Anticipating Extreme Hurricane Winds in the United States Using Bayesian Hierarchical Models

PI将分析和模拟现有的沿海飓风活动记录(1)提供更准确和相关的美国年度至千年时间尺度上极端飓风风的概率估计，以及(2)开发分析和模拟极端气候事件变异性的计算方法。建模和分析工具将源自极值理论和贝叶斯推理方法。数据将来自现代记录、整理的历史记录和地质沉积岩心。沿海飓风是美国严重的社会和经济问题。强风、暴雨和风暴潮造成人员伤亡和财产损失。飓风造成的破坏可与地震相提并论。PI将通过将分析置于贝叶斯框架中，推动极值理论在气候数据分析中的使用。这项研究将为飓风风险评估问题提供尖端科学。为实现赠款目标而采取的步骤包括：(1)为19世纪上半叶的飓风登陆事件分配概率，(2)根据气候指数的值为沿海地区生成年度飓风风速超过分布的样本，(3)将贝叶斯预测概率与来自其他飓风风模型的概率进行比较，(4)检查将空间信息添加到风速模型中的效用，(5)利用国家大气研究中心(NCAR)/国家环境预测中心(NCEP)的再分析数据研究厄尔尼诺-南方涛动(ENSO)和北大西洋涛动(NAO)沿海飓风的联系，以及(6)开发用于校准极端飓风活动的地质记录的软件。首要目标是创建一套可利用贝叶斯模拟技术进行气候研究的分析和建模工具。贝叶斯模拟技术尚未渗透到气候分析和预测领域。这项研究的一个目标是推广这项新技术，并使其能够为气候社区所用。更广泛的影响将包括加强科学，以便在面对气候变化时更好地评估自然灾害的风险。PI将通过在互联网上传播数据、模型和预报来培训研究生并促进公众的气候教育。结果将与私人(再)保险行业和政府资助的应急管理相关。通过推动和促进思维从使用计算机生成分析解决方案转向使用计算机生成解决方案空间的样本(模拟)，预计将对气候科学产生重大影响。结果将挑战并最终改善气候和相关科学中统计学的教学方式。其目标是使贝叶斯模拟成为气候科学中分析和预测的常规组成部分。该项目得到气候变异性和可预测性计划(CLIVAR)的支持。

The PI will analyze and model available records of coastal hurricane activity (1) to provide more accurate and relevant estimates of the probability of extreme hurricane winds in the United States on annual to millennial time scales, and (2) to develop the computational means to analyze and simulate the variability of extreme climate events. The modeling and analysis tools will derive from extreme value theory and methods of Bayesian inference. Data will come from modern records, collated historical accounts, and geological sediment cores. Coastal hurricanes are a serious social and economic concern to the United States. Strong winds, heavy rainfall, and storm surge kill people and destroy property. Destruction from a hurricane rivals that from an earthquake. The PI will advance the use of extreme value theory in analyzing climate data by putting the analysis into a Bayesian framework. The research will provide cutting-edge science to the problem of hurricane risk assessment. Steps to be taken to achieve the grant goals include: (1) Assign probabilities to hurricane landfall events during the first half of the 19th century, (2) Generate samples of annual hurricane wind speed excedence distributions for coastal segments conditioned on values of climate indices, (3) Compare Bayesian predictive probabilities with probabilities from other hurricane wind models, (4) Examine the utility of adding spatial information into the wind speed model, (5) Examine the El-Nino-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) coastal hurricane linkages using the National Center for Atmospheric Research (NCAR)/National Centers for Environmental Prediction (NCEP) reanalysis data, and (6) Develop software for calibrating geological records of extreme hurricane activity. The overarching goal is to create an accessible set of analysis and modeling tools for climate research that makes use of Bayesian simulation technology. Bayesian simulation techniques have yet to infiltrate the climate analysis and prediction communities. An objective of the research will be to expand this new technology and make it accessible to the climate community. The broader impacts will include strengthening the science to better assess the risk of a natural disaster in the face of climate change. The PI will train graduate students and promote climate education of the public by disseminating data, models, and forecasts over the Internet. Results will be relevant to the private (re) insurance industry and to government funded emergency management. By advancing and promoting a shift in thinking away from using the computer to generate analytical solutions to using it to generate samples of the solution space (simulations), a large impact on climate science is anticipated. Results will challenge and ultimately improve the way statistics is taught in the climate and related sciences. The goal is to make Bayesian simulation a routine component of analysis and prediction in climate science. This project is supported under the Climate Variability and Predictability Program (CLIVAR).