Generative Adversarial Models for Extreme Geospatial Downscaling

Addressing the challenges of climate change requires accurate and high-resolution mapping of geospatial data, especially climate and weather variables. However, many existing geospatial 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 promise in improving geospatial datasets. This paper describes a conditional GAN-based stochastic geospatial downscaling method that can accommodates very high scaling factors. Compared to most existing methods, the method can generate high-resolution accurate climate datasets from very low-resolution inputs. More importantly, the method explicitly considers the uncertainty inherent to the downscaling process that tends to be ignored in existing methods. Given an input, the method can produce a multitude of plausible high-resolution samples instead of one single deterministic result. These samples allow for an empirical exploration and inferences of model uncertainty and robustness. With a case study of gridded climate datasets (wind velocity and solar irradiance), we demonstrate the performances of the framework in downscaling tasks with large scaling factors (up to $64\times$) and highlight the advantages of the framework with a comprehensive comparison with commonly used and most recent downscaling methods, including area-to-point (ATP) kriging, deep image prior (DIP), enhanced super-resolution generative adversarial networks (ESRGAN), physics-informed resolution-enhancing GAN (PhIRE GAN), and an efficient diffusion model for remote sensing image super-resolution (EDiffSR).