Posters

Abstract

In the quest to optimize wind flow modeling, the integration of microscale and mesoscale phenomena presents a significant challenge. Microscale Computational Fluid Dynamics (CFD) models excel in capturing the interaction between wind flow and topographical features but fall short in encompassing the broader physics necessary for mesoscale phenomena. Conversely, mesoscale models, adept at capturing larger-scale phenomena, lack the resolution to detail the interplay between airflow and topography. This study introduces a meso-microscale coupling methodology that aims to synergize the strengths of both modeling scales to form a comprehensive, multi-scale model. We propose a simple method that scales microscale CFD outputs using the spatial average derived from mesoscale models in post-processing. This approach maintains the mesoscale model's large-scale mean while leveraging CFD to articulate short-scale deviations from this mean, thus yielding a more accurate representation of wind flow dynamics. Our method is contrasted with traditional CFD simulations that do not incorporate mesoscale information. Comparative analyses are conducted across multiple European sites to ascertain the efficacy of our proposed method, showing that coupling with a mesoscale model decreases the cross-prediction errors by about 20-30 %. Furthermore, we demonstrate the practicality of utilizing the New European Wind Atlas as a readily accessible mesoscale data source, quickly and easily enhancing the CFD flow modeling results.