Bayesian spatio-temporal models for hurricane risk assessment
ハリケーンリスク評価のためのベイズ時空間モデル (AI 翻訳)
M. C. Ausín, A. Sarhadi, M. Wiper
🤖 gxceed AI 要約
日本語
米国大西洋岸・メキシコ湾岸のハリケーンリスク評価のため、ベイズ時空間モデルを提案。高解像度合成台風データとINLAを用いた統計モデリングで、温暖化に伴う風速リスクの空間・時間変動を評価する。
English
This paper presents a Bayesian spatio-temporal modeling framework for hurricane risk assessment along the U.S. coasts. Using high-resolution synthetic cyclone data and INLA, it captures the spatiotemporal variability of wind hazards and joint exceedance probabilities under climate change.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本は台風リスク評価において類似の統計的枠組みを応用可能だが、本論文は大西洋バシンに特化しており、日本のGX文脈では直接の関連性は限定的。ただし、気候変動適応策の基礎として参考になる。
In the global GX context
This paper is directly relevant to global climate risk modeling, particularly for hurricane-prone regions. The Bayesian framework and copula-based dependence approach can be adapted for other basins, supporting climate adaptation and resilience planning in corporate and policy contexts.
👥 読者別の含意
🔬研究者:Provides a computationally efficient Bayesian framework for extreme wind risk assessment that can be extended to other hazards.
🏢実務担当者:The hazard maps and exceedance probabilities can inform insurance and infrastructure risk management.
🏛政策担当者:Offers a method to assess spatially coherent risks under climate change, useful for disaster preparedness and adaptation policy.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://link.springer.com/content/pdf/10.1007/s00477-026-03312-0.pdffirst seen 2026-07-13 07:26:20
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