Abstract
In this study, the Bayesian Inference and Dynamic Programming Tracking (BIDTrack) algorithm is developed for enhanced global tropical cyclone (TC) tracking in reanalysis datasets, particularly ERA5. BIDTrack addresses challenges like trajectory discontinuities and parameter sensitivity in traditional methods by combining Bayesian inference with dynamic programming. The algorithm is optimized through a Bayesian Interval Optimization (BIO) process, which refines the parameters to retain cyclone candidates that are statistically significant and physically meaningful. Results indicate a strong spatial correlation between BIDTrack-derived trajectory and IBTrACS observations, especially in cyclone-prone regions like the North Atlantic and Western Pacific. BIDTrack captures both major hurricanes and weak storms, providing a reliable tool for cyclone path reconstruction and climate impact assessments. This research demonstrates BIDTrack's potential in improving TC tracking and enhancing the understanding of cyclone dynamics in ERA5.
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