Estimating Tropical Cyclone Intensity Using an STIA Model From Himawari-8 Satellite Images in the Western North Pacific Basin

Analyzing the temporal evolution of historical tropical cyclone (TC) structures is essential for accurate TC intensity estimation. In this article, a novel spatiotemporal interaction attention (STIA) model is proposed to estimate TC intensity using Himawari-8 data in the western North Pacific (WNP) basin. The model incorporates a spatial feature extraction module and a spatiotemporal interaction module, which leverage historical satellite images. Based on a sequence of observed satellite images, the spatial feature extraction module is expected to extract spatial features of each TC frame. After that, the spatiotemporal interaction module comprising the temporal-spatial (TS) module and the spatial-temporal (ST) module is responsible for fusing the temporal and spatial features of each frame. The experimental data are composed of Himawari-8 infrared (IR) and water vapor (WV) images from 2015 to 2020 with a time interval of 1 h. The model is trained on images from 2015 to 2018 and evaluated on images from 2019 to 2020. Ablation experiments are conducted to analyze the impact of the number of frames, and the ST and TS modules. The results demonstrate that using 18-frame inputs yields the best performance, achieving an overall root-mean-square error (RMSE) of 3.61 m/s and a mean absolute error (MAE) of 2.83 m/s. In addition, the ST and TS modules significantly contribute to enhancing the accuracy of TC intensity estimation. The performance of the STIA model already surpasses the state-of-the-art benchmarks, demonstrating its excellence in TC intensity estimation.