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

Mitchell L. Krock, W. Neal Mann, Zhi Zhou

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.

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

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.
Paper Structure (6 sections, 6 equations, 7 figures)

This paper contains 6 sections, 6 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Bayesian Moving Window Generalized Additive Model (BMW-GAM)
  3. Marginal Distribution
  4. Copula
  5. ADDA Analysis
  6. Discussion and Conclusions

Figures (7)

  • Figure 1: First row: historical data. Second row: posterior mean of BMW-GAM simulations. Third row: posterior variance of BMW-GAM simulations.
  • Figure 2: Same configuration as Figure \ref{['fig:date1plot']}, but during a different date.
  • Figure 3: Comparison of autocorrelation and partial autocorrelation functions for historical and predicted temperature.
  • Figure 4: Same configuration as Figure \ref{['fig:acfpacftemp']}, but for wind speed.
  • Figure 5: Same configuration as Figure \ref{['fig:acfpacfwind']}, but for global horizontal irradiance.
  • ...and 2 more figures