CMG: Multivariate Nonstationary Spatial Extremes in Climate and Atmospherics

CMG：气候和大气中的多元非平稳空间极值

• 批准号: 0934595
• 负责人: Montserrat Fuentes
• 金额: $ 32.5万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0934595&HistoricalAwards=false
• 关键词: CMG; Multivariate; Nonstationary; Spatial; Extremes

This project will develop methods to estimate the likelihood of extreme weather and climate events, both in observations and in climate model simulations. Extremes, expressed as the magnitude of an event that is only expected to occur once in a given time period, such as the size of the 100-year flood, are central to planning for infrastructural projects like dams and levees. But return times for extreme events are difficult to estimate from the relatively short time period available from the instrumented record. The calculation of expected extreme values for a given return time is further complicated by long-term trends in the data due to climate change, which contradict the stationarity assumption on which traditional statistics is predicated. Beyond these temporal issues, long-term data for extreme event studies is only available at specific locations, while estimates of extreme value likelihood are desired over the large intervening regions. The work conducted under this project will develop statistical techniques to overcome the difficulties presented by nonstationarity in time and sparseness in space. Three new frameworks will be introduced to characterize extremes: (1) Nonparametric multivariate spatial Dirichlet-type mixture models for the observations, (2) Bayesian nonparametric functional data analysis to estimate multivariate spatial extremes, and (3) Mixture models, with marginals that have generalized extreme value (GEV) distributions with spatially varying parameters and the observations are spatially-correlated even after accounting for the spatially varying parameters. The research will produce spatial maps of extreme values for temperature, both from observations and climate model simulations of the recent past (1970-2000).The research will be of interest to a large audience including statisticians, climatologists, and resource managers. One motivation for the work is the problem of determining how the frequency of extreme events will change in a changing climate. Climate change is usually expressed in terms of changes in long-term means averaged over large regions, but the adverse impacts associated with changes in extremes, such as increases in the occurrence of heat waves, can pose greater challenges than changes in means. In addition, planning for extreme events is usually conducted based on past occurrences of extremes, but new techniques such as the ones developed here will be required to anticipate the likelihood of extremes in a changing climate.

该项目将开发在观测和气候模型模拟中估计极端天气和气候事件可能性的方法。 极端情况以预计在给定时间段内只会发生一次的事件的强度来表示，例如 100 年一遇的洪水的规模，它是水坝和堤坝等基础设施项目规划的核心。 但是，根据仪器记录中相对较短的时间段很难估计极端事件的返回时间。 由于气候变化导致的数据长期趋势，给定返回时间的预期极值的计算变得更加复杂，这与传统统计所依据的平稳性假设相矛盾。 除了这些时间问题之外，极端事件研究的长期数据仅在特定位置可用，而需要对大的干预区域进行极值可能性的估计。该项目进行的工作将开发统计技术，以克服时间非平稳性和空间稀疏性带来的困难。 将引入三个新框架来表征极值：（1）用于观测的非参数多元空间狄利克雷型混合模型，（2）贝叶斯非参数函数数据分析以估计多元空间极值，以及（3）混合模型，其边际具有具有空间变化参数的广义极值 (GEV) 分布，即使考虑了空间变化参数，观测结果也具有空间相关性。 该研究将根据最近（1970-2000 年）的观测和气候模型模拟生成温度极值的空间图。这项研究将引起包括统计学家、气候学家和资源管理者在内的大量受众的兴趣。 这项工作的动机之一是解决气候变化中极端事件发生频率如何变化的问题。 气候变化通常用大区域平均长期平均值的变化来表示，但与极端变化相关的不利影响，例如热浪发生的增加，可能比平均值的变化带来更大的挑战。 此外，极端事件的规划通常是根据过去发生的极端事件进行的，但需要像这里开发的新技术来预测气候变化中发生极端事件的可能性。