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Nowcasting of typhoon tracks based on LLM and RAG

Published online by Cambridge University Press:  19 September 2025

Baichuan Peng ,
Youchao Jiang  and
Li Yao Open the ORCID record for Li Yao [Opens in a new window]
Baichuan Peng
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China
Youchao Jiang
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China
Li Yao*
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China
*
Corresponding author: Li Yao; Email: yaoli@shmtu.edu.cn
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Abstract

Accurate typhoon track nowcasting is vital for navigation and coastal disaster prevention. This research integrates a Large Language Model (LLM) with Retrieval-Augmented Generation (RAG) technology for typhoon path prediction. Leveraging LLMs as the predictive foundation, the approach tailors forecasts to individual typhoon characteristics. The methodology involves collecting satellite imagery, standardizing data, and employing optical flow methods to track typhoons and derive path coordinates. These coordinates are preprocessed and embedded into the LLM. RAG enhances the LLM’s predictive performance, enabling effective forecasting. Increasing typhoon-specific embedded data further improves accuracy. Using the FY-4 dataset, the method achieved an average absolute error of 10.78 km in 12-hour predictions. The study demonstrates that LLM-RAG integration excels in nowcasting.

Keywords

LLMnowcasting of typhoon tracksRAG

Information

Type
Research Article
Information
The Journal of Navigation , Volume 78 , Issue 2-3 , May 2025 , pp. 289 - 304
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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