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.