Surface-driven categorisation of extreme wind events in convection-permitting models: Implications for wind energy planning in Central Europe

Localised surface properties are essential in assessing wind resources for renewable energy development. Here, we estimate extreme winds using three convection-permitting models (CPMs) through a systematic surface-based categorisation for Central Europe. We developed a comprehensive classification framework integrating three fundamental surface parameters: climate regimes (Koppen-Geiger), aerodynamic roughness length (Z0), and slope variability. The methodology combines these parameters into distinctive surface categories, enabling a detailed analysis of wind extremes at 100m height across different surface configurations.

We analysed wind speed time series from the CPM ensemble for each resulting surface category, focusing on extreme events and their relationship with surface characteristics. The resulting classification has provided a sound basis for 67 unique surface combinations, allowing us to compare models over varying terrain and climate types and establish substantial differences in extreme wind behaviours.

This research contributes to improving wind energy planning by (1) identifying surface configurations that may influence extreme wind predictions, (2) providing a systematic approach to evaluate model performance across different surface conditions, and (3) giving an understanding of the relationship between surface characteristics and wind extremes at turbine height. The findings directly apply to wind farm siting and risk assessment in complex terrain regions.

Our methodology and results are particularly relevant for renewable energy applications. This work addresses critical needs in wind energy planning by improving our understanding of extreme wind behaviour across diverse surface conditions.