Assessing Atmospheric Extreme Events in a Stochastic Framework

该奖项是根据2009年美国复苏和再投资法案(公法111-5)资助的。该项目将提供对非高斯大气变率的详细评估，以了解和预测大气中发生极端事件的可能性。在非技术术语中，极端事件是一种高度影响、难以预测的现象，超出了我们正常的(高斯钟形曲线)预期。在技术术语中，极端事件通常被定义为数据的概率密度函数的非正态(非高斯)尾部。了解极端情况已成为气候变异性研究的重要目标，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主要是经验性的。也就是说，大多数研究人员使用观测或模型输出来估计例如极端风和温度的概率，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，一个最近发展起来的动力学理论根据第一动力学原理预测了大气中非高斯统计的特征。这一理论将极端的大气流动异常归因于随机强迫的线性动力学，其中随机强迫的强度取决于气流本身(乘性噪声)。由于随机理论对非高斯变率做出了清晰且可检验的预测，因此可以通过分析大气变率的详细非高斯性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，但到目前为止，验证还没有系统地进行。因此，这项工作的主要重点是在随机理论的指导下，从观测和模型中系统地绘制和分析动态相关的大气变量(如气压、位势高度、涡度、温度、风)的非高斯性。由于这项研究的目的是更好、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对非高斯大气变率和极端事件的看法。这一结果应该对气候诊断和建模都有意义，并可能对天气和气候风险管理产生重大影响，可能使企业、消费者和公共政策制定者受益。

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.