Generative Adversarial Models for Extreme Downscaling of Climate Datasets

arXiv:2402.14049v1 Announce Type: new
Abstract: Addressing the challenges of climate change requires accurate and high-resolution mapping of climate and weather variables. However, many existing climate datasets, such as the gridded outputs of the state-of-the-art numerical climate models (e.g., general circulation models), are only available at very coarse spatial resolutions due to the model complexity and extremely high computational demand. Deep-learning-based methods, particularly generative adversarial networks (GANs) and their variants, have proved effective for refining natural images, and have shown great …

abstract adversarial art arxiv challenges change climate climate change climate models cs.ai cs.lg datasets general generative mapping numerical physics.ao-ph spatial state type variables weather