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

• URL: http://arxiv.org/abs/2309.10172v2
• Date: Tue, 2 Apr 2024 15:24:24 GMT
• Title: Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach
• Authors: Jacob Wulff Wold, Florian Stadtmann, Adil Rasheed, Mandar Tabib, Omer San, Jan-Tore Horn,
• Abstract summary: We show that a neural network-based model can reconstruct fully resolved 3D velocity fields from a coarser scale while respecting the local terrain. We also demonstrate that using appropriate cost function based on domain knowledge, we can alleviate the use of adversarial training.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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