中山大学学报（自然科学版）（中英文）2024，Vol.63Issue(4)：9-18,10.DOI:10.13471/j.cnki.acta.snus.ZR20230016
基于CFSv2产品和机器学习的东江流域月降水预报
Monthly precipitation forecast in the Dongjiang Basin based on CFSv2 products and machine learning
摘要
Abstract
Mid to long-term precipitation forecasting has always been a hot topic in hydro-meteorological research,with the issue of low accuracy and reliability needing urgent solutions.This study focuses on the Dongjiang Basin and evaluates the prediction accuracy and stability of CFSv2 model products at the monthly scale using the anomaly coefficient of correlation(ACC),normalized root mean square error(NRMSE),mean absolute error(MAE),and the multi-model stability index(MSI).Two methods,namely the CFSv2 model precipitation forecast and the machine learning model forecast combined with CFSv2 model predictors,are employed to predict future precipitation.The results show that under different lead times,the CFSv2 model precipitation forecast exhibits a high correlation with observed precipitation,performing better during the dry season compared to the flood season.However,there is significant variability in precipitation forecasts and poor model stability with changes in the initial time.Combining CFSv2 model predictors with machine learning models improves the forecast stability,reducing the MSI from 0.45 to 0.25 and effectively reducing the randomness in forecasts caused by changes in the initial time.The findings contribute to providing a new approach for mid to long-term precipitation forecasting and offer decision-making support for mid to long-term hydrological forecasting and water resource management.关键词
CFSv2/中长期预报/机器学习/产品评估Key words
CFSv2/mid to long-term forecast/machine learning/product evaluation分类
天文与地球科学引用本文复制引用
庄胜杰,王大刚,林泳恩,林泽群,陈润庭..基于CFSv2产品和机器学习的东江流域月降水预报[J].中山大学学报（自然科学版）（中英文）,2024,63(4):9-18,10.基金项目
国家自然科学基金(52079151,52111540261) （52079151,52111540261）
