Abstract: Accurately predicting the trajectory and intensity of tropical cyclones(TCs) is essential for disaster mitigation and prevention.Although deep learning-based advances have demonstrated great prediction performance,a majority of these methods only focus on unimodal data and overlook the rich correlations between different modalities.To fully leverage the rich information contained in multimodal data,a novel framework for tropical cyclone forecasting based on cross-modal fusion and multi-generators is proposed.The framework includes a multimodal feature extraction module,a cross-model feature fusion module and a generative adversarial network(GAN).The multimodal feature extraction module obtains feature representations from the best historical trajectory data,atmospheric reanalysis data and environmental field data respectively.The cross-model feature fusion module fuses multimodal features through a novel cross-modal feature complementation strategy.GAN produces final TC predictions through multiple generators.Additionally,a feature fusion loss is constructed to help boosting the model’s performance.Experiments show that the proposed method can not only maintain high efficiency in training and inference stages,but also achieve better prediction performance.

Key words: Tropical cyclone prediction, Cross-modal fusion, Multiple generators, Attention mechanism