Multi-scale Spin Convolutional Neural Network for Typhoon Intensity Prediction

Typhoons, formidable natural phenomena, typically unleash a trail of destruction, inflicting severe wind damage, floods, and even triggering tsunamis in coastal regions. Therefore, accurate prediction of typhoon intensity carries immense significance, both in theory and practical applications. Convolutional Neural Networks (CNNs) have shown superior capability in modelling spatial and temporal data. However, conventional convolutional operators are very sensitive to rotations, and is not good at modelling hierarchical structures. Since typhoon intensity data are typical spatiotemporal data that possess a distinctive rotational structure and a multi-layered hierarchy, conventional CNN based models fall short in capturing high-order spatial information, thereby limiting the accuracy of predictions. To address the limitations of conventional convolutional operators, we introduce a typhoon intensity prediction model called Multi-scale Spin Convolutional Neural Network (MS-CNN). Our model rotates the convolution kernel approximately to better model the rotating horizontal structure of the typhoons. Additionally, it employs a fine-grained multi-scale architecture to capture the hierarchical structure of typhoons. We conducted a series of comprehensive experiments using typhoon track dataset in the Western North Pacific. The results demonstrate that the superior performance of our MS-CNN model compared to other state-of-the-art models, marking a substantial advancement in typhoon intensity prediction tasks.