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。该项目将提供非高斯大气变化的详细评估，以了解和预测大气中极端事件的概率。在非技术术语中，极端事件是一种高影响，难以预测的现象，超出了我们的正常（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率研究的一个重要目标，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，最近发展起来的动力学理论从第一动力学原理出发，预测了大气中的非高斯统计特性。该理论将极端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明确和可检验的预测，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。虽然乘性噪声理论的有效性已经存在令人信服的证据，但到目前为止，还没有系统地进行验证。因此，这项工作的主要重点是在随机理论的指导下，系统地绘制和分析动态相关大气变量的非高斯性（例如，更广泛的影响或这项研究可能延伸到教育和风险管理活动。由于这项研究旨在更好地详细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。研究结果对气候诊断和建模都有意义，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.