An image-to-image adversarial network to generate high resolution wind data over complex terrains from weather predictions

In this work, we propose a Machine Learning method to predict detailed wind fields over extensive, complex terrains. The ability to predict local wind fields is becoming increasingly important for a range of applications, including sports in Nature, large outdoors events, light-aircraft flying, or the management of natural disasters. The intricate nature of wind dynamics, particularly in regions with complex orography such as a mountain range, presents a major challenge to traditional forecasting models. This work presents an efficient way to predict local wind conditions with a high resolution, similar to that of Computational Fluid Dynamics (CFD), in large geographical areas with complex terrain, using the results from relatively coarse (and therefore economical) data from Numerical Weather Prediction (NWP). To achieve this goal, we developed a conditional Generative Adversarial Neural network model (cGAN) to convert NWP data into CFD-like simulations. We apply the method to a rugged region in the Pyrenees mountain range in Spain. The results show that the proposed model outperforms traditional Machine Learning methods, such as Support Vector Machines (SVM), in terms of accuracy and computational efficiency. The method is four orders of magnitude faster than traditional CFD. Mean Average Errors of $1.36\mathrm{m/s}$ for wind speed and 18.73°for wind direction are obtained with the proposed approach.