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Adversarial super-resolution of climatological wind and solar data

Significance Global climate simulations are typically unable to resolve wind and solar data at a resolution sufficient for renewable energy resource assessment in different climate scenarios. We introduce an adversarial deep learning approach to super resolve wind and solar outputs from global climate models by up to 50×. The inferred high-resolution fields are robust, physically consistent with the properties of atmospheric turbulence and solar irradiation, and can be adapted to domains from regional to global scales. This resolution enhancement enables critical localized assessments of the potential long-term economic viability of renewable energy resources. Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50× resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.