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

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. Plain Language Summary The partitioning between rainfall and snowfall is important for understanding the impacts of climate change and water resource availability. Most land surface and hydrological models use surface air temperature to partition precipitation into rain and snow and thus underestimate snowfall and snow mass accumulated on the ground in the drier Western United States. A falling hydrometeor evaporates or sublimates at its surface depending on the humidity of the surrounding air and cools off, resulting in a surface temperature that is cooler than the air temperature. The depressed surface temperature is close to the wet‐bulb temperature. We developed a scheme using the wet‐bulb temperature and tested it with a physically based snow model over the contiguous United States. The testing results strongly support the use of wet‐bulb temperature, which enhances snowfall and the snow mass on the ground more significantly over the higher and drier mountains in the Western United States, while it retains the modeling accuracy in the more humid Eastern United States. Key Points We developed a snow‐rain partitioning scheme using the wet‐bulb temperature and tested it with a physically based snow model over CONUS The new scheme produces more snowfall and snow mass on the ground that agree better with a groun