Assimilating Spaceborne Passive Microwave Measurements into a Land Surface Model to Estimate Snow Water Equivalent in the Yampa River Basin

Thursday, 18 December 2014
Rhae-Sung Kim, Ohio State University Main Campus, Columbus, OH, United States, Dongyue Li, The Ohio State University, Columbus, OH, United States and Michael T Durand, Ohio St Univ-Earth Sciences, Columbus, OH, United States
Acquiring accurate spatiotemporal snow information over large areas for understanding snowcover in the global and regional water and energy balances is crucial and has motivated snow characterization via remote sensing. The passive microwave (PM) measurements have been widely used and invested in order to obtain information about snowpack properties. In this paper, we utilize a snow data assimilation system to estimate snow water equivalent (SWE) in the Yampa River basin in the Colorado Rockies within the NASA Cold Land Processes Experiment (CLPX) area of 2002-2003. The Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) observations were used. Forcing data were derived from the North American Land Data Assimilation v2 (NLDAS-2) dataset. Also, the Microwave Emission Model of Layered Snowpacks (MEMLS) was used to convert the snow state variables to brightness temperatures. The ensemble Kalman filter was directly employed to assimilate PM brightness temperature data into a land surface model snow scheme. Assimilation results are compared with SNOTEL and snow course observations.