Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2855-2024
https://doi.org/10.5194/gmd-17-2855-2024
Model evaluation paper
 | 
16 Apr 2024
Model evaluation paper |  | 16 Apr 2024

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

Related authors

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
A case study of wind farm effects using two wake parameterizations in the Weather Research and Forecasting (WRF) model (V3.7.1) in the presence of low-level jets
Xiaoli G. Larsén and Jana Fischereit
Geosci. Model Dev., 14, 3141–3158, https://doi.org/10.5194/gmd-14-3141-2021,https://doi.org/10.5194/gmd-14-3141-2021, 2021
Short summary

Related subject area

Atmospheric sciences
Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024,https://doi.org/10.5194/gmd-17-7285-2024, 2024
Short summary
Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5)
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024,https://doi.org/10.5194/gmd-17-7263-2024, 2024
Short summary
RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024,https://doi.org/10.5194/gmd-17-7245-2024, 2024
Short summary
Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024,https://doi.org/10.5194/gmd-17-7199-2024, 2024
Short summary
Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024,https://doi.org/10.5194/gmd-17-7001-2024, 2024
Short summary

Cited articles

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, b
Badger, M., Karagali, I., and Cavar, D.: Offshore wind fields in near-real-time, DTU [data set], https://doi.org/10.11583/DTU.19704883.v1, 2022. a, b
Baidya Roy, S. and Traiteur, J. J.: Impacts of wind farms on surface air temperatures, P. Natl. Acad. Sci. USA, 107, 17899–17904, https://doi.org/10.1073/pnas.1000493107, 2010. a
Bärfuss, K., Hankers, R., Bitter, M., Feuerle, T., Schulz, H., Rausch, T., Platis, A., Bange, J., and Lampert, A.: In-situ airborne measurements of atmospheric and sea surface parameters related to offshore wind parks in the German Bight, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.902845, 2019. a, b, c, d
Download
Short summary
Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.