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<p>We present a Bayesian spatio-temporal modeling framework to assess hurricane-driven wind-related risks along the U.S. South Atlantic and Gulf coasts, explicitly accounting for spatially and temporally varying risk dynamics in a warming climate. Our analysis leverages a high-resolution, downscaled wind speed dataset generated from a large ensemble of synthetic tropical cyclones using the Coupled Hurricane Intensity Prediction System (CHIPS) model. To estimate the marginal probability that a tropical storm reaches hurricane strength at each location and time, we apply binomial generalized linear models (GLMs) fitted using the Integrated Nested Laplace Approximation (INLA), which provides substantial computational gains compared to traditional Markov Chain Monte Carlo (MCMC) methods. Our modeling framework incorporates key environmental and spatial covariates, including the El Niño-Southern Oscillation (ENSO), Sea Surface Temperature (SST), wind shear, and a land/water indicator, to capture the physical drivers of hurricane intensity. A copula-based dependence approach is then applied to derive joint spatial exceedance probabilities across the domain, conditioned on the estimated marginal probabilities. By integrating both marginal behavior and joint spatial dependence, the framework effectively captures the space-time variability in wind intensity distributions, enabling more robust and spatially coherent assessments of hurricane-induced wind hazards in a changing climate.</p>

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Bayesian spatio-temporal models for hurricane risk assessment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • M. C. Ausin,
  • A. Sarhadi,
  • M. P. Wiper

摘要

We present a Bayesian spatio-temporal modeling framework to assess hurricane-driven wind-related risks along the U.S. South Atlantic and Gulf coasts, explicitly accounting for spatially and temporally varying risk dynamics in a warming climate. Our analysis leverages a high-resolution, downscaled wind speed dataset generated from a large ensemble of synthetic tropical cyclones using the Coupled Hurricane Intensity Prediction System (CHIPS) model. To estimate the marginal probability that a tropical storm reaches hurricane strength at each location and time, we apply binomial generalized linear models (GLMs) fitted using the Integrated Nested Laplace Approximation (INLA), which provides substantial computational gains compared to traditional Markov Chain Monte Carlo (MCMC) methods. Our modeling framework incorporates key environmental and spatial covariates, including the El Niño-Southern Oscillation (ENSO), Sea Surface Temperature (SST), wind shear, and a land/water indicator, to capture the physical drivers of hurricane intensity. A copula-based dependence approach is then applied to derive joint spatial exceedance probabilities across the domain, conditioned on the estimated marginal probabilities. By integrating both marginal behavior and joint spatial dependence, the framework effectively captures the space-time variability in wind intensity distributions, enabling more robust and spatially coherent assessments of hurricane-induced wind hazards in a changing climate.