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气象:2021,47(8):953-965
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随机参数扰动在一次山地暴雨集合预报中的对比研究
熊洁,李俊,王明欢
(中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室,武汉 430205)
Comparative Study of Stochastically Perturbed Parameterization in Ensemble Forecast of a Mountain Rainstorm Event
XIONG Jie,LI Jun,WANG Minghuan
(Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, CMA, Wuhan 430205)
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投稿时间:2020-08-03    修订日期:2021-06-21
中文摘要: 基于WRF v3.9模式使用随机参数扰动MYNN边界层方案和RUC陆面过程方案参数对我国西南山地一次特大暴雨模拟,对比评估出山地暴雨集合预报中针对MYNN边界层、RUC陆面过程方案随机参数扰动的较优设置,主要结论如下:随机扰动MYNN边界层方案(SPPM)和RUC陆面过程方案(SPPR)参数试验,扰动的主要是地面和模式低层的变量,扰动能量从模式低层逐步向高层发展, 两者相比扰动边界层方案能获得更大的扰动能量; 较去空间相关尺度而言,SPPM方案对去时间相关参数的变化更敏感,而SPPR方案由于其扰动能量总体偏小,去空间和时间相关参数的变化对其集合预报性能影响相对较小;SPPM方案中去时间相关选择6 h,去空间尺度选择70 km可以获得较好的集合预报技巧,SPPR方案中相对而言去时间相关选择6 h,去空间尺度选择50 km可以获得较好的集合预报技巧。
Abstract:Based on WRF v3.9 model, the stochastically perturbed parameterization is used to perturb MYNN boundary layer and RUC land surface process scheme parameters to simulate a heavy rainstorm in southwestern mountains of China. The optimal settings for parameter perturbations of MYNN boundary layer and RUC land surface process in mountain rainstorm ensemble forecast are explored. The main conclusions are as follows. In the random disturbance MYNN boundary layer scheme (SPPM) and RUC land surface process scheme (SPPR) parameters, the disturbance is mainly for the variables at the surface and the lower level of the model. The disturbance energy gradually develops from lower levels to higher levels in the model, and the SPPM can get greater disturbance energy than SPPR. The SPPM scheme is more sensitive to the variation of temporal correlation parameters than the spatial correlation parameters. However, as the perturbation energy of SPPR scheme is generally small, the variations of spatio temporal correlation parameters have relatively small influence on its ensemble prediction performance. In SPPM scheme, a better ensemble prediction skill can be obtained by the temporal correlation selection for 6 h and the spatial scale selection for 70 km, while in SPPR scheme, a better ensemble prediction skill can be obtained by the temporal correlation for 6 h and the spatial scale for 50 km.
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基金项目:国家重点研发计划(2018YFC1507200和2016YFE0109400)共同资助
引用文本:
熊洁,李俊,王明欢,2021.随机参数扰动在一次山地暴雨集合预报中的对比研究[J].气象,47(8):953-965.
XIONG Jie,LI Jun,WANG Minghuan,2021.Comparative Study of Stochastically Perturbed Parameterization in Ensemble Forecast of a Mountain Rainstorm Event[J].Meteor Mon,47(8):953-965.