Uncertainty quantification for critical energy systems during compound extremes via BMW-GAM

TL;DR

This paper introduces BMW-GAM, a copula-based method utilizing Bayesian generalized additive models for efficient uncertainty quantification of climate variables during extreme weather events affecting critical energy systems.

Contribution

It presents a novel, interpretable framework combining copula models with Bayesian GAMs for scalable, probabilistic analysis of compound climate extremes.

Findings

Efficient emulation of multivariate climate data during extremes.

Uncertainty quantification through probabilistic simulations.

Application to high-fidelity climate model outputs.

Abstract

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.