Existing ocean front detection methods--including histogram-based variance analysis, Lyapunov exponent, gradient thresholding, and machine learning--suffer from critical limitations: discontinuous outputs, over-detection, reliance on single-threshold decisions, and lack of open-source implementations. To address these challenges, this paper proposes the Bayesian Front Detection and Tracking framework with Metric Space Analysis (BFDT-MSA). The framework introduces three innovations: (1) a Bayesian decision mechanism that integrates gradient priors and field operators to eliminate manual threshold sensitivity; (2) morphological refinement algorithms for merging fragmented fronts, deleting spurious rings, and thinning frontal zones to pixel-level accuracy; and (3) a novel metric space definition for temporal front tracking, enabling systematic analysis of front evolution. Validated on global SST data (2022--2024), BFDT-MSA reduces over-detection by 73% compared to histogram-based methods while achieving superior intensity (0.16∘C/km), continuity, and spatiotemporal coherence. The open-source release bridges a critical gap in reproducible oceanographic research.
@article{arxiv.2502.15250,
title = {An ocean front detection and tracking algorithm},
author = {Yishuo Wang and Feng Zhou and Qicheng Meng and Muping Zhou and Zhijun Hu and Chengqing Zhang and Tianhao Zhao},
journal= {arXiv preprint arXiv:2502.15250},
year = {2025}
}