and Southern Hemisphere is derived using the average snow density values for January through March from Canadian and Former Soviet Union ground measurements described in Brown (1998) and Krenke (2004). Average density values are calculated within each of the six classes (Sturm et al. 1995).
The possibility of snow mask and the land/ocean/ice mask are gridded to the 25 km EASE-Grid domain. The land/ocean/ice mask is based on the MOD12Q1 data set.
The retrievals are performed at the Instantaneous Field of View (IFOV) 1 km grid cell and then projected in a EASE-Grid cell. The number of IFOVs that contribute to each EASE-Grid cell is tracked, and an average value of all contributing retrievals is computed for the EASE-Grid cell.
SWE retrievals are performed using AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperature unresampled data. For each low frequency (< 89 GHz) sample, a Snow Depth (SD) retrieval is performed using brightness temperatures at the IFOV and then projected to the 25 km EASE-Grid projection.
The probability of snow per pixel is determined using snow cover maps from Dewey and Heim (1981) and the land/ocean/ice mask. If snow is impossible for a given pixel, the algorithm flags the pixel as no snow and continues to the next pixel. If snow is possible, a snow detection algorithm is applied to the pixel.
In pixels where snow is possible, brightness temperatures are screened. The algorithm uses data from various low frequency channels and a land surface temperature estimator from Kelly et al. (2003) to detect snow. Detection of snow is determined by the thresholds Tb36V ≤ 255 K and Tb36H> ≤ 245 K. If these conditions are satisfied, the brightness temperatures for different channels are checked to determine if snow is likely to be shallow or medium-to-deep.
Retrievals are calibrated for snow depth and projected in the 25 km EASE-Grid array. The number of IFOV retrievals comprising the accumulated SD total is used to convert the accumulated total from all daily descending granules into an average SD. The SD average is then converted to a SWE average with the snow density climatology file.
If snow presence is detected but it is likely to be shallow, the SD for the IFOV sample is estimated as 5.0 cm. For medium-to-deep snow, separate retrievals for forested and un-forested fractions are combined. The SD for the IFOV sample is calculated as:
SD = (ff * ( SDf )) + ((1 - ff) * ( SDo ))
|SDf||=||snow depth from the forested component of the IFOV|
|SDo||=||snow depth from the non-forested component of the IFOV|
|ff||=||forest fraction (1.0 = 100% forest fraction and 0.0 = 0% forest fraction)|
The IFOV sample SD value is projected and added to the appropriate 25 km EASE-Grid cell.
After processing all granule sample SD and accumulating the SD in the 25 km EASE-Grid array, the average SD is computed for each 25 km EASE-Grid cell, also known as a drop in the bucket average. SWE is estimated for each cell using the snow depth and the ancillary snow density data:
SWE = SD (cm) * density (g cm-3) * 10.0 (mm)
Refer to the Format section of this document for information on scaling of SWE data.
See Processing History for changes to the SWE products by algorithm.
Daily granules are created by performing retrievals on individual AMSR-E Level-2A brightness temperature samples. The retrievals are then averaged to the 25 km Northern and Southern Hemisphere EASE-Grid. The 5-day maximum SWE granules are created from daily data composites. Derived snow variables from the daily product over the same grid cell are screened for consistency based on statistical tests described in the SWE Estimation section above. Maximum SWE is recorded.
Monthly averaged SWE granules are created from daily data composites. Derived snow variables from the daily product over the same grid cell are screened for consistency based on statistical tests. Mean SWE is recorded.
See AMSR-E Data Versions for a summary of algorithm changes since the start of mission.
Snow crystal size, snow detection in mountainous terrain, wet snow discrimination, and snow mapping in densely forested areas are factors that introduce errors into snow mapping and increase the variance of estimated SWE. Mapping snow in topographically-rough areas as if they were flat also causes errors in SWE estimation (Chang and Rango 2000).
Each HDF-EOS file contains core metadata with Quality Assessment (QA) metadata flags that are set by the Science Investigator-led Processing System (SIPS) at the Global Hydrology and Climate Center (GHCC) prior to delivery to NSIDC. A separate metadata file in XML format is also delivered to NSIDC with the HDF-EOS file; it contains the same information as the core metadata. Three levels of QA are conducted with the AMSR-E Level-2 and Level-3 products: automatic, operational, and science QA. If a product does not fail QA, it is ready to be used for higher-level processing, browse generation, active science QA, archive, and distribution. If a granule fails QA, SIPS does not send the granule to NSIDC until it is reprocessed. Level-3 products that fail QA are never delivered to NSIDC (Conway 2002).
Chang visually examined random samples of SWE products to ensure they were consistent with an understanding of climate and that no gross errors were present. Future validation will involve comparing retrieved SWE values with estimates from airborne gamma observations over the U.S. (Carroll 1997) and with snow gauge data (Carroll et al. 1995), as well as comparing snow extent with MODIS snow maps (Chang and Rango 2000).
AMSR-E Level-2A data arriving at GHCC are subject to operational QA prior to processing higher-level products. Operational QA varies by product, but it typically checks the following criteria for a given file (Conway 2002):
AMSR-E Level-2A data arriving at GHCC are also subject to science QA prior to processing higher-level products. If less than 50 percent of a granule's data is good, the science QA flag is marked "suspect" when the granule is delivered to NSIDC. In the SIPS environment, the science QA includes checking the maximum and minimum variable values, and percent of missing data and out-of-bounds data per variable value. At the Science Computing Facility (SCF), also at GHCC, science QA involves reviewing the operational QA files, generating browse images, and performing the following additional automated QA procedures (Conway 2002):
Geolocation errors are corrected during Level-2A processing to prevent processing anomalies such as extended execution times and large percentages of out-of-bounds data in the products derived from Level-2A data.
The Team Lead SIPS (TLSIPS) developed tools for use at SIPS and SCF for inspecting the data granules. These tools generate a QA browse image in Portable Network Graphics (PNG) format and a QA summary report in text format for each data granule. Each browse file shows Level-2A and Level-2B data. These are forwarded from Remote Sensing Systems (RSS) to GHCC along with associated granule information, where they are converted to HDF raster images prior to delivery to NSIDC.
SWE is estimated for SD retrievals greater than 1 mm. Based on the 2002-2003 winter AMSR-E data and 38 coincident ground observations in the World Meteorological Organization (WMO) Global Telecommunications System (GTS) network, the standard error is 24.2 cm. Further validation is planned using multiple local, regional, and global data sets.
See NSIDC's AMSR-E Validation Data for information about data used to check the accuracy and precision of AMSR-E observations.
Basist, A., N. C. Grody, T. C. Peterson, and C. N. Williams. 1998. Using the Special Sensor Microwave Imager to Monitor Land Surface Temperatures, Wetness and Snow Cover. Journal of Applied Meteorology 37(9): 888-911.
Brodzik, M. J. 1997. EASE-Grid: A Versatile Set of Equal-Area Projections and Grids. Boulder, CO, USA: National Snow and Ice Data Center.
Chang, Alfred T. C., and Albert Rango. 2000. Algorithm Theoretical Basis Document for the AMSR-E Snow Water Equivalent Algorithm, Version 3.1. Greenbelt, Maryland USA: NASA Goddard Space Flight Center. (PDF file, 300 KB)
Chang, A. T. C., J. L. Foster, Dorothy K. Hall, B. E. Goodison, A. E. Walker, and J. R. Metcalfe. 1997. Snow Parameters Derived from Microwave Measurements During the BOREAS Winter Field Experiment. Journal of Geophysical Research 102: 29663-29671.
Chang, A. T. C., R. E. J. Kelly, J. L. Foster, and Dorothy K. Hall. The Testing of AMSR-E Snow Depth and Snow Water Equivalent Estimates in the Northern Hemisphere. Poster presented at the AGU Fall Meeting, San Fransisco, CA., 8-12 December 2003a.
Chang, A. T. C., Richard E. J. Kelly, J. L. Foster, and Dorothy K. Hall. Global SWE Monitoring Using AMSR-E Data. Poster presented at the Proceedings of IGARSS, Toulouse, France, 21-25 July 2003
Chang, A. T. C., Richard E. J. Kelly, J. L. Foster, and Dorothy K. Hall. Estimation of Snow Depth from AMSR-E in the GAME-Siberia Experiment Region. Poster presented at the Proceedings of IGARSS, Alaska, USA 2004.
Carroll, T. R. 1997. Integrated Ground-based, Airborne, and Satellite Snow Cover Observations in the National Weather Service. 77th AMS Annual Meeting; Symposium on Integrated Observing Systems, Long Beach, CA.
Goodison, B., A. E. Walker, and F. W. Thirkettle. 1990. Determination of Snowcover on the Canadian Prairies Using Passive Microwave Data. Proceedings of the International Symposium on Remote Sensing and Water Resources. Enschede, The Netherlands, 127-136.
Hansen, M. C., R. S. DeFries, J. R. G. Townshend, M. Carroll, C. Dimiceli, and R. A. Sohlberg. 2003. Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Continuous Fields Algorithm. Earth Interactions, 7 10:15.
Josberger, E. G. and N. M. Mognard. 2000. A Passive Microwave Snow Depth Algorithm with a Proxy for Snow Metamorphism. Proceedings of the Fourth International Workshop on Applications of Remote Sensing in Hydrology, Santa Fe, NM.
Kelly, Richard E. J. and J. L. Foster. The AMSR-E Snow Water Equivalent Product: Status and Future Development. Poster presented at the American Geophysical Union Fall Meeting, San Francisco, CA., 5-9 December 2005.
Kelly, Richard E. J., J. L. Foster and Dorothy K. Hall. The AMSR-E Snow Water Equivalent Product: Algorithm Development and Progress in Product Validation. Poster presented at the Proceedings of the 28th General Assembly of the Union of International Radio Science, New Delhi, India, 23-29 October 2005.
Kelly, Richard. E. J., A. T. C. Chang, L. Tsang, and J. L. Foster. 2003. A Prototype AMSR-E Global Snow Area and Snow Depth Algorithm. IEEE Transactions on Geoscience and Remote Sensing 41(2): 230-242.
Kelly, Richard E. J., A. T. C. Chang, J. L. Foster, Dorothy K. Hall, B. b. Stankov, and A. J. Gasiewski, A. J. Testing AMSR-E Snow Retrievals with Cold Lands Processes Experiment Data. Poster presented at the AGU Fall Meeting, San Fransisco, CA., 8-12 December 2003.
Kelly, Richard E. J., A. T. C. Chang, J. L. Foster, and Dorothy K. Hall. The Effect of Sub-pixel Areal Distribution of Snow on the Estimation of Snow Depth from Spaceborne Passive Microwave Instruments. Poster presented at the Proceedings of IGARSS, Toulouse, France, 21-25 July 2003.
Knowles, K. 2004. EASE-Grid Land Cover Data Resampled from Boston University Version of Global 1 km Land Cover from MODIS 2001, Version 4. Boulder CO, USA: National Snow and Ice Data Center. Digital media.
Krenke, A. 1998, updated 2004. Former Soviet Union Hydrological Snow Surveys, 1966-1996. Edited by NSIDC. Boulder, CO: National Snow and Ice Data Center/World Data Center for Glaciology. Digital media.
Kunzi, K. F., S. Patil and H. Rott. 1982. Snow-cover Parameters Retrieved from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Data. Geoscience and Remote Sensing: IEEE Transactions GE-20(4):452-467.
For more information regarding related publications, see the Research Using AMSR-E Data Web page.
The following acronyms are used in this document.
|AMSR-E||Advanced Microwave Scanning Radiometer - Earth Observing System|
|CD-ROM||Compact Disc Read-Only Memory|
|DLT||Digital Linear Tape|
|DVD||Digital Versatile Disc|
|EASE-Grid||Equal-Area Scalable Earth Grid|
|EOS||Earth Observing System|
|FTP||File Transfer Protocol|
|HDF-EOS||Hierarchical Data Format - Earth Observing System|
|IFOV||Instantaneous Field of View|
|NASA||National Aeronautics and Space Administration|
|NSIDC||National Snow and Ice Data Center|
|PNG||Portable Network Graphics|
|RSS||Remote Sensing Systems|
|SCF||Science Computing Facility|
|SIPS||Science Investigator-led Processing System|
|SWE||Snow Water Equivalent|
|TLSIPS||Team Lead SIPS|
|URL||Universal Resource Locator|