arXiv subjects

Mitchell L. Krock

Publications and source records attributed to Mitchell L. Krock.

3 recordsLinked to original sources

arXiv2026

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

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.

stat.ME↗

arXiv2023

Modeling spatial asymmetries in teleconnected extreme temperatures

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.

stat.AP↗

arXiv2022

Teleconnected warm and cold extremes of North American wintertime temperatures

Current models for spatial extremes are concerned with the joint upper (or lower) tail of the distribution at two or more locations. Such models cannot account for teleconnection patterns of two-meter surface air temperature ($T_{2m}$) in North America, where very low temperatures in the contiguous Unites States (CONUS) may coincide with very high temperatures in Alaska in the wintertime. This dependence between warm and cold extremes motivates the need for a model with opposite-tail dependence in spatial extremes. This work develops a statistical modeling framework which has flexible behavior in all four pairings of high and low extremes at pairs of locations. In particular, we use a mixture of rotations of common Archimedean copulas to capture various combinations of four-corner tail dependence. We study teleconnected $T_{2m}$ extremes using ERA5 reanalysis of daily average two-meter temperature during the boreal winter. The estimated mixture model quantifies the strength of opposite-tail dependence between warm temperatures in Alaska and cold temperatures in the midlatitudes of North America, as well as the reverse pattern. These dependence patterns are shown to correspond to blocked and zonal patterns of mid-tropospheric flow. This analysis extends the classical notion of correlation-based teleconnections to considering dependence in higher quantiles.

stat.ME↗