遥相关极端温度中的空间不对称性建模
Modeling spatial asymmetries in teleconnected extreme temperatures
AI总结：
针对极端事件样本少限制深度学习建模的问题，结合极值理论研究北半球冬季气温多元概率分布的生成模型，提出空间不对称性度量指标，证实深度学习方法在重现温度空间不对称性上优于vine copulas。
摘要：
结合深度学习与极值理论的优势，有助于描述变量间的复杂关系，在这些场景中极端事件会产生重大影响（例如环境或金融应用）。神经网络能在有限参数假设下，从大型数据集中学习复杂的非线性关系。但根据定义，极端事件的发生数量很少，这限制了依赖大量数据的非参数神经网络对稀有事件的描述能力。受近期由大气阻塞引发的北美冬季极端寒冷天气事件启发，我们针对北半球冬季地表气温的整个多元概率分布，研究了多种概率生成模型。我们提出了衡量空间不对称性的指标，例如阻塞事件期间温度场中常见的长程反相关模式。与多元copula建模的统计标准——vine copulas相比，深度学习方法在重现ERA5温度再分析数据空间分布中的复杂不对称性（包括样本内极端事件的空间范围）方面，表现出更优的能力。
英文摘要：
Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
