基于SAL方法对一次区域性大暴雨过程多模式预报空间检验及误差分析

采用SAL定量降水预报检验方法，对2017年梅雨期一次区域性极端降水过程EC-THIN、RIOF、NCEP、CMA的高分辨率数值预报产品，从结构、强度和位置3个方面进行检验对比，同时对72 h内各模式降水预报稳定性开展检验分析。在此基础上，剖析了降水预报误差成因。分析发现：（1）在降水分布上，RIOF、EC-THIN和CMA预报的雨带走向与实况基本一致，NCEP预报主雨带范围偏大，暴雨区偏东；（2）雨区结构上RIOF和EC-THIN把握较好，NCEP和CMA在降水强度方面预报较好，位置预报上各家误差均较小，其中CMA误差最小；（3）EC-THIN和NCEP在结构、强度和位置预报上均有较好的稳定性。CMA在降水强度方面预报稳定较好，位置预报上调整较大。RIOF在降水结构预报上稳定性较好，落区预报上变化幅度较大；（4）降水预报误差根本原因是由系统预报误差而形成，系统强度、位置、移动直接影响着降水偏差。垂直物理量的预报偏差对降水时段、加强、强度也具有一定影响。

SAL quantitative verification and error analysis for multi-model forecast of a regional heavy rain process

By using SAL quantitative verification method, a heavy rainfall prediction in Meiyu period, 2017 of EC-THIN,RIOF,NCEP and CMA models were verified from amplitude,location and structure aspects. The causes of precipitation forecast errors, and the stability of 72 h forecast of the models were analyzed. The results show that:(1)RIOF, EC-THIN and CMA forecast the trend of rainband is basically consistent with the observation. NCEP forecast the main rainband range is larger and the rainstorm area is east. (2)RIOF and EC-THIN are better in the structure prediction of rainy area, NCEP and CMA are better in amplitude precipitation, and the location prediction errors are all small, and the error of CMA is minimal. (3)EC-THIN and NCEP show better stability in structure amplitude and location prediction. CMA shows better stability in intensity prediction and a large adjustment in location prediction. RIOF shows better stability in structure prediction and a large variation in area prediction. (4)The primary cause of precipitation forecast error is the system forecast error. The intensity, position and movement of the system directly affect the precipitation. The forecast of vertical physicals also affect the precipitation period, enhancement and intensity.