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Abstract: 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, Lin et 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 downstream 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 selecting 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.

天气预报模式对雅砻江下游强降水预报能力检验研究

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