基于改进U-Net模型的湖南地区定量降水预报订正试验  

Research on Quantitative Precipitation Correction Forecasting Based on the Improved U-Net Model in Hunan

作　　者：周莉 许霖 陈鹤 兰明才 欧小锋 周悦[3,4] 谢忆南 肖思晗 ZHOU Li;XU Lin;CHEN He;LAN Mingcai;OU Xiaofeng;ZHOU Yue;XIE Yinan;XIAO Sihan(Hunan Key Laboratory of Meteorological Disaster Prevention and Mitigation,Changsha 410118,China;Hunan Meteorological Observatory,Changsha 410118,China;China Meteorological Administration Basin Heavy Rainfall Key Laboratory,Wuhan 430205,China;Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research,Institute of Heavy Rain,Wuhan 430205,China;Shaoyang Meteorological Bureau,Shaoyangy,Hunan 422000,China)

机构地区：[1]气象防灾减灾湖南省重点实验室,湖南长沙410118 [2]湖南省气象台,湖南长沙410118 [3]中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室,湖北武汉430205 [4]中国气象局流域强降水重点开放实验室,湖北武汉430205 [5]邵阳市气象局,湖南邵阳422000

出　　处：《热带气象学报》2024年第6期1005-1017,共13页Journal of Tropical Meteorology

基　　金：湖南省自然科学基金项目-青年基金(2022JJ40214);湖南省气象局2022年重点课题(XQKJ22A005);中国气象局创新发展专项(CXFZ2023J025);中国气象局气象能力提升联合研究专项(23NLTSZ005);全国暴雨研究开放基金(BYKJ2024M08)共同资助。

摘　　要：利用2017—2022年汛期(4—9月)湖南省1912个地面观测站降水实况和欧洲中心中期天气预报一体化预报系统(European Centre for Medium-Range Weather Forecasts-Integrated Forecasting System,ECMWFIFS)最优因子集,在一种U型语义分割网络(U-Net模型)基础上结合残差网络和注意力机制网络,构建了逐时降水订正预报模型(SARU),并将模型2023年汛期预报结果与最优TS评分订正法(OTS)以及中国气象局上海数值预报模式系统(CMA-SH9)进行对比。(1)SARU模型整体的晴雨准确率、相关系数(COR)、平均绝对误差(MAE)、偏差(BIAS)分别为0.87、0.17、0.35、0.73,皆优于OTS模型和CMA-SH9模式,尤其在湘中地区,SARU模型对于预报趋势和量级有明显优势,其空报率和漏报率比例基本相当,OTS模型漏报率远超空报率,CMA-SH9则正好相反。(2)SARU模型对于分级降水频次的预报较OTS模型和CMA-SH9模式更接近实况,尤其是20mm以上降水频次,预报偏少27.29%,远优于OTS模型预报的偏少85.54%和CMA-SH9模式的偏多95.50%。(3)对于小时雨量[5,10)、[10,20)和≥20 mm这三个级别的降水,SARU模型TS、命中率(POD)、空报率(FAR)、漏报率(MAR)皆最优,尤其短时强降水,SARU模型较CMA-SH9模式有明显优势,而OTS模型的预报能力则明显不足。(4)湖南存在明显的夜雨特征,夜间时段(北京时02—08时)短时强降水频次明显高于其他时段。SARU模型很好地把握了夜间短时强降水特征,TS在夜间明显升高,尤其是在北京时05时达到峰值(0.07左右),明显优于CMA-SH9模式和OTS模型。This study presents a quantitative precipitation forecasting correction experiment in Hunan Province based on an improved U-Net model.Utilizing precipitation data from 1912 ground observation stations in Hunan during the rainy season from April to September between 2017 and 2022,along with the optimal factor set of the European Centre for Medium-Range Weather Forecast-Integrated Forecasting System(ECMWF-IFS)model,we developed an hourly precipitation forecasting correction model(SARU)on the basis of the U-Net model,integrating residual networks and attention mechanism networks.The model’s forecast results for the 2023 rainy season were compared with those corrected by using the optimal threat score(OTS)method and those from the CMA-SH9 model.The results show that:(1)The SARU model’s overall accuracy in clear/rainy forecasts,correlation coefficient,mean absolute error,and bias were 0.87,0.17,0.35,and 0.73,respectively,all of which outperformed the OTS model and the CMASH9 model,especially in central Hunan,where SARU showed a clear advantage in forecasting trends and magnitudes;its false alarm and missed forecast ratios were nearly equal,contrasting with the OTS model’s significantly higher missed forecast ratio and the CMA-SH9 model’s opposite trend.(2)The SARU model’s forecasts of categorized precipitation frequency were closer to actual observations than the OTS model and the CMA-SH9 model,particularly for precipitation exceeding 20 mm,where it was under-predicted by 27.29%,significantly better than the OTS model’s under-prediction of 85.54%and the CMA-SH9 model’s over-prediction of 95.50%.(3)For hourly precipitation at levels of[5,10),[10,20),and≥20 mm,the SARU model achieved the best threat score(TS),probability of detection,false alarm ratio,and missed alarm ratio,particularly for short-term heavy precipitation,where it significantly outperformed the CMASH9 model,while the OTS model’s forecasting capability was notably insufficient.(4)Nocturnal rainfall was pronounced in Hunan,with a noticeab

关 键 词：SARU 订正试验 U-Net OTS 逐小时降水预报 夜雨特征 

分 类 号：P435[天文地球—大气科学及气象学]