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

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 (T-a) 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 (T-w), which is closer to the surface temperature of a falling hydrometeor than T-a. T-w becomes more depressed in drier environments as derived from T-w depression equation using T-a and surface air humidity, resulting in a greater fraction of snowfall. We implemented this new T-w 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 T-w 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.