Machine learning analysis uncovers four distinct cyclone exposure regimes in the southwestern Caribbean, highlighting critical storm hazard risks for offshore wind infrastructure.
Offshore wind development in the Colombian Caribbean requires characterization of tropical cyclone exposure to support climate-resilient planning, yet objective regime classifications for this basin remain limited. This study develops an unsupervised Self-Organizing Map (SOM) framework using the NOAA HURDAT2 best-track database for 1970–2023, comprising 668 six-hourly observations from 103 storms and nine physically interpretable features describing position, intensity, kinematics, and seasonality. A 4 × 4 SOM combined with hierarchical Ward clustering identified four cyclone exposure regimes: R0, late-season slow north-eastward disturbances near San Andrés (11.5%); R1, fast westward mature tropical storms across the central Caribbean (62.0%); R2, slow north-westward disturbances off Central America (21.9%); and R3, rare major hurricanes crossing the central Caribbean (4.6%, seven storms; historical mean recurrence interval 7.7 years). R3 exhibited mean maximum sustained winds of 67.8 m/s and a minimum central pressure of 938.5 hPa, with peak winds reaching 79.7 m/s. The results provide a complementary historical hazard characterization layer for offshore wind planning, supporting risk-informed spatial and operational assessment. However, the identified regimes do not represent wind-resource conditions or probabilistic design return periods and should therefore be integrated with site-specific wind, wave, and structural assessments.
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Rosa et al. (2026) studied this question.
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