Generating high-resolution climatological precipitation data using SinGAN

Abstract

High-resolution (HR) climate data are indispensable for studying regional climate trends, disaster prediction, and urban development planning in the face of climate change. However, state-of-the-art long-term global climate simulations do not provide appropriate HR climate data. Deep learning models are often used to obtain high-resolution climate data. However, due to the fact that these models require sufficient low-resolution (LR) and HR data pairs for the training process, they cannot be applied to scenario with inadequate training data. In this paper, we explore the applicability of a single image generative adversarial network (SinGAN) in generating HR climate data. SinGAN relies on single LR input data to obtain the corresponding HR data. To improve the performance for extreme-value regions, we propose a SinGAN combined with the weighted patchGAN discriminator (WSinGAN). The proposed WSinGAN outperforms comparable models in generating HR precipitation data, and its results are close to real HR data with sharp gradients and more refined small-scale features. We also test the scalability of the pre-trained WSinGAN for unseen samples and show that although only a single LR sample is used to train WSinGAN, it can still produce reliable HR data for unseen data.