Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary Information

Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail, which finds wide applications in climate modeling, numerical weather forecasting, and renewable energy. Although deep-learning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales, the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice, and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale uncertainty. This article presents DualDS, a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate downscaling. Such a learning method is unified in a two-stream framework through up- and downsamplers, where the downsampler is used to simulate the information loss process during the upscaling, and the upsampler is used to reconstruct lost details and correct errors incurred during the upscaling. This dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping process. Experimental findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches, both qualitatively and quantitatively. Specifically, for a single surface-temperature data downscaling task, our method is comparable with other unsupervised algorithms with the same dataset, and we can achieve a 0.469 dB higher peak signal-to-noise ratio, 0.017 higher structural similarity, 0.08 lower RMSE, and the best correlation coefficient. In summary, this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.