210 citations · 475 across the 10 of their papers we have counts for
11 papers
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales
Nicolaas J. Annau, Alex J. Cannon, Adam H. Monahan
This paper explores the application of emerging machine learning methods from image super-resolution (SR) to the task of statistical downscaling. We specifically focus on convoluti…
Teleconnected warm and cold extremes of North American wintertime temperatures
Mitchell L. Krock, Adam H. Monahan, Michael L. Stein
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 p…
Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails
Mitchell Krock, Julie Bessac, Michael L. Stein +1
In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the distribution are used for inference. Extreme observations are specified as values…
Estimating Concurrent Climate Extremes: A Conditional Approach
Whitney K. Huang, Adam H. Monahan, Francis W. Zwiers
Simultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in underst…
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
David John Gagne, Hannah M. Christensen, Aneesh C. Subramanian +1
Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible sub-grid forcings. Some ex…
Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data
Fei Lu, Nils Weitzel, Adam H. Monahan
While nonlinear stochastic partial differential equations arise naturally in spatiotemporal modeling, inference for such systems often faces two major challenges: sparse noisy data…