Detection of Eye Occurrence in Sequential Satellite Infrared Imagery and Its Application to Improve Deep Learning‐Based Tropical Cyclone Intensity Estimation

Yi Liu, Jing‐Yi Zhuo, Kekuan Chu, Zhe-Min Tan

Journal of Geophysical Research Machine Learning and Computation · 01 February 2026 · Princeton University

In one sentence. DeepTCEye detects tropical cyclone eyes in infrared imagery with about 96% accuracy, and an eye persistence index cuts intensity-estimation RMSE for intense storms by up to 15.65%.

Abstract

Abstract Tropical cyclone (TC) intensity estimation remains a critical yet challenging task, especially for intense storms that are often underestimated by both conventional and deep learning satellite‐based methods. Among various structural features, eye formation is the most distinct transformation during TC development and is closely linked to rapid intensification and peak intensity. Despite its significance, TC eye occurrence information remains underutilized in existing deep learning‐based intensity estimation models. In this study, we improve TC intensity estimation by incorporating TC eye occurrence information as a physical auxiliary input to deep learning models. We first develop a deep learning‐based classifier (DeepTCEye) to detect TC eyes from infrared imagery, achieving classification accuracies of 96.54% for eye scenes and 95.93% for non‐eye scenes. Using the output probabilities from DeepTCEye, we construct eye persistence index (EPI) of varying time windows and use them as auxiliary inputs to the intensity estimation model. Results show that incorporating EPI leads to substantial improvements on TC intensity estimation, especially for intense TCs (intensity >96 kt), reducing the root mean square error by up to 15.65%. Moreover, EPI remains effective when combined with other known physical auxiliary input such as TC fullness, a structural indicator. Our results demonstrate the value of bringing the TC eye occurrence information into focus, showing that temporal evolution of TC eye provides deep learning models with a simple but physically grounded signal that significantly improves intensity estimation, particularly for intense storms.

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