Mathematical Sciences: Chaotic Time Series and Environmental Extremes

数学科学：混沌时间序列和极端环境

• 批准号: 9205112
• 负责人: Richard Smith
• 金额: $ 7.25万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-07-15 至 1995-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9205112&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Chaotic; Time; Series

Previous research on threshold methods and extreme values is in the process of being extended from the case of univariate time series, to multivariate or spatial data. This is particularly relevant in climatological research because of the need to combine data from many measuring stations. The climatological objective is to come up with measures of trend among the crossings by a temperature series of a very high or very low threshold. A second objective is to look at alternatives to the standard models for time series. In particular the concept of long-range dependence appears to be important in climatology, and the research will explore methods of measuring this from the low- frequency behavior of the periodogram. An important theme of current climate research is the need for reliable statistical measures of greenhouse-gas-induced climate change. Global warming would cause an increase in extreme temperatures. This research will study the statistical aspects of these extreme values, paying particular attention to the problem of efficiently combining information from a number of measuring stations.

以前的研究阈值方法和极值是 在从单变量时间的情况 系列，多变量或空间数据。 这是特别 与气候研究有关，因为需要 联合收割机从许多测量站获得的数据。 气候学 目标是提出衡量趋势的措施， 一系列温度的交叉点， 阈值 第二个目标是寻找替代方案， 时间序列的标准模型 特别是， 长期依赖性在气候学中似乎很重要， 这项研究将探索从低- 周期图的频率特性。 当前气候研究的一个重要主题是， 温室气体引起的气候变化的可靠统计措施 气候变化 全球变暖会导致 极端的温度 这项研究将研究统计 这些极端值的各个方面，特别注意 有效地组合来自多个 测量站