Presentations

Abstract

The Global Wind Atlas (GWA) and New European Wind Atlas (NEWA) have been pivotal in finding wind resources all over the world. Its methodology is based on combining global reanalysis data, mesoscale modelling and the microscale WAsP model. Here we present updates to the microscale part of the model chain that allow a more generic way of performing the downscaling by using PyWAsP, a python interface to the WAsP core routines. We describe the high-level workflow to go from both global reanalysis and regional modelling to microscale wind climate. Specifically, we address previous modelling limitations by introducing three major physical updates ("NEWAv2"): (1) integrating high-resolution canopy height data (Sentinel-2/GEDI) to objectively diagnose roughness and zero-plane displacement heights in forests; (2) applying a novel stability correction to the geostrophic drag law based on mesoscale-derived mean and variance of surface heat flux and boundary layer height; and (3) utilizing Copernicus DEM and WorldCover data for improved topographic representation. We validated the model chain using time series from 60 masts. The model chain including the microscale model showed significant improvements in the prediction of wind speed compared to using the mesoscale only. In addition, the mesoscale model chain showed significant improvements compared to the global reanalysis products. The inclusion of displacement height modelling was found to be crucial for correcting biases previously observed in forested terrain. This workflow was applied to generate a 50 m resolution wind atlas for Europe (~4.7 billion grid points), demonstrating a validated, scalable framework for the next generation of wind resource assessment tools.