Assessing Atmospheric Extreme Events in a Stochastic Framework

在随机框架中评估大气极端事件

• 批准号: 0903579
• 负责人: Philip Sura
• 金额: $ 36.64万
• 依托单位: Florida 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: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0903579&HistoricalAwards=false
• 关键词: Assessing; Atmospheric; Extreme; Events; Stochastic

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This project will provide a detailed assessment of non-Gaussian atmospheric variability in order to understand and predict the probability of extreme events in the atmosphere. In non-technical terms, an extreme event is a high-impact, hard-to-predict phenomenon that is beyond our normal (Gaussian bell curve) expectations. In technical terms, an extreme event is often defined as the non-normal (non-Gaussian) tail of the probability density function of the data. Understanding extremes has become an important objective in climate variability research, because climate and weather risk assessment depends on knowing the probability of extreme events such as hurricanes and windstorms. Until recently the study of extreme meteorological events has been largely empirical. That is, most investigators used observations or model output to estimate the probabilities of, for example, extreme winds and temperatures, without actually addressing the detailed dynamical/physical reason for the shape of the probability density functions. A recently developed dynamical theory, however, predicts the characteristics of non-Gaussian statistics in the atmosphere from first dynamical principles. This theory attributes extreme atmospheric flow anomalies to stochastically forced linear dynamics, where the strength of the stochastic forcing depends on the flow itself (multiplicative noise). Because stochastic theory makes clear and testable predictions about non-Gaussian variability, the multiplicative noise hypothesis can be verified by analyzing the detailed non-Gaussian statistics of atmospheric variability. While compelling evidence for the validity of the multiplicative noise theory already exists, the validation has, so far, not been done systematically. Therefore, the main focus of this work is to systematically map and analyze, guided by stochastic theory, the non-Gaussianity of dynamically relevant atmospheric variables (e.g., pressure, geopotential height, vorticity, temperature, winds) from observations and from models.Broader impacts or this research potentially extend to educational, and risk-management activities. As the study is aimed at gaining a better, detailed understanding of extreme events in climate, it is anticipated that the multiplicative noise approach has the potential to influence how non-Gaussian atmospheric variability and extreme events are viewed. The results should be of interest for both climate diagnostics and modeling and may have a significant impact on weather and climate risk management, potentially benefiting businesses, consumers and public policy makers.

该奖项由 2009 年美国复苏和再投资法案（公法 111-5）资助。该项目将对非高斯大气变率进行详细评估，以了解和预测大气中极端事件的概率。用非技术术语来说，极端事件是一种影响大、难以预测的现象，超出了我们的正常（高斯钟形曲线）预期。用技术术语来说，极端事件通常被定义为数据概率密度函数的非正态（非高斯）尾部。了解极端事件已成为气候变化研究的重要目标，因为气候和天气风险评估取决于了解飓风和风暴等极端事件的概率。直到最近，对极端气象事件的研究很大程度上还是实证的。也就是说，大多数研究人员使用观测或模型输出来估计极端风和温度等的概率，而没有实际解决概率密度函数形状的详细动力学/物理原因。然而，最近发展的动力学理论从第一动力学原理预测了大气中非高斯统计的特征。该理论将极端的大气流动异常归因于随机强迫线性动力学，其中随机强迫的强度取决于流动本身（乘性噪声）。由于随机理论对非高斯变率做出了清晰且可测试的预测，因此可以通过分析大气变率的详细非高斯统计来验证乘性噪声假设。虽然乘性噪声理论有效性的令人信服的证据已经存在，但迄今为止尚未系统地进行验证。因此，这项工作的主要重点是在随机理论的指导下，系统地绘制和分析来自观测和模型的动态相关大气变量（例如压力、位势高度、涡度、温度、风）的非高斯性。这项研究的更广泛影响可能延伸到教育和风险管理活动。由于该研究的目的是更好、更详细地了解气候中的极端事件，预计乘性噪声方法有可能影响如何看待非高斯大气变率和极端事件。结果应该对气候诊断和建模都有意义，并且可能对天气和气候风险管理产生重大影响，从而可能使企业、消费者和公共政策制定者受益。