Monthly precipitation forecast in the Dongjiang Basin based on CFSv2 products and machine learning
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.
CFSv2mid to long-term forecastmachine learningproduct evaluation
国家科技期刊平台
NSTL
万方数据
