Fusion of Satellite Imagery and Convection Features for Tropical Cyclone Intensity Short‐Term Prediction

Abstract Tropical cyclones (TCs) are among the most impactful extreme disasters affecting humanity, and TC forecasting has become a crucial research area. Addressing the current issues of low utilization of infrared imagery information and insufficient extraction of domain knowledge, we employ objective techniques to extract convective features related to cloud organization from infrared imagery. These features, along with satellite imagery and historical intensity values, are selected as model inputs. This paper introduces a deep learning model designed for the short‐term prediction of TC intensity in the Northwest Pacific by fusing satellite imagery and convective features (TCISP‐fusion). We developed a spatiotemporal feature extraction module to capture high‐level features from the spatio‐temporal sequences of satellite imagery and convective features. Additionally, we introduced a spatiotemporal feature fusion module to integrate asymmetrically distributed convective features while minimizing information loss during feature extraction. Furthermore, we applied the Laplacian Pyramid Image Fusion algorithm to effectively combine observations from the infrared (IR) and water vapor (WV) channels. This method captures large‐scale cloud system structures and retains small‐scale detailed features, generating high‐contrast fused imagery and reducing the complexity of input data. The TCISP‐fusion model achieves a root mean square error of 10.87 kt for 24‐hr intensity prediction of western North Pacific TCs. Compared to traditional and mainstream methods, our model achieves comparable accuracy while significantly reducing the required human and material resources. The data used ensure real‐time applicability, making it highly valuable for operational applications.