水-热力土壤-植被方案与第五代宾夕法尼亚州立大学- ncar中尺度模式耦合预测雪深和土壤温度的评价

B. Narapusetty, N. Mölders
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引用次数: 19

摘要

利用美国宾夕法尼亚州立大学-美国国家大气研究中心(NCAR)第五代中尺度气象模式(MM5)对波罗的海地区25个土壤温度、355个雪深和344个降水站点的观测数据,分别进行了1000次、1775次和1720次观测,对HTSVS方案进行了双向耦合。利用再分析资料对预报的近地面气象场的性能进行了评价。雪深取决于雪的变质作用、升华作用和降雪作用。由于在耦合模式中,这些过程受到预测的地表辐射通量和云和降水过程的影响,因此使用两种不同的云微物理方案和/或辐射方案进行敏感性研究。技能分数作为耦合模型在典型预测范围120小时内的性能的质量度量来计算。
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Evaluation of Snow Depth and Soil Temperatures Predicted by the Hydro–Thermodynamic Soil–Vegetation Scheme Coupled with the Fifth-Generation Pennsylvania State University–NCAR Mesoscale Model
Abstract The Hydro–Thermodynamic Soil–Vegetation Scheme (HTSVS) coupled in a two-way mode with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Meteorological Model (MM5) is evaluated for a typical snowmelt episode in the Baltic region by means of observations at 25 soil temperature, 355 snow-depth, and 344 precipitation sites that have, in total, 1000, 1775, and 1720 measurements, respectively. The performance with respect to predicted near-surface meteorological fields is evaluated using reanalysis data. Snow depth depends on snow metamorphism, sublimation, and snowfall. Because in the coupled model these processes are affected by the predicted surface radiation fluxes and cloud and precipitation processes, sensitivity studies are performed with two different cloud microphysical schemes and/or radiation schemes. Skill scores are calculated as a quality measure for the coupled model’s performance for a typical forecast range of 120 h for a typic...
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