基于改进傅里叶神经算子的台风路径与强度预测OA

Typhoons are among the most severe natural disasters impacting the southeastern coastal regions of China,and accurately predicting their tracks and intensities is crucial for effective disaster prevention and mitiga-tion.Traditional numerical forecasting methods,while comprehensive in their physical mechanisms,face chal-lenges such as high computational costs,sensitivity to initial meteorological conditions,and limitations in cap-turing the rapid evolution of typhoons.In recent years,data-driven deep learning methods have introduces a novel paradigm for typhoon prediction.This study presents an end-to-end model based on the Fourier Neural Operator(FNO)for the joint prediction of typhoon tracks and intensities upon landfall in southeastern China.By leveraging the unique capability of FNO to model global dependencies in continuous function spaces,this method effectively captures the non-local spatiotemporal dynamics of meteorological fields,addressing the inher-ent limitations of traditional convolutional and recurrent neural networks in long-range dependency modeling.The experiments utilized the 1949-2024 best-track dataset of tropical cyclones from the China Meteorological Administration(CMA),focusing on typhoon samples that entered the region between 105°E-130°E and 15°N-35°N.Additionally,multi-source reanalysis meteorological fields were integrated as environmental covariates.The results show that the proposed FNO model achieves an average 24-hour track prediction error of 128.6km and an intensity prediction root mean square error(RMSE)of 6.4 m/s,significantly outperforming state-of-the-art deep learning models such as LSTM and Transformer.