Extreme weather poses a large risk to critical energy systems (Ekisheva, Rieder, Norris, Lauby, & Dobson 2021; Levin, Botterud, Mann, Kwon, & Zhou 2022). Uncertainty quantification of negative impacts is important for developing resilience, especially during compound extreme weather events involving multiple climate variables. We leverage BMW-GAM (Economou & Garry 2022), a copula workflow that relies on fitting marginal distributions with Bayesian generalized additive models in moving windows -- an embarrassingly parallel task. The Gaussian copula has separable multivariate space-time correlation, allowing for efficient emulation and likelihood fitting with big datasets. Overall, the formulation is interpretable and provides uncertainty quantification through probabilistic simulations of weather variables during extreme events. Our method is illustrated in an analysis of temperature, wind speed, and global horizontal irradiance from Argonne National Laboratory's high-fidelity climate model output ADDA.

翻译：极端天气对关键能源系统构成重大风险（Ekisheva、Rieder、Norris、Lauby & Dobson 2021；Levin、Botterud、Mann、Kwon & Zhou 2022）。对负面影响的量化不确定性对于提升系统韧性至关重要，尤其是在涉及多气候变量的复合极端天气事件期间。本研究采用BMW-GAM方法（Economou & Garry 2022）——一种基于移动窗口内贝叶斯广义可加模型拟合边缘分布的联结函数工作流，该流程具有天然并行计算特性。高斯联结函数具备可分离的多元时空相关性，能够对大规模数据集进行高效仿真和似然拟合。整体框架具有良好的可解释性，并通过极端事件期间气象变量的概率模拟提供不确定性量化。我们使用阿贡国家实验室高保真气候模型输出ADDA中的温度、风速和全球水平辐照度数据，对本方法进行了实证分析。