Towards the Improved Estimates of Mountain Snow Water Equivalent Using Space-borne Passive Microwave Measurements: an Ensemble Kalman Batch Reanalysis over the Upper Kern Basin, Sierra Nevada, USA

Tuesday, 16 December 2014
Dongyue Li, The Ohio State University, Columbus, OH, United States, Michael T Durand, Ohio St Univ-Earth Sciences, Columbus, OH, United States and Steven A Margulis, University of California Los Angeles, Los Angeles, CA, United States
Improving the estimate of snow water equivalent (SWE) in the Sierra Nevada has merit for California, given the ongoing drought that has lasted for years. In this study, we carried out an experiment to estimate SWE in the Upper Kern Basin, Sierra Nevada, by assimilating AMSR-E observed brightness temperatures (Tb) into a coupled hydrology and radiative transfer model using an ensemble Kalman batch reanalysis. The data assimilation framework merges the complementary SWE information from modeling and observations for an improved SWE estimate. The novelty of this assimilation study is that both the modeling and the radiance data processing were specifically improved to provide more information about SWE. With the enhanced SWE signals in both simulation and observation, the batch reanalysis should stand a better chance of successfully improving the SWE estimates.

The modeling was at a very high resolution (90m) and spanned a range of mountain environmental factors to better characterize the effects of the mountain environment on snow distribution and radiance emission. We have developed a dynamic snow grain size module to improve the radiance modeling during the intense snowfall events. The AMSR-E 37GHz V-pol observed Tb was processed at its native footprint resolution at ~100 km2. In the batch assimilation, the model predicted the prior SWE and Tb; the prior estimate of an entire year was then updated by the dry-season observations at one time. One advantage of this is that the prior SWE of a certain period is updated using the observations both before and after this period, which takes advantage of the temporally continuous signal of the seasonal snow accumulation in the observations.

We found the posterior SWE estimates showed improved accuracy and robustness. During the study period of 2003 to 2008, at point-scale, the average bias of the six-year April 1st SWE was reduced from -0.17 m to -0.01m, the average temporal SWE RMSE of the dry season and the entire year decreased by 51.2% and 11.7%, respectively. The basin-scale results showed that the average April 1st SWE bias reduced from -0.17m to -0.11m, and the temporal SWE RMSE of the dry season and the entire year decreased by 23.6% and 13.1%, respectively. Future work includes assimilating visible band observations to improve the overall SWE estimate in the ablation season.