  Atmospheric flows are governed by a broad variety of spatio-temporal scales,
thus making real-time numerical modeling of such turbulent flows in complex
terrain at high resolution computationally intractable. In this study, we
demonstrate a neural network approach motivated by Enhanced Super-Resolution
Generative Adversarial Networks to upscale low-resolution wind fields to
generate high-resolution wind fields in an actual wind farm in Bessaker,
Norway. The neural network-based model is shown to successfully reconstruct
fully resolved 3D velocity fields from a coarser scale while respecting the
local terrain and that it easily outperforms trilinear interpolation. We also
demonstrate that by using appropriate cost function based on domain knowledge,
we can alleviate the use of adversarial training.
