Implementing the wet-bulb temperature snowfall parameterization in Noah-MP land surface model over CONUS

Yuan-Heng Wang, Patrick Broxton1, Guo-Yue Niu, Ali Behrangi, and Xubin Zeng

Hydrology and Atmospheric Sciences
The University of Arizona

Snow constitutes a valuable water resource and plays an important role both for regulating hydrological processes as well as affecting the earth’s climate system. Accurate estimation of snowpack states is important for improving numerical weather prediction forecasts, climate prediction, and water resources management. Therefore, it is essential that models used to simulate global climate and hydrological processes can accurately describe the evolution of snowpack. This study evaluates a new snowfall scheme using the wet bulb temperature in the Noah-MP land surface model (versus the original dry bulb temperature scheme) against the ground-based, UA snow data products from 1982 to 2015 across the conterminous United States (CONUS). We a) compared the climatological mean and temporal trend between the model and the UA data product, b) evaluated the model skill for two snowfall schemes for each USGS Hydrologic Unit Code 2 (HUC2) basin, and c) examined topographic effects on the modeling biases. The results indicate that using the wet bulb temperature as a criterion for snowfall improves the simulation over the mountainous US West and hopefully may improve the snowmelt runoff prediction by WRF-Hydro, the hydrological model at the core of the National Water Model.

1School of Natural Resources and the Environment, The University of Arizona, Tucson, AZ

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