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A Wet‐Bulb Temperature‐Based Rain‐Snow Partitioning Scheme Improves Snowpack Prediction Over the Drier Western United States
Author(s) -
Wang YuanHeng,
Broxton Patrick,
Fang Yuanhao,
Behrangi Ali,
Barlage Michael,
Zeng Xubin,
Niu GuoYue
Publication year - 2019
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2019gl085722
Subject(s) - snow , snowpack , environmental science , snowmelt , precipitation , atmospheric sciences , water equivalent , wet bulb temperature , humidity , climatology , meteorology , hydrology (agriculture) , geology , geography , geotechnical engineering
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.