589 Evaluation of the Weather Research and Forecasting Model Estimates of Mountain Snow

Wednesday, 13 January 2016
Melissa L. Wrzesien, Ohio State University, Columbus, OH; and M. T. Durand and T. M. Pavelsky

Snow conditions, such as snow water equivalent (SWE) measurements, have a high degree of spatial variability and can change drastically over a short distance. For this reason, scale is an important consideration when studying seasonal snow, especially in complex mountain environments where topography and vegetation affect snow cover distribution. Here we investigate the implications of scale and resolution for two domains the Sierra Nevada Mountains of California and the Coast Mountains of British Columbia. We evaluate water year 2009 (October 2008 through September 2009) for the Sierra Nevada and water year 2005 for the Coast Mountains, since both years experienced average snow accumulation conditions for their respective region. In particular, we compare 3 km SWE estimates from the Weather Research and Forecasting (WRF) model with both fine and coarse resolution datasets, including SNODAS, NLDAS, GLDAS, and VIC. Each gridded dataset is additionally compared to in situ observations to determine the bias of each model compared to measurements. On average, biases from the coarser datasets are nearly an order of larger than biases from WRF. For an average snow accumulation year in the Sierra Nevada, biases range from 75 mm for WRF to over -600 mm for GLDAS. Results highlight the importance of fine-scale estimates of SWE for decreasing biases in mountainous environments.
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