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

Yuan Heng Wang, Patrick Broxton, Yuanhao Fang, Ali Behrangi, Michael Barlage, Xubin Zeng, Guo Yue Niu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)13825-13835
Number of pages11
JournalGeophysical Research Letters
Volume46
Issue number23
DOIs
StatePublished - Dec 16 2019

Keywords

  • Noah-MP land surface model
  • precipitation partitioning
  • snow water equivalent
  • wet-bulb temperature

ASJC Scopus subject areas

  • Geophysics
  • Earth and Planetary Sciences(all)

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