Knowledge-Guided Prompt Learning for Tropical Cyclone Intensity Estimation

Wenhui Li, Yue Li, Ying Zhou, Dan Song, Jing Zhang, Zhiqiang Wei, An-An Liu

Abstract: Tropical cyclones (TCs) are one of the most destructive climatic phenomena, making accurate estimation of their intensity crucial for assessing disaster risks. However, due to the complex and indistinguishable structure of their satellite images, existing deep learning methods struggle to differentiate between images of varying intensity, which leads to unsatisfactory accuracy in intensity estimation. In this article, we propose a novel knowledge-guided prompt learning (KGPL) method, KGPL, for TC intensity estimation. KGPL utilizes a pretrained VLM to encode both historical intensity and current satellite image for estimating TC intensity. To minimize domain disparities and facilitate the transfer of a general large model to the TC domain, we introduce a cross-modal interactive prompt learning strategy. Specifically, we embed shared prompts in the text and vision encoders, aiming to learn domain knowledge and promote collaboration and interaction between these two modalities. Furthermore, we design a subregion contrastive learning strategy, which sets constraints on the intensity differences of convective activity in different subregions and guides the model to focus on learning strong convective areas such as the eye and eyewall. Extensive experiments show that our KGPL achieves a significant 41.1% reduction in root mean square error (RMSE) with only one-ninth of the trainable parameters compared to the state-of-the-art method, which validates the effectiveness of our method for TC intensity estimation. The code and data are available at https://github.com/LiYue-TC/KGPL.