数值天气预报模式对雅砻江下游强降水预报能力检验研究（英文）  

A weather research and forecasting model evaluation for simulating heavy precipitation over the downstream area of the Yalong River Basin

作　　者：Ming-xiang YANG Yun-zhong JIANG Xing LU Hong-li ZHAO Yun-tao YE Yu TIAN 

机构地区：[1]Department of Hydraulic Engineering, Tsinghua University [2]State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research [3]China Eastern Route Corporation of South-to-North Water Diversion

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2015年第1期18-37,共20页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：Project supported by the National Natural Science Foundation of China(Nos.51109177 and 51209223);the Major National Science and Technology Program(No.2012ZX07205-005);the National Key Technology R&D Program of China during the"12th Five-Year Plan"(No.2013BAB05B01);the Doctoral Thesis Innovation Program of the China Institute of Water Resources and Hydropower Research

摘　　要：目的:检验数值天气预报模式(WRF)在雅砻江下游对强降水的预报能力,并找出表现最优的参数化方案组合。创新点:首次针对雅砻江流域检验WRF模式对强降水的预报能力,并加入了计算时间作为评价的重要参考。方法:通过三场强降水事件,利用七种常用的云微物理参数化方案(Kessler,Lin et al.(Lin),SingleMoment 3-class(WSM3),Single-Moment 5-class(WSM5),Ferrier,Single-Moment 6-class(WSM6),和New Thompson et al.(NTH))和3种积云对流参数化方案(Kain-Fritsch(KF),Betts-Miller-Janjic(BMJ)和Grell-Devenyi(GD))的组合,对WRF模式在雅砻江下游的降水预报能力进行检验。为了评价WRF模式的预报能力,引入探测率(POD),空报率(FAR),BIAS和公平预报评分(ETS),对比不同方案组合的降水空间分布和站点预报的有效性。同时,均方根误差(RMSE)等指标被用来评价面雨量预报的精确性。除常规评价外,还将计算时间作为方案评价的重要参考,在满足精度需求的前提下优先选择计算效率高的方案组合。结论:1.WRF模式能够适用于雅砻江下游强降水预报;2.WSM3以及GD参数化方案组合的表现最为有效和稳定。The forecasting capability of the weather research and forecasting （WRF） model for heavy precipitation in the downstream area of the Yalong River Basin in Southwest China was evaluated for the first time through the simulation of three heavy precipitation events with seven commonly used microphysics parameterization schemes （MPS） （Kessler, Linet al. （Lin）, Single-Moment 3-class （WSM3）, Single-Moment 5-class （WSM5）, Ferrier, Single-Moment 6-class （WSM6）, and New Thompson et al. （NTH）） and three cumulus parameterization schemes （CPS） （Kain-Fritsch （KF）, Betts-Miller-Janjic （BMJ）, and Grell-Devenyi （GD））. Of the three rainfall events, the first two are typical large-area heavy precipitation events in the Yalong River Basin and consist of several continuous storms. The third one is a heavy precipitation event with only one storm. In this study, a triple nested domain with a 3-km grid resolution inner-most domain over the study area was configured for the WRF model. We employed the probability of detection （POD）, false alarm ratio （FAR）, BIAS, and equitable threat （ET） scores to compare the spatial distribution of heavy rainfall created by the WRF model with the observations from the gauges in the down-stream area of the river basin. The root mean squared errors （RMSEs） at each sub river basin and the whole downstream of Yalong River Basin were also calculated for the evaluation. In addition, it is important to include the computation efficiency when choosing a scheme combination. We recorded the time consumption for each model simulation and made comparisons for se-lecting the optimum scheme with less time consumption and acceptable prediction accuracy. Through comprehensive comparison, the scheme combination of WSM3 and GD holds a stable performance in leveraging the prediction accuracy and computation efficiency for the heavy precipitation events.

关 键 词：WRF模式 雅砻江流域 降水模拟 积云对流参数化方案 云微物理参数化方案 

分 类 号：P456.7[天文地球—大气科学及气象学]