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.

该项目将开发方法，以在观测和气候模型模拟中估算极端天气和气候事件的可能性。 极端情况表示为仅在给定时间段内发生一次的事件的大小，例如100年洪水的规模，对于计划基础设施项目（如大坝和堤坝）的规划至关重要。 但是，从仪器记录可用的相对较短的时间段来看，极端事件的返回时间很难估算。 由于气候变化而导致的数据的长期趋势，给定返回时间的预期极端值的计算更加复杂，这与传统统计数据的平稳性假设相矛盾。 除了这些时间问题外，极端事件研究的长期数据仅在特定位置可用，而在大型介入区域中则需要对极值可能性的估计。在该项目下进行的工作将开发统计技术，以克服非机构性在时间和太空中稀疏所带来的困难。 将引入三个新框架来表征极端：（1）用于观察结果的非参数多元空间差异式式混合模型，（2）贝叶斯非参数功能数据分析以估计多变量空间极端的贝叶斯非参数功能数据分析，以及（3）与Marginals的混合物（3）具有跨性值（GEEV）的混合物（GEEV）的分布（GEEV），具有普遍性的极端价值（GEEV）。即使考虑到空间变化的参数后，空间相关。 这项研究将从最近的观察和气候模型模拟（1970-2000）中产生温度极端值的空间图。这项研究将吸引包括统计学家，气候学家和资源经理在内的大型受众。 工作的一种动机是确定极端事件的频率如何在不断变化的气候下发生变化的问题。 气候变化通常以长期平均在大区域平均的变化表示，但是与极端变化相关的不利影响（例如，热浪发生的增加，都可能构成比平均值变化更大的挑战。 此外，通常会根据过去的极端发生进行极端事件的规划，但是需要在此处开发的新技术来预测气候不断变化的极端可能性。