the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Dec 2025Modelling wind farm effects in HARMONIE-AROME (cycle 43.2.2) – part 2: Wind turbine database and application to Europe
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.
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Status: open (until 07 Mar 2026)
- RC1:'Comment on egusphere-2025-5407', Anonymous Referee #1, 10 Feb 2026reply
This paper is valuable in that it integrates and organizes diverse wind turbine datasets in Europe into a single, coherent resource. Although this must have been a very demanding task, the authors have successfully accomplished it.
Furthermore, they apply the dataset to HARMONIE-AROME model to investigate the local impacts of wind farms.
Based on the manuscript, the following comments are proposed:- Specific comments:
1) Figure 1 caption:
Readers encounter the abbreviations NEA and DINI for the first time in this caption, and it would be helpful to provide their full names in advance. In addition, it is unclear whether the notation “Fischereit et al. (modified)” is a common convention; it might be preferable to use a format such as “Fischereit et al. (2022c’)” or “modified–Fischereit et al. (2022c).”2) L125:
Since the EMODnet polygon is not familiar to many readers, it would be helpful to provide an intuitive map in the supplementary material so that readers can easily understand it without having to visit the website.3) Chapter 2:
Although building the wind turbine database is second goal of this paper, It would be beneficial if the process were described more clearly and explicitly.
The authors might consider adding a schematic flowchart to better organize and clarify the complexity of the procedure.4) L258:
Since the Danish Meteorological Institute is later referred to as DMI (Figure 1 and L289), the abbreviation should be introduced when it is first mentioned.5) L260:
With a horizontal resolution of 2.5 km, it seems likely that multiple turbines could fall within a single grid cell. How such cases are handled in the WFP?6) Figure 9 and Table 5:
It would be helpful to distinguish masts and lidars more clearly, as the current color strategy does not appear sufficiently clear. Adding a table line could also improve readability.7) L400:
Since the current wording may cause confusion, it might be clearer to write “1) FITCH, 2) EWP, and 3) OFF and NWF.”8) Figure 15:
The lead time on the x-axis should be made consistent with the ranges used in the text (1–11, 12–23, 24–35, and 36–47).9) Figure 20 in text:
It is unclear whether Figure 20 is correctly referenced in lines 483–484.Citation: https://doi.org/10.5194/egusphere-2025-5407-RC1
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