Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019GL085722 |
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 |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
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 |
引用统计 | |
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 |
推荐引用方式 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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