GSTDTAP
DOI10.1029/2019GL085722
A Wet-Bulb Temperature-Based Rain-Snow Partitioning Scheme Improves Snowpack Prediction Over the Drier Western United States
Wang, Yuan-Heng1; Broxton, Patrick2; Fang, Yuanhao1; Behrangi, Ali1; Barlage, Michael3; Zeng, Xubin1; Niu, Guo-Yue1,4
2019-12-10
发表期刊GEOPHYSICAL RESEARCH LETTERS
ISSN0094-8276
EISSN1944-8007
出版年2019
卷号46期号:23页码:13825-13835
文章类型Article
语种英语
国家USA
英文摘要

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.


英文关键词precipitation partitioning wet-bulb temperature Noah-MP land surface model snow water equivalent
领域气候变化
收录类别SCI-E
WOS记录号WOS:000501673900001
WOS关键词PRECIPITATION-PHASE ; LAND-SURFACE ; MOUNTAIN SNOWPACK ; PARAMETERIZATION ; ACCUMULATION ; SENSITIVITY ; TRENDS ; MODEL ; VARIABILITY ; SIMULATION
WOS类目Geosciences, Multidisciplinary
WOS研究方向Geology
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文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/225179
专题环境与发展全球科技态势
作者单位1.Univ Arizona, Hydrol & Atmospher Sci, Tucson, AZ USA;
2.Univ Arizona, Sch Nat Resources Environm, Tucson, AZ USA;
3.Natl Ctr Atmospher Res, Res Applicat Lab, POB 3000, Boulder, CO 80307 USA;
4.Univ Arizona, Biosphere 2, Tucson, AZ USA
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GB/T 7714
Wang, Yuan-Heng,Broxton, Patrick,Fang, Yuanhao,et al. A Wet-Bulb Temperature-Based Rain-Snow Partitioning Scheme Improves Snowpack Prediction Over the Drier Western United States[J]. GEOPHYSICAL RESEARCH LETTERS,2019,46(23):13825-13835.
APA Wang, Yuan-Heng.,Broxton, Patrick.,Fang, Yuanhao.,Behrangi, Ali.,Barlage, Michael.,...&Niu, Guo-Yue.(2019).A Wet-Bulb Temperature-Based Rain-Snow Partitioning Scheme Improves Snowpack Prediction Over the Drier Western United States.GEOPHYSICAL RESEARCH LETTERS,46(23),13825-13835.
MLA Wang, Yuan-Heng,et al."A Wet-Bulb Temperature-Based Rain-Snow Partitioning Scheme Improves Snowpack Prediction Over the Drier Western United States".GEOPHYSICAL RESEARCH LETTERS 46.23(2019):13825-13835.
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