沙漠与绿洲气象2024，Vol.18Issue(3)：96-104,9.DOI:10.12057/j.issn.1002-0799.2024.03.013
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
摘要
Abstract
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Key words
ECWMF/temperature correction/machine learning/mountain meteorological stations分类
天文与地球科学引用本文复制引用
刘杰,刘高平,安晶晶,邱学兴,章颖..基于机器学习的模式温度预报订正方法[J].沙漠与绿洲气象,2024,18(3):96-104,9.基金项目
中国气象局创新发展专项(CXFZ2022J001) （CXFZ2022J001）
安徽省气象局创新发展专项(CXM202201) （CXM202201）
