Geoscientific model development· 2026Q1
Modelling wind farm effects in HARMONIE-AROME (cycle 43.2.2) – Part 2: wind turbine database and application to Europe
- 3citations
- Q1SCImago
- 2026year
Short summary
A new European wind turbine database, constructed by harmonizing eight sources and using random forest models for gap-filling, enables HARMONIE-AROME weather forecasts to accurately capture wind farm impacts.
AI-generated from the title and abstract; the full text is not read.
Key points
- A harmonized European wind turbine database was created by combining eight sources, with missing data imputed using random forest models.
- HARMONIE-AROME forecasts with wind farm parameterizations (WFPs) showed significant reductions in hub-height wind speed near wind farms.
- The Fitch et al. (2012) WFP demonstrated the best performance compared to observational data, particularly near wind farms.
- Onshore turbines exert a non-negligible wake effect, necessitating their inclusion for accurate wind forecasting.
- Wind farm impacts on near-surface temperature were generally small but could be considerable during specific periods, with WFPs causing opposing warming/cooling effects at night.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract. Wind farm parameterizations (WFPs) are used to include the effects of operating wind farms on near-surface weather variables simulated by weather forecasting. In the first part of this series of papers, we implemented and evaluated two WPFs in the HARMONIE-AROME numerical weather prediction model (Fischereit et al., 2024). In this second part, we apply them in HARMONIE-AROME to perform sequential weather forecasts for Northern Europe with a lead time of 48 hours every 12 hours for two separate months. Combined, the selected summer and winter months are shown to represent the 30-year wind climate (wind speed, wind direction, and stability) in the forecast area reasonably well. A European wind turbine database is constructed as an input for the forecasts by combining eight different sources, harmonizing and filling gaps in the combined data set, filling missing data using random forest-based models, and associating wind farm information with individual turbines using a developed wind farm splitting algorithm. The final product and the algorithms are published for open access. We included scenarios for the forecasts with both on- and offshore turbines, as well as only offshore turbines, and analyzed the impact of wind farms on the hub height wind and near-surface temperature forecast. The forecasts using the WFPs show strong reductions in hub height wind speed near the wind farms. Wind forecast using the WFP of Fitch et al. (2012) compares best with observations for all sites, especially for mast and lidar sites close to wind farms. Onshore turbines have a nonnegligible wake effect both in terms of strength and area, and must, therefore, be included for accurate wind forecasting. The differences in wind speed between forecasts ignoring wind farms and with a WFP included are statistically significant for many on- and offshore farms. The impact of wind farms on near-surface temperature is small on average over the 2 months, but can be considerable during certain periods. The two WFPs cause opposing signs of impact, i.e., warming versus cooling during nighttime.
The authors' abstract, as published at the source. Geoscientific model development, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Aerospace Engineering
Aerospace EngineeringEngineering