Satellite Cloud Image Nowcasting Based on CGAFNet
KANG Qixiu
DU Dongsheng
CHEN Lifu
OU Xiaofeng
YE Chengzhi
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.
Keywords：satellite cloud imagenowcastingspatiotemporal predictionfusion networkattention mechanism
Publication Date：2024-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 1074-1084 )