摘要
为了优化和提高洪水预报的准确性,需要通过评估2024年长江流域洪水期间不同数值模式预报产品对面雨量的预报效果,理解目前业务模式的预报能力,通过对多种数值模式(EC、GRAPES-GFS、NCEP、GERMAN及日本模式)进行降水预报的TS评分及平均绝对误差检验,分析不同模式在长江2024年第1号、2号洪水期间面雨量预报中的表现。结果表明:①在长江2024年第1号洪水期间,EC模式在降水预报中表现最为稳定,整体预报效果最佳,尤其在大雨和暴雨预报中具有较高的参考价值;GRAPES-GFS模式次之,其在暴雨预报中展现出较强的参考性;相比之下,日本模式在各预报时效中的表现较差,尤其是在48~96 h的预报中,TS评分显著低于其他模式。②长江2024年第2号洪水期间的预报结果显示,GERMAN模式在小雨和中雨预报中表现较好,但在大雨和暴雨预报中不及EC和GRAPES-GFS模式。总体而言,各模式在不同量级和区域的降水预报能力存在显著差异,深入理解这些模式间的差异可为未来的降水预报工作提供参考。
This study aims to evaluate the area precipitation forecasting performance of various numerical models during the 2024 flood in the Changjinag River Basin,and understand the forecasting capabilities of current operational models.Through TS score and mean absolute error(MAE),we analyze the precipitation forecasting performances of different numerical models(EC,GRAPES-GFS,NCEP,GERMAN,and the Japanese model)during the 2024 Changjinag River No.1 flood and No.2 flood.The results indicated that:①during the 2024 Changjinag River No.1 flood,the EC model demonstrated the most stable performance in precipitation forecasting,exhibiting the best results,particularly in forecasting heavy rain and rainstorms.The GRAPES-GFS model ranked second,showing good performance,especially in rainstorm forecasts.In contrast,the Japanese model performed poorly across all forecast periods,with TS scores lower than other models,particularly in the 48 to 96-hour forecasts.②Additionally,analysis during the 2024 Changjinag River No.2 flood revealed that the GERMAN model performed well in forecasting light and middle rain,but was less effective than the EC and GRAPES-GFS models in forecasting heavy rain and rainstorm.Overall,there were large differences in the precipitation forecasting capabilities of various models facing different magnitudes of rain and regions.A deeper understanding of these inter-model differences can provide important references for future precipitation forecasting efforts.
作者
董轩
徐卫立
邱辉
DONG Xuan;XU Weili;QIU Hui(Bureau of Hydrology,Changjiang Water Resources Commission,Wuhan 430010,China;China Yangtze Power Co.,Ltd.,Yichang 443002,China)
出处
《人民长江》
北大核心
2024年第12期22-29,共8页
Yangtze River
基金
湖北省自然科学基金联合基金项目(2023AFD094)
国家自然科学基金项目(U2340205)
国家重点研发计划项目(2022YFC3002701)。
关键词
面雨量预报
TS评分
数值模式产品
2024年长江洪水
area precipitation forecast
TS score
numerical model products
2024 flood in the Changjinag River Basin
