FHDTIE: Fine-Grained Heterogeneous Data Fusion for Tropical Cyclone Intensity Estimation

A tropical cyclone is a highly destructive extreme weather phenomenon. Estimating the intensity of a tropical cyclone can help provide early warnings, guiding specific disaster defense measures. However, two main challenges hinder performance improvement. The first challenge is how to combine heterogeneous tropical cyclone data into a latent space so that the model can leverage the cloud structure of satellite imagery and the comprehensive meteorological information from reanalysis or forecast data for intensity estimation. The second challenge lies in detecting multiple pseudo-fine-grained areas for the final estimation since tropical cyclones are highly diverse extreme weather phenomena. Neglecting any pseudo-fine-grained areas or relying solely on a single one can potentially result in subpar estimation performance. To address the challenges mentioned above, a fine-grained heterogeneous data fusion framework named FHDTIE is proposed. Two key components in this framework can address the aforementioned challenges. One component is the HDF, which offers shape matching and channel fusing strategies for heterogeneous data fusion. The other component is called the fine-grained cluster features integrator (FCFI). It utilizes a clustering method to identify multiple pseudo-fine-grained areas. Within these areas, the U-Net is used to automatically learn pseudo-fine-grained area representations, and then the graph neural network handles information interaction across these representations. Extensive experiments were conducted to demonstrate the robustness and superiority of the proposed fine-grained heterogeneous data fusion framework. The code is available at GitHub: https://github.com/xuguangning1218/FHDTIE.