Sensitivity of Extreme Hurricane Winds to Climate Change

在地区性极端飓风风场的模拟方面取得了进展。例如，根据可靠的登陆报告得出的极值模型估计，一场类似卡特里娜飓风的风暴在整个美国海岸的重现期为14年。但在特定地点，如纽约市，飓风的重现期是多少？这个问题更难回答，因为影响这个地点的历史风暴非常罕见，强度足够大。在这个项目中，将基于对极值分布参数的缩放行为的新见解，对极端飓风风进行局部建模。这项技术将允许用户根据气候变量(如海洋温度和转向流)来调整风力超过的概率，以便就飓风风险而言，定量评估哪些城市对气候变化最敏感。目标是更好地了解当地飓风风险如何以及在多大程度上受到气候的影响。目标是开发和实施预测美国沿海极端风的技术。需要解决的技术问题包括：(1)为现有数据确定适当的模型；(2)考虑到数据记录中不同程度的不确定性。科学问题是了解局部极端飓风活动对气候的敏感性。将采取系统的数据建模方法，并将模型提供给科学界和风险管理界。这项工作的更广泛影响是更好地了解美国和其他地方的飓风威胁，气候科学中用于数据建模的新工具包，以及对任何地点任何强度的飓风重现期的估计。学生将学习气候理论和现代统计方法在飓风气候研究中的应用。将使用开源软件，并将对学生进行应用计算和统计领域的培训。这项工作将推动和促进扩大量化数据分析的范围，包括数据模型和贝叶斯方法。这些成果将有助于改进将统计学纳入气候及相关科学的研究和教育的方式。

Advances have been made in modeling extreme hurricane winds regionally. For example, the return period of a Katrina-like storm is 14 years along the entire U.S. coast as estimated from an extreme-value model derived from reliable landfall reports. But what are the return periods of hurricane winds at specific locations, like New York City? This question is more difficult to answer, because of the rarity of historical storms with sufficient intensity affecting this location. In this project extreme hurricane winds will be modeled locally based on new insights into the scaling behavior of the parameters of the extreme value distribution. This technology will allow users to condition wind exceedance probabilities on climate variables, such as ocean temperatures and steering currents, in order to quantitatively assess which cities are most sensitive to climate variations, in terms of their risk from hurricanes.The goal is to better understand how and to what extent local hurricane risk is affected by climate. The objectives are to develop and implement the technologies for anticipating extreme winds along the coast of the United States. The technical problems to be solved include (1) determining the proper model for the available data, and (2) accounting for the variable levels of uncertainty in the data records. The scientific problem is to understand how sensitive local extreme hurricane activity is to climate.A systematic approach to data modeling will be taken and the models will be made available to the scientific and risk management communities.The broader impacts of this work are a better understanding of the hurricane threat to the United States and elsewhere, a new tool kit for data modeling in the climate sciences, and estimates of hurricane return periods for any intensity at any location. Students will be educated in the theory of climate and in the application of modern statistical methods for hurricane climate research. Open-source software will be used, and students will be trained in the areas of applied computing and statistics. This work will advance and promote a broadening of quantitative data analysis that encompasses data models and Bayesian methodologies. Results will help to improve the way statistics is integrated into research and education in the climate and related sciences.