大气科学学报2025，Vol.48Issue(3)：417-428,12.DOI:10.13878/j.cnki.dqkxxb.20240828002
基于全连接神经网络与分位数匹配相结合的阵风预报
Offshore gust forecasting based on combined fully connected neural net-work and quantile matching approach
摘要
Abstract
Gusts significantly impact the safety of maritime shipping and offshore operations.However,due to the complex mechanisms driving wind speed variability,accurate gust prediction remains a longstanding challenge.To improve the forecasting accuracy of offshore gusts,this study utilized hourly maximum wind speed observations from the China Meteorological Administration(January 2021 to December 2022)along with 24-hour forecasts from the European Centre for Medium-Range Weather Forecasts(ECMWF)deterministic model.Based on data from 15 buoy stations located in China's coastal waters,three distinct gust forecasting methods were developed:1)a model based on a fully connected neural network(FCNN),2)a model applying quantile matching to the 10-m wind speed forecasts from the numerical model,and 3)a hybrid model combining FCNN prediction with subsequent quantile matching correction(FCNN+QM). These methods were compared and validated using independent data from January to December 2023,leading to the following conclusions:When used alone,the FCNN method tends to significantly underestimate strong gust events.To address this,the study explored transforming the prediction target from gust wind speed to the adjust-ment value between gusts and the 10-m wind speed(or wind speeds at other pressure levels)from the numerical model.Although this transformation improved the distribution of the dependent variable,making it closer to normal and alleviating sample imbalance issues,it also introduced additional errors from the numerical model into the dependent variable.Comparative experiments demonstrated that modifying the prediction target alone could not fully compensate for the FCNN's limitations in capturing extreme wind speeds.Applying a secondary correction through quantile matching(FCNN+QM)substantially improved the model's performance for strong gusts while maintaining stable accuracy for weaker winds.Moreover,direct quantile matching of the 10-m wind speed forecasts from ECMWF produced prediction skill comparable to,or even exceeding,that of the FCNN+QM meth-od for strong gusts.This finding suggests that,in the context of offshore gust prediction,the FCNN step is not strictly necessary when using quantile matching.Nevertheless,incorporating FCNN as a preliminary step enhances the robustness of strong gust prediction overall.Validation results across 2023 confirmed that the FCNN+QM method achieved the best performance for strong gust forecasts. The combined FCNN and quantile matching approach,trained uniformly across 15 coastal buoy stations,demonstrated strong applicability for offshore gust prediction across diverse sea areas.However,slight overestima-tion of strong gusts was observed at a few individual stations.Given the scarcity of offshore observations and the complexity of influencing meteorological systems,this generalizable model provides a valuable reference for poorly observed regions,as demonstrated using during Typhoon Doksuri's strong wind event.While the FCNN ap-proach enables more detailed quantification of the influence of different meteorological variables on gusts,its pre-dictive capability remains limited by the accuracy of the underlying numerical model and the variability of off-shore weather systems.To further enhance offshore gust forecasting,future work should explore segmented ma-chine learning modeling tailored to specific conditions,combined with quantile matching correction,to achieve more targeted and accurate predictions.关键词
阵风预报/全连接神经网络/分位数匹配/强阵风/订正方法/机器学习Key words
gust forecasting/fully connected neural network/quantile matching/strong gust/correction method/machine learning引用本文复制引用
胡海川,曹勇..基于全连接神经网络与分位数匹配相结合的阵风预报[J].大气科学学报,2025,48(3):417-428,12.基金项目
国家重点研发计划项目(2022YFC3004200) （2022YFC3004200）
中国气象局重点创新团队智能预报技术团队项目(CMA2022ZD04) （CMA2022ZD04）
