Graph-theoretical investigation of trajectory dynamics and size characteristics in tropical cyclones

Yixiang Wang, Jiayao Wang (), Yu Chang, Kang Cai (), Sunwei Li and You Dong
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Yixiang Wang: The Hong Kong University of Science and Technology
Jiayao Wang: The Hong Kong Polytechnic University
Yu Chang: Tsinghua Shenzhen International Graduate School
Kang Cai: The Hong Kong Polytechnic University
Sunwei Li: Tsinghua Shenzhen International Graduate School
You Dong: The Hong Kong Polytechnic University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 10, No 26, 11957-11974

Abstract: Abstract The intensification of climate changes has led to increased tropical cyclone (TC) intensities and subsequent damage, emphasizing the critical need for accurate trajectory prediction to mitigate their impact. In this study, a graph-theory-based approach was employed for the identification of TC trajectory. Using reanalysis data, each targeted TC can be constructed as a graph during its TC lifetime. Four graph metrics are computed from each graph constructed using different data sources, including mean sea level pressure, wind speed, and total precipitation. Among the graphs constructed, those representing mean sea level pressure (MSLP) and wind speed at 10 m (WD10) graphs show superior advantages in identifying TC trajectory. Furthermore, the metric PageRank of MSLP graph even reveals a notable ability to estimate TC size. Comparisons with a similar graph-theoretical approach demonstrate that our method exhibits superior performance in capturing complex TC dynamics. We anticipate to integrating the graph-theory-based approach into machine learning models to enhance the accuracy of predicting TC trajectories and intensities in future studies.

Keywords: Tropical cyclone; Machine learning; Forecast model; Trajectory (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07268-2

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