Physics-Informed Learning for Tropical Cyclone Intensity Prediction

Accurate prediction of tropical cyclone (TC) intensity is important to disaster prevention. However, many deep learning (DL) models analyzing remote sensing images for TC intensity prediction lack explainability or interpretability due to insufficient consideration of physical knowledge. Therefore, we propose physics-informed cyclone intensity prediction networks (Pici-Nets) to forecast TC intensity measured by the maximum sustained wind (MSW) speed. There are three types of physical information closely related to MSW being used, the structural knowledge of cyclones reflected by multitemporal multispectral images (MSIs), the motional prior representing the dynamics of TC, and the environmental factors affecting the evolvement of TC provided by the public track data. As the multimodal information are fused by Pici-Nets, the MSW speed prediction process which imitates the manual forecast by considering multiple factors to reduce the prediction errors can be regarded as interpretable. Specifically, the first type of information guides Pici-Nets to sharpen the low-spatial-resolution MSIs, enhance temporal structural features, and produce explainable feature maps. Since the second or the third type of data are sometimes missing, we derive Pici-Net+ and Pici-Net++ from the vanilla Pici-Net to cope with the situations. In Pici-Net++, we even incorporate a physics simulation model (PSM) to simulate the missing data. This way, the models become applicable to incomplete datasets and more interpretable in MSW speed prediction. Experiments on different TC datasets validate the efficacy of Pici-Nets, as the results show that Pici-Nets outperform many classic and state-of-the-art (SOTA) models in TC intensity prediction.