RMFM 2019 - Physics-Informed Super-Resolution of Climatological Wind Data
Date:
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. In this work, we apply a deep learning image transformation technique, known as super-resolution (SR), to enhance GCM wind velocity data. Our model, based off of the SRGAN model, is trained on coarsened wind velocity data from the Wind Integration National Dataset (WIND) Toolkit. Wind resource planning would greatly benefit from having GCM data at a comparable resolution to the WIND Toolkit. The model successfully increased the spatial resolution of the data 50x while preserving the underlying physics. When applied to the global wind velocity trained model was able to generate perceptually-realistic and physically-consistent wind velocity data fields at 2km resolutions from the original 100km resolution that preserve a number of turbulent flow physics like energy spectra and velocity gradients.