Model Uncertainty Representation for a Convection-Allowing Ensemble Prediction System Based on CNOP-P

Abstract

Formulating model uncertainties for a convection-allowing ensemble prediction system (CAEPS) is a much more challenging problem compared to well-utilized approaches in synoptic weather forecasting. A new approach is proposed and tested through assuming that the model uncertainty should reasonably describe the fast nonlinear error growth of the convection-allowing model, due to the fast developing character and strong nonlinearity of convective events. The Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP-P) is applied in this study. Also, an ensemble approach is adopted to solve the CNOP-P problem. By using five locally developed strong convective events that occurred in pre-rainy season of South China, the most sensitive parameters were detected based on CNOP-P, which resulted in the maximum variations in precipitation. A formulation of model uncertainty is designed by adding stochastic perturbations into these sensitive parameters. Through comparison ensemble experiments by using all the 13 heavy rainfall cases that occurred in the flood season of South China in 2017, the advantages of the CNOP-P-based method are examined and verified by comparing with the well-utilized stochastically perturbed physics tendencies (SPPT) scheme. The results indicate that the CNOP-P-based method has potential in improving the under-dispersive problem of the current CAEPS.

摘要

相对于全球集合预报, 对流尺度集合预报 (CAEPS) 中有关模式不确定性的研究缺乏系统性和理论基础, 成为目前研究的热点和难点. 针对强对流天气发展迅速、 非线性强等特点, 本文充分考虑了对流尺度模式误差非线性快速增长特征, 提出基于条件非线性最优参数扰动 (CNOP-P) 方法研究CAEPS模式不确定性问题的新思路. 同时, 在CNOP-P方法的计算中, 本文采用了集合求解算法, 不依赖于切线伴随模式. 我们首先选取了2017年中国华南前汛期5个典型暖区暴雨个例求解CNOP-P, 得到对累积降水预报产生最大影响的参数扰动集合, 挑选敏感物理参数; 然后, 通过随机扰动敏感参数来描述模式不确定性, 构造基于CNOP-P的模式扰动方案, 并将该方案与目前广泛应用的随机物理参数化倾向扰动方案进行对比, 展开对流尺度集合预报关于模式不确定性的敏感性试验. 2017年华南地区13个强对流过程的检验结果显示: 基于CNOP-P的模式扰动方案对于提高对流尺度集合离散度方面具有应用潜力.