Physics-Informed Super Resolution of Climatological Wind and Solar Resource Data

Many aspects of modern society—including agriculture, transportation, emergency preparation, and resource planning—rely on high resolution (HR) weather and climate data. However, due the complex nature of weather and climate models, HR meteorological data is only available at local scales and even low resolution (LR) global climate models (GCM) are extremely computationally expensive. While GCM's can forecast long term climatological trends, their resolutions are insufficient to study the impact on wind and solar energy production. In this work, we apply a deep learning image transformation technique, known as super-resolution (SR), to enhance GCM wind velocity and solar irradiance data in future climate scenarios. We propose a physics-informed variation to the super resolution generative adversarial network (SRGAN) model, which extends proven performance on super resolution of natural images to scientific datasets. Our model is trained on coarsened wind velocity data from the Wind Integration National Dataset (WIND) Toolkit, which includes a variety of meteorology data over the continental United States at a 2km resolution. The model learns the complex, nonlinear mapping from the LR input data to the associated HR output and is able to perform 50x SR in a manner that preserves the underlying turbulent flow physics in the data better than traditional SR methods. The trained model is then applied to global wind velocity data from the National Center for Atmospheric Research's Community Climate System Model 4 (CCSM4). Our model was able to generate perceptually-realistic and physically-consistent wind velocity data fields at 2km resolutions from the original 100km resolution. Additionally, we trained a similar network on direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) data from the National Solar Radiation Database (NSRDB) to demonstrate its ability to increase CCSM DNI and DHI data from 100 km to 4km. Thus, this model has the potential to be utilized as an efficient method for enhancing coarse climate data from GCMs, enabling local energy resource assessment and grid resiliency studies to be performed for different climate scenarios.