Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales

no code implementations • 17 Feb 2023 • Nicolaas J. Annau, Alex J. Cannon, Adam H. Monahan

Our analyses reveal how GAN configurations influence spatial structures in the generated fields, particularly biases in spatial variability spectra.

Feature Importance Image Super-Resolution

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

no code implementations • 10 Sep 2019 • David John Gagne II, Hannah M. Christensen, Aneesh C. Subramanian, Adam H. Monahan

Some of the GAN configurations perform better than a baseline bespoke parameterization at both timescales, and the networks closely reproduce the spatio-temporal correlations and regimes of the Lorenz '96 system.

BIG-bench Machine Learning Generative Adversarial Network