TCIP-Net: Quantifying Radial Structure Evolution for Tropical Cyclone Intensity Prediction

Tropical cyclones (TCs) are among the most deadly and damaging natural disasters in coastal areas worldwide. Traditional forecasting methods face challenges as they neglect crucial spatial information related to intensity changes and require substantial human and material resources. Moreover, current deep learning approaches often rely on reanalysis of data from observations far from land, making them challenging to acquire and operationalize. In response to these issues, the article introduces the TC intensity prediction network (TCIP-Net), which, while maintaining interpretability, successfully extracts rich convective structural information from the infrared (IR) channel of satellite imagery. We present the spatio-temporal evolution trajectory of TC radial structural information through Hovm & ouml;ller diagrams. In addition, we construct a subnetwork with one backbone convolution and four branch convolution operations to extract asymmetric information of TC structure. The convective core (CC) reveals the distribution of convective systems around the eye, aiding in targeted attention to convective information in IR imagery. The model aims to quantitatively explain the contributions of satellite imagery (IR and microwave), convective structure, and key physical factors to the TC intensity prediction task. We utilize multiple TC cases to assess and validate the applicability and effectiveness of the model. The results indicate that TCIP-Net achieved good performance. This approach provides practical guidance for predicting TC intensity using advanced artificial intelligence-based methods and is expected to complement operational models.