Articles | Volume 19, issue 17
https://doi.org/10.5194/gmd-19-8115-2026
https://doi.org/10.5194/gmd-19-8115-2026
Model evaluation paper
 | 
02 Sep 2026
Model evaluation paper |  | 02 Sep 2026

Modelling wind farm effects in HARMONIE-AROME (cycle 43.2.2) – Part 2: wind turbine database and application to Europe

Jana Fischereit, Bjarke T. E. Olsen, Marc Imberger, Henrik Vedel, Kristian H. Møller, Andrea N. Hahmann, and Xiaoli Guo Larsén

Related authors

Investigating the wind-wave interaction on mean wind and turbulence structure using COAWST with WRF-LES
Sima Hamzeloo, Xiaoli Guo Larsén, Alfredo Peña, Jana Fischereit, and Oscar García-Santiago
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-267,https://doi.org/10.5194/wes-2025-267, 2026
Revised manuscript under review for WES
Short summary
Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024,https://doi.org/10.5194/gmd-17-2855-2024, 2024
Short summary
Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022,https://doi.org/10.5194/wes-7-1069-2022, 2022
Short summary

Cited articles

Agarwal, N. J., Lundquist, J. K., Juliano, T. W., and Rybchuk, A.: A North Sea in situ evaluation of the Fitch wind farm parameterization within the Mellor–Yamada–Nakanishi–Niino and 3D planetary boundary layer schemes, Wind Energ. Sci., 11, 2369–2403, https://doi.org/10.5194/wes-11-2369-2026, 2026. a
Ali, K., Schultz, D. M., Revell, A., Stallard, T., and Ouro, P.: Assessment of Five Wind-Farm Parameterizations in the Weather Research and Forecasting Model: A Case Study of Wind Farms in the North Sea, Mon. Weather Rev., 151, 2333–2359, https://doi.org/10.1175/MWR-D-23-0006.1, 2023. a
Archer, C. L., Wu, S., Ma, Y., and Jiménez, P. A.: Two Corrections for Turbulent Kinetic Energy Generated by Wind Farms in the WRF Model, Mon. Weather Rev., 148, 4823–4835, https://doi.org/10.1175/MWR-D-20-0097.1, 2020. a
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.: Classification And Regression Trees, Routledge, https://doi.org/10.1201/9781315139470, 2017. a
Brümmer, B., Lange, I., and Konow, H.: Atmospheric boundary layer measurements at the 280 m high Hamburg weather mast 1995–2011: mean annual and diurnal cycles, Meteorol. Z., 21, 319–335, https://doi.org/10.1127/0941-2948/2012/0338, 2012. a, b
Short summary
We evaluated how operating wind farms influence the atmosphere in numerical weather prediction using two wind farm parameterizations in the HARMONIE-AROME model, applied by over 10 European weather services. Accurate yield forecasts require including both onshore and offshore turbines. Wind turbines slightly alter near-surface temperature (<1 K on average). We also present an open-access European wind turbine dataset combining multiple data sources.
Share