CMA对流尺度集合随机物理倾向扰动参数敏感性研究

A Sensitivity study of the stochastic perturbed parameterization tendencies (SPPT) scheme in the CMA convection-permitting ensemble prediction system

  • 摘要: 有效表征模式不确定性是提升高分辨率对流尺度集合预报技巧的关键。随机物理倾向扰动方案(Stochastic Perturbed Parameterization Tendencies, SPPT)是表征模式不确定性的主流方法之一,其效果与扰动幅度、时间相关尺度与水平扰动尺度3个参数相关。对于业务化的中国气象局3 km CMA-REPS(Regional Ensemble Prediction System of China Meteorological Administration) v4.0,其SPPT参数的协同优化尚缺乏系统研究。基于CMA-REPS v4.0对流尺度集合预报系统,选取2024年华北13个强降水个例,开展SPPT参数敏感性试验,分析了等压面和近地面变量、降水的集合预报技巧以及扰动能量增长特征。结果表明:(1)采用较小扰动幅度(标准差0.35)并保持边界层扰动不衰减能最有效提升变量集合预报技巧。(2)3 h时间尺度利于初期12 h内集合预报技巧改进,而6 h时间尺度在积分24 h后表现更优。500 km水平尺度综合效果最佳:相比1000 km尺度,它能提升多数变量的离散度与一致性;进一步减小至200 km虽能改善初期12 h内小雨和中雨预报,但会导致18 h后集合预报技巧下降。(3)时空尺度显著影响扰动能量演变。3 h时间尺度促进初期12 h内各尺度扰动能量增长,6 h时间尺度则更利于积分18 h后的扰动发展。500 km水平尺度最有利于扰动动能和潜热能发展;200 km水平尺度虽能在初期增强低层扰动能量并促进对流活跃期的小尺度扰动增长,但大、中尺度分量以及中后期的扰动能量发展最小。综上,在CMA-REPS v4.0业务采用的单一尺度框架下,推荐使用标准差为0.35且边界层不衰减、时间相关尺度6 h、水平扰动尺度500 km的组合。

     

    Abstract: Effective represention of model uncertainty is crucial for improving the forecast skill of convection-permitting ensemble prediction system. The Stochastic Perturbed Parameterization Tendencies (SPPT) scheme is one of the primary approaches used to represent model uncertainty, and its effect is controlled by three parameters: Perturbation magnitude, temporal correlation scale, and horizontal perturbation scale. There have been few studies on the optimization of these three parameters for the operational 3 km CMA-REPS (Regional Ensemble Prediction System of China Meteorological Administration) v4.0. Based on CMA-REPS, this study selects 13 heavy rainfall cases in North China in 2024 to conduct SPPT parameter sensitivity experiments. The forecast skill for upper-air and surface variables, precipitation, and perturbation energy growth are analyzed. First, using a smaller magnitude (with a standard deviation of 0.35) and dropping attenuation of the perturbations within the boundary layer most effectively enhances the forecast skill of the variables. Second, a 3 h temporal scale is conducive to improving the forecast skill within the initial 12 h, whereas a 6 h time scale performs better after 24 h of integration. A horizontal scale of 500 km yields the best overall performance. Compared to a 1000 km scale, it improves the spread and consistency for most variables. Further reducing the scale to 200 km can improve light and moderate rain forecasts within the initial 12 h but leads to a decline in the forecast skill after 18 h. Third, spatiotemporal scales significantly influence the perturbation energy growth. The 3 h temporal scale promotes the perturbation energy growth across scales within the initial 12 h, while the 6 h scale is more favorable for perturbation growth after 18 h. The 500 km horizontal scale is most beneficial for the development of difference kinetic energy and difference latent energy. Although the 200 km horizontal scale can initially enhance low-level perturbation energy and promote smaller-scale perturbation growth during convectively active periods, it results in the minimal development of larger- and meso-scale components, as well as perturbation in the middle and late periods of integration. In conclusion, a 0.35 standard deviation with unattenuated boundary layer perturbations, a 6 h temporal scale, and a 500 km horizontal scale are recommended.

     

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