气象与环境学报2025，Vol.41Issue(1)：35-42,8.DOI:10.3969/j.issn.1673-503X.2025.01.004
CMA多模式对台风"梅花"在东北降水预报的检验分析
Spatial verification of CMA models on precipitation forecast for typhoon"Meihua"in Northeast China
摘要
Abstract
In this study,the SAL(structure amplitude and location)spatial verification method is used to evaluate the 12～36 hours total precipitation forecast performance of five China Meteorological Administration(CMA)models for the strongest precipitation day during the remnants of Typhoon"Meihua"over Northeast China in Sep-tember of 2022(from 08:00 on September 16 to 08:00 on September 17,with the typhoon category as a tropical depression).The results can be summarized as follows:The CMA-GFS model exhibits the best forecast of precipi-tation structure,amplitude,and location in this case,while for the other models tend to underestimate extreme pre-cipitation,except CMA-BJ.Due to its well-performance in predicting the location,intensity,and moving speed of the 850 hPa low-level jet,the CMA-GFS model has the best precipitation forecast.Meanwhile,the faster speed of the low-level jet predicted by the CMA-TYM model leads to a smaller area of significant rainfall exceeding 100 mm.The CMA-GFS model demonstrates better forecasting performance as the lead time shortens,with notable ad-vantages in now-casting forecasts but limited skill for longer lead times.In contrast,although CMA-TYM model exhibits poorer structure and amplitude performance in approaching lead time,it is the earliest model to provide in-dicative signals for the general location and magnitude of heavy precipitation.The summary of CMA models fore-cast performance in typhoon remnant system precipitation aims to enhance the applicability of numerical weather models in China in similar scenarios of the future.关键词
CMA模式/SAL空间检验/台风残余系统降水/偏差分析Key words
CMA models/SAL(structure amplitude and location)spatial verification/Typhoon remnant precipi-tation forecast/Bias analysis分类
大气科学引用本文复制引用
姚凯,朱晓彤,陈长胜,秦玉琳,周东雪,朴美花..CMA多模式对台风"梅花"在东北降水预报的检验分析[J].气象与环境学报,2025,41(1):35-42,8.基金项目
中国气象局沈阳大气环境研究所和东北冷涡重点开放实验室联合开放基金(2023SYIAEKFMS07)、吉林省科技发展计划项目(20220203186SF)共同资助. （2023SYIAEKFMS07）
