EOS Validation Program

Institution: University of Colorado
                    CIRES/NSIDC
                    CB 449
                    Boulder, CO 80309
Phone: (303) 492-1828
FAX: (303) 492-1828
E-mail: rlax@kryos.Colorado.edu

Christian Matzler (Institute of Applied Physics, University of Bern, Switzerland)

February 2003
April 2004
Final Report

The extent of the seasonal snow cover, which may include as much as 50 percent of the Northern Hemisphere land surface, is an extremely important parameter in global climate and hydrologic systems due to significant effects on energy and moisture budgets. Realistic simulation of snow cover in climate models is essential for correct representation of the surface energy balance, as well as for understanding winter water storage and predicting year-round runoff. When snow covers the ground, some of the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in emissivity and associated brightness temperatures. Passive microwave signatures of seasonal snow cover are clearly characterized by this strong dielectric contrast between snow-covered and snow-free ground, by decreasing emissivity (dry snow) with increasing microwave frequency (negative spectral gradient) and by decreasing emissivity with increasing snow mass. Because of this clear capability, a microwave snow cover algorithm is under development by the EOS AMSR-E Science Team. Our validation study will supplement and enhance the validation activities currently planned by the Science Team for the AMSR-E Level 2 and Level 3 snow water equivalent products.

In order to include a wide range of spatial scales, we will evaluate algorithm skill at the local or grid scale, the river basin scale, and the regional to hemispheric scale. Local or grid scale studies will be accomplished through direct participation in the NASA Cold Land Processes Experiment planned for mid-February and late March of 2002 and 2003 in northwestern Colorado. Prior snow validation efforts have primarily relied on what might be termed "data sets of opportunity" - data sets which typically were not entirely appropriate for passive microwave algorithm validation and often contributed as many questions as answers to the process. The synergy of expertise and optimal sampling scheme offered by the Cold Land Processes Experiment represents a major advancement over previous efforts to validate passive microwave snow algorithms. The synergy of multiple investigators and their collective ground-based and airborne meteorological, active and passive microwave and gamma measurement systems will provide the basis for an unprecedented validation campaign for snow cover remote sensing. The overall design and spatial sampling scheme of both the ground-based and airborne measurements constitute an optimal test environment for this validation study. Although this local or grid scale study is limited to three land cover types (grassland, coniferous forest and alpine) these regions are typical of many other locations world-wide where seasonal snow cover is the prevalent land cover during the winter season and the algorithms validated in this experiment can therefore be applied and tested over comparable land cover types in other locations.

Larger river basin scale validation will be facilitated through participation in an ongoing NASA and NSF funded program that involves an integrated near-real time monitoring and analysis of the major components of the pan-Arctic hydrologic cycle. Output from the AMSR-E snow algorithm will be compared with river discharge data compiled by this project as well as with modeled values of distributed winter precipitation. For continuous long-term observations in diverse environments at the regional to hemispheric scale we will evaluate the snow algorithms by comparison with the EOS MODIS daily global snow extent as well as the NOAA IMS daily Northern Hemisphere snow extent maps. For snow extent and water equivalent we will augment the current Science Team plan to utilize data from the United States (NWS, NOHRSC and USDA) with surface station data obtained through our ongoing collaboration with the All-Russia Research Institute of Hydrometeorological Information, Obninsk, Russia, the Satellite Meteorology Institute, Beijing, China, the Cold and Arid Regions Environmental and Engineering Research Institute, Lanzhou, China and the Canadian IDS team CRYSYS.