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Abstract: Accurate tropical cyclone (TC) intensity estimation is crucial for preventing and mitigating TC-related disasters. Despite recent advances in TC intensity estimation using convolutional neural networks (CNNs), existing techniques fail to adequately incorporate the priori knowledge of TCs. Therefore, information strongly correlated with TC intensity can be obscured by irrelevant data, limiting model performance. To address this challenge, we introduce the Convective-Stratiform Separation Technique, which acts as a physical constraint on the model, to extract pivotal features from the convective core in satellite infrared imagery. Concurrently, we propose a new dual-branch TC intensity estimation model, comprising a "Satellite Imagery Analysis Branch" to extract overall features from satellite imagery and a "Physics-Guided Branch" to analyze the identified convective cores. We further improve the estimation accuracy by incorporating key physical and environmental factors that are often overlooked by the model. We train the model on 1285 TC cases globally during 2003-2016 and evaluate the performance of best-optimized model using an independent test dataset of 95 TC cases globally from 2017. The results show that the root mean square error (RMSE) of TC intensity estimation is 8.13 kt, demonstrating superior performance compared to existing deep learning models.

Key words: tropical cyclone (TC), convolutional neural network; priori knowledge; remote sensing

(ProQuest: ... denotes formulae omited.)

1 INTRODUCTION

Tropical cyclones (TCs) are one of the most devastating weather systems on Earth. Their life cycle is divided into four stages: generation, development, maturity, and dissipation. The morphological characteristics of a TC's eye evolve through these stages. Intensifying TCs typically exhibit a clear, symmetrical eye with spiral structures; mature TCs feature distinct vortex cloud systems, eyewalls, and spiral rainbands (Wang and Wu 2004); weak TCs, by contrast, exhibit irregular eyewall characteristics. TC intensity, defined as the maximum sustained wind speed near the center of a TC, is important for understanding TC development and evolution, improving TC forecasting, and enabling effective disaster prevention and mitigation (Bloemendaal et al. 2021).

Traditional meteorological approaches for TC intensity estimation include the Dvorak technique (Dvorak 1975), the Advanced Dvorak Technique (ADT) (Olander and Velden 2019), the Deviation-Angle Variance (DAV) (Ritchie et al. 2014) , and the Satellite Consensus technique(SATCON) (Herndon et al. 2010). Among them, the widely used Dvorak technique relies on correlating TC rotation, eye shape,...

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