Publication: Paper/Books

A Wet‐Bulb Temperature‐Based Rain‐Snow Partitioning Scheme Improves Snowpack Prediction Over the Drier Western United States

Geophysical Research Letters 46: 13825-13835

Guo-Yue Niu

Faculty

Abstract

Accumulation of snowfall during winter and snowmelt in the subsequent spring or earlier summer provides a dominant water source in alpine regions. Most land surface and hydrological models use near‐surface air temperature (Ta) thresholds to partition precipitation into snow and rain, underestimating snowfall over drier regions. We developed a snow‐rain partitioning scheme using the wet‐bulb temperature (Tw), which is closer to the surface temperature of a falling hydrometeor than Ta. Tw becomes more depressed in drier environments as derived from Tw depression equation using Ta and surface air humidity, resulting in a greater fraction of snowfall. We implemented this new Tw scheme in the Noah‐MP land surface model and evaluated the model against a high‐quality ground‐based snow product over the contiguous United States. The results suggest that the new Tw scheme substantially improves the model skill in simulating snow depth and snow water equivalent over most snow‐covered grids, especially the higher and drier continental mountain ranges in the Western United States, while it retains the modeling accuracy over the more humid Eastern United States.

Citation

Wang, Y.‐H., Broxton, P., Fang, Y., Behrangi, A., Barlage, M., Zeng, X., Niu, G.‐Y. (2019): A Wet‐Bulb Temperature‐Based Rain‐Snow Partitioning Scheme Improves Snowpack Prediction Over the Drier Western United States . Geophysical Research Letters 46: 13825-13835. doi: 10.1029/2019GL085722