海洋预报2025，Vol.42Issue(5)：1-10,10.DOI:10.11737/j.issn.1003-0239.2025.05.001
引入预报时效的中国近海海面风速预报深度学习订正方法
A study on the deep learning correction method incorporating forecast lead time for sea surface wind speed forecasts along the nearshore areas of China
摘要
Abstract
This study evaluates the performances of the Global Forecast System(GFS)of the National Centers for Environmental Prediction and the Integrated Forecasting System(IFS)of the European Centre for Medium-Range Weather Forecasts in forecasting sea surface wind speed along the nearshore areas of China.Additionally,a deep neural network(DNN)-based correction method is applied to improve the GFS wind speed forecasts along the nearshore areas of China.Results show that,prior to correction,the root mean square error(RMSE)of the 120-hour sea surface wind speed forecasts is 3.3 m/s for the GFS model and 2.5 m/s for the IFS model,with the IFS model demonstrating better performance.The DNN model effectively reduces the negative systematic bias of the GFS forecasts,and the multi-variable correction approach achieves significant improvements.To mitigate error accumulation caused by increasing forecast lead time,this study proposes a correction method incorporating forecast lead time as an input variable.Compared to other correction models,the lead-time-aware correction model further reduces forecast errors.Compared to the original GFS model,this approach reduces systematic bias by 1.3 m/s,RMSE by 1.1 m/s,relative error by 7%,and scatter index by 3%.In the highest wind speed range,the lead-time-aware correction model reduces the negative bias of the 96-hour and 120-hour forecasts by 1.0 m/s and 0.7 m/s,respectively,outperforming the IFS model.关键词
海面风/风速/预报时效/深度学习/数值模式/订正Key words
Sea surface wind/wind speed/forecast lead time/deep learning/numerical weather prediction/correction分类
海洋学引用本文复制引用
周陈羽,张弛,王喜冬,魏立新,刘晓燕..引入预报时效的中国近海海面风速预报深度学习订正方法[J].海洋预报,2025,42(5):1-10,10.基金项目
2023年广西北部湾海洋环境变化与灾害研究重点实验室开放课题(2023KF04). （2023KF04）
