87th AMS Annual Meeting

Tuesday, 16 January 2007: 4:00 PM
Modeling of wet snowpack evolution and radiobrightness
211 (Henry B. Gonzalez Convention Center)
Yi-Ching Chung, University of Michigan, Ann Arbor, MI; and R. D. De Roo and A. W. England
The albedo of a wet snowpack is significantly less than that of dry snowpack so that the predictive skills of weather and climate models of snow-covered regions are improved when the models accurately capture late winter and early spring snow melt and re-freezing. Because the microwave signature of wet snow is distinct from that of dry snow, satellite radiobrightness observations during this period have the potential of being used to correct current estimates of snow wetness. We have exploited this potential by developing an improved Snow-Soil-Vegetation-Atmosphere Transfer (SSVAT) model, which combines soil processes from our Land Surface Processes (LSP) with the snow pack processes of SNTHERM, and a new microwave emission model for wet snow. Late winter/early spring comparisons between SSVAT or SNTHERM performance and observations from the NASA Cold Land Processes Field Experiment Plan (CLPX) in 2003 show SSVAT providing better temperature and moisture profiles in the snowpack and the underlying soil than does SNTHERM. This has been reported elsewhere. Our objective here is to report on the performance of the new microwave emission model for wet snow.

Liquid water in snowpacks accumulates at the contact points between snow particles, forming pendular rings. The microwave absorption of these pendular rings has been modeled using the program DIELCOM. The consequent polarizability tensor and an estimate of the number density of these rings in a wet snowpack yield the absorption coefficient. We find that the absorption efficiency of a pendular ring of water is significantly greater than that of a sphere of the same quantity of liquid water, but that the predicted absorption for the same forcing of the SSVAT model was less than that for the SNTHERM model. This occurs because SSVAT predicts larger grain sizes due to the contribution of soil vapor fluxes and, consequently, fewer rings.

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