Tropical Cyclone Track Prediction Method Based on DSTFN Model
FANG Wei
DU Juan
QI Meihan
HU Pengyu
Abstract：In the context of global climate change,more and more regions are facing the threat of tropical cyclones.Therefore,accurate prediction of changes in the tracks of tropical cyclones is essential for meteorological warning and disaster reduction.However,existing tropical cyclone prediction methods based on deep learning have limitations in modeling the spatio-temporal correlation of tropical cyclones.In the present study,we proposed a new deep spatio-temporal fusion network(DSTFN)model to improve the prediction accuracy and stability of tropical cyclone tracks.We developed the CaConvNeXt-GRU model,which effectively integrated the ConvNeXt model and the gated recurrent unit,to extract complex nonlinear spatio-temporal features in the 3D time series data of tropical cyclones.Meanwhile,the convolutional block attention module was introduced to automatically focus on the features that were affected more heavily by different isobaric surfaces on tropical cyclones.Moreover,we designed a staged training strategy to realize the effective integration of different modules through pre-training,joint training,and overall training.To evaluate the proposed model,we conducted extensive experiments on the International Best Track Archive for Climate Stewardship(IBTrACS)and the ERA5 dataset.Overall,in predicting tropical cyclone tracks for the next 24 hours,the DSTFN model reduced the average prediction error by about 13.71 km compared to existing tropical cyclone track prediction models based on deep learning.
Keywords：tropical cyclonepath predictionDSTFN modelCaConvNeXt-GRU modelspatio-temporal series prediction
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：14( 882-895 )