Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach

2023/09/18 by Jacob Wulff Wold, Wold, Jacob Wulff, Florian Stadtmann +9
Computer Science · #Advanced Image Processing Techniques #Image and Signal Denoising Methods #Advanced Vision and Imaging

paper · pdf · doi:10.48550/arxiv.2309.10172

Abstract

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.

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