Enhanced Typhoon Center Localization Using Geostationary Satellite Imagery

Abstract

An accurate center localization in near real-time is critical for tropical cyclone (TC) monitoring and forecasting. This study presents a robust algorithm for localizing typhoon centers using the Chinese geostationary (GEO) meteorological satellite. The results using the Advanced Geostationary Radiation Imager (AGRI) onboard Fengyun-4A (FY-4A) satellite data, achieving a mean absolute error (MAE) of 29.4 km across various typhoon intensities in the Western North Pacific, superior to other baseline methods. By harnessing the multi-spectral imagery from the FY-4A and incorporating an attention mechanism, it significantly boosts the deep learning convolutional neural network's ability to identify typhoon cloud features and their centers, even during their initial and weakest stages, which is laudable because these are the most difficult for center fixing even for human analysts. Remarkably, it requires just a single moment satellite imagery to locate the center of typhoon, enabling automated updates of the typhoon centers in near real-time applications.