AISRS22 - Super Resolution of Climatological Data with Generative Adversarial Networks
Date:
Accurate and high-resolution data reflecting different climate scenarios are vital for policymakers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, numerical global climate models are unable to resolve the high resolution spatiotemporal characteristics required for such decisions. Here we discuss a generative adversarial neural network capable of super resolving (SR) variables such as 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× spatial resolution enhancement of wind and solar data while maintaining physical relevance. We also extend this model to generate an ensemble of unique but physically correct SR outputs which are useful for uncertainty quantification studies. Finally, we show that a simultaneous 24x temporal SR and 10x spatial SR better captures the advection of fronts and produces more realistic wind ramp rates that are crucial for grid integration studies.