基于机器学习的模式温度预报订正方法  

Correction Method of Model Temperature Forecast Based on Machine Learning

作　　者：刘杰 刘高平 安晶晶[1,2] 邱学兴 章颖[1,2] LIU Jie;LIU Gaoping;AN Jingjing;QIU Xuexing;ZHANG Ying(Huaihe River Basin Meteorological Center,Hefei 230031,China;Anhui Meteorological Observatory,Hefei 230031,China)

机构地区：[1]淮河流域气象中心,安徽合肥230031 [2]安徽省气象台,安徽合肥230031

出　　处：《沙漠与绿洲气象》2024年第3期96-104,共9页Desert and Oasis Meteorology

基　　金：中国气象局创新发展专项(CXFZ2022J001);安徽省气象局创新发展专项(CXM202201)。

摘　　要：基于ECWMF模式数据(地面2 m温度、10 m风、降水等多气象要素预报产品)和安徽省80个国家气象站观测资料,利用决策树、随机森林、LightGBM三种机器学习算法订正ECMWF模式0~72 h温度预报,并将其与传统MOS订正方法和主观预报产品进行对比分析。结果表明:ECMWF模式高温预报误差明显高于低温预报,在安徽皖南山区和大别山区存在较大的预报误差。随机森林对最高温度预报的表现最优,LightGBM对最低温度预报的表现最优,与ECMWF模式结果相比,预报准确率分别提高了18.16%和5.19%。高山站点融合周围站点信息的机器学习模型能有效降低高、低温的预报误差。机器学习在高温和寒潮天气过程中相比主观订正仍有良好表现,能显著优化或改善数值模式在转折天气中的温度预测精度。Based on the ECWMF model forecast products(2 m temperature,10 m wind,precipitation,etc.)and the 2 m temperature historical observation data of 80 national meteorological stations in Anhui province,three machine learning algorithms,decision tree(DT),random forest(RF)and light gradient boosting machine(LightGBM),were utilized to correct ECMWF model forecast products of the daily maximum and minimum temperature with the lead time of 0-72 hours.The corrected temperature products were further compared with that corrected by model output statistics(MOS)method and the forecaster’s subjective forecast products.The results show that:(1)The mean absolute error(MAE)of ECMWF daily maximum temperature forecast is obviously higher than that of minimum temperature,and MAE in mountainous area of Dabies and southern Anhui are large.(2)RF has the best performance in predicting daily maximum temperature and LightGBM in minimum temperature.Compared with the ECMWF model,the prediction accuracy has increased by 18.16%and 5.19%respectively.(3)The machine learning model fusing the surrounding stations information can effectively reduce the temperature forecast errors ofmountain meteorological stations.(4)Compared with the subjective correction,machine learning methods can significantly improve the temperature prediction accuracy of the numerical model in the transition weather,such as high temperature and cold wave.

关 键 词：ECMWF 温度订正 机器学习 高山站点 

分 类 号：P456.7[天文地球—大气科学及气象学]