徐致真,陈静,王勇,李红祺. 2019. 中尺度降水集合预报随机参数扰动方法敏感性试验[J]. 气象学报, (0):-, doi:10.11676/qxxb2019.039
中尺度降水集合预报随机参数扰动方法敏感性试验
Sensitivity test of stochastically Perturbed Parameterizations (SPP) scheme for mesoscale precipitation ensemble prediction
投稿时间:2018-08-14  修订日期:2018-10-20
DOI:10.11676/qxxb2019.039
中文关键词:  区域集合预报,随机参数扰动方法,时间尺度,空间尺度
英文关键词:Regional ensemble forecast, Stochastically perturbed parameterization, Temporal decorrelation scales,Spatial decorrelation scales
基金项目:国家重点研发计划项目(SQ2018YFC150096),国家科技支撑计划项目(2015BAC03B01)
作者单位E-mail
徐致真 中国气象科学研究院 409238256@qq.com 
陈静 中国气象局数值预报中心 chenj@cma.gov.cn 
王勇 奥地利中央气象和地球动力学研究所 yong.wang@zamg.ac.at 
李红祺 中国气象局数值预报中心 leehqli@gmail.com 
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中文摘要:
      [目的] 中尺度降水模式预报具有很大的不确定性,为更好地描述与模式降水预报密切相关的物理过程关键参数的不确定性,【方法与资料】基于中国气象局GRAPES(Global/Regional Assimilation and Prediction System)中尺度区域集合预报模式,从对模式降水预报不确定性有较大影响的积云对流、云微物理、边界层及近地面层等四个参数化方案中选取了18个关键参数,设计了一种随机参数扰动方案(Stochastically Perturbed Parameterization,SPP),并通过2015年6至7月总计10天的随机扰动集合预报试验,对比分析了SPP方案对不同物理过程参数扰动敏感性、随机场时空尺度敏感性、能量变化特征及其集合预报效果。【结果】结果显示:对所选择的任一物理过程参数化方案增加SPP扰动后,降水及等压面要素的概率预报技巧优于无SPP扰动的预报,而扰动积云对流和边界层过程中的参数较扰动云微物理过程中的参数影响更显著,,且同时扰动积云对流、云微物理、边界层及近地面层参数化方案中的18个参数的集合预报效果优于扰动任何单一物理过程中的部分参数,表明SPP方案能够有效地提高中尺度降水概率预报技巧;从能量变化特征可知,不同物理过程的参数扰动对动能、内能和总能量的影响层次和特征有所不同,但总体而言,扰动前后各项能量基本相同;随机场时空尺度敏感性试验发现,SPP扰动随机场时间、空间相关尺度对集合预报效果有明显影响,当扰动随机场选用12h失时间相关系数及20n截断波数时,集合预报结果最优。【结论】上述结果表明,SPP随机参数扰动方案不仅能够有效提高集合概率预报效果,还能够提高集合预报降水概率预报技巧,具有良好的业务应用与发展前景。
英文摘要:
      There are great uncertainties in mesoscale precipitation model forecasting, to better represent the uncertainties of key parameters which are closely related to model precipitation forecasting in the physical processes, a Stochastically Perturbed Parameterizations (SPP) scheme consisting of temporally and spatially varying perturbations of 18 key parameters in the cumulus convection, microphysics, boundary layer and surface layer parameterization schemes is developed in the Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS) of China Meteorological Administration. Sensitivities of parameter perturbations in different physical processes, spatial and temporal decorrelation scales, as well as energy evolution characteristics and ensemble prediction performance were analyzed by conducting the sensitivity experiments for 10 summer days in June and July 2015. The main conclusions are as follows: Almost all SPP experiments which added parameter perturbation in selected physical parameterization schemes are characterized by better probabilistic forecasting skill and are superior to the experiment without SPP in the precipitation and isobar verification, and perturbing the parameters in the cumulus convection and boundary layer schemes has more significant impact than perturbing the parameters in the microphysics scheme. Furthermore, simultaneously disturbing the parameters in the cumulus convection, microphysics, boundary layer and surface layer parameterization schemes achieves better ensemble prediction performance than perturbing part of the parameters in any single parameterization scheme, indicating that SPP can effectively improve the meso-scale precipitation probabilistic forecasting skill .The characteristics of energy evolution indicate that perturbing parameters in different physical processes affects different levels of energy and has different characteristics, but overall the SPP scheme has little influence on the internal energy, kinetic energy and total energy of the atmosphere, and the energy before and after the perturbation is basically the same. By conducting the sensitivity experiments on the spatial and temporal decorrelation scales of random patterns, it is found that the choice of spatial and temporal decorrelation scales of random patterns has great impact on the ensemble prediction performance. The optimal ensemble prediction results can be obtained when choosing the temporal decorrelation coefficient of 12h and the truncated wave number of 20n of the stochastic perturbation field. In conclusion, the SPP scheme can not only effectively improve the performance of ensemble probability prediction, but also improve the ensemble prediction skill of precipitation forecasting. It has promising prospect of operational application and development.
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