热带气象学报2024，Vol.40Issue(6)：1074-1084,11.DOI:10.16032/j.issn.1004-4965.2024.094
基于CGAFNet的卫星云图临近预报研究
Satellite Cloud Image Nowcasting Based on CGAFNet
摘要
Abstract
Satellite cloud image extrapolation technology enables timely tracking of the movement and changes of cloud clusters,providing important references for nowcasting and severe weather monitoring.However,existing cloud image prediction methods face challenges such as difficulty in capturing the development of small-scale cloud clusters,unclear details in cloud images,and gradually blurred prediction results,leading to suboptimal forecasting performance.To effectively extract spatiotemporal information from satellite cloud images and forecast the development of mesoscale cloud clusters,this study utilized FY-4A infrared cloud images,focusing on the central and eastern regions of China with Hunan as the center.From the perspective of spatiotemporal sequence prediction,we proposed a convolutional gated recurrent attention fusion network(CGAFNet)and introduced primary and secondary loss(PaSLoss)as the model's loss function.An encoder-decoder structure was constructed to better extract spatiotemporal information from satellite cloud images.To validate the effectiveness of the network framework,we conducted comparative experiments with three typical networks.The results show that CGAFNet achieved a root mean squared error of 10.00 K,a structural similarity index of 0.74,and a peak signal-to-noise ratio of 31.43 in the cloud image extrapolation task.Outperforming other networks across various metrics,the model accurately predicted the evolution of cloud clusters,demonstrating that this method can achieve more accurate prediction accuracy and possesses good generalization ability.关键词
卫星云图/临近预报/时空序列预测/融合网络/注意力机制Key words
satellite cloud image/nowcasting/spatiotemporal prediction/fusion network/attention mechanism分类
天文与地球科学引用本文复制引用
康奇秀,杜东升,陈立福,欧小锋,叶成志..基于CGAFNet的卫星云图临近预报研究[J].热带气象学报,2024,40(6):1074-1084,11.基金项目
国家自然科学基金联合基金项目(U2242201) （U2242201）
湖南省自然科学基金重大项目(2021JC0009)共同资助 （2021JC0009）
