Articles | Volume 13, issue 1
https://doi.org/10.5194/gmd-13-249-2020
https://doi.org/10.5194/gmd-13-249-2020
Model evaluation paper
 | 
29 Jan 2020
Model evaluation paper |  | 29 Jan 2020

Turbulent kinetic energy over large offshore wind farms observed and simulated by the mesoscale model WRF (3.8.1)

Simon K. Siedersleben, Andreas Platis, Julie K. Lundquist, Bughsin Djath, Astrid Lampert, Konrad Bärfuss, Beatriz Cañadillas, Johannes Schulz-Stellenfleth, Jens Bange, Tom Neumann, and Stefan Emeis

Related authors

In situ airborne measurements of atmospheric and sea surface parameters related to offshore wind parks in the German Bight
Astrid Lampert, Konrad Bärfuss, Andreas Platis, Simon Siedersleben, Bughsin Djath, Beatriz Cañadillas, Robert Hunger, Rudolf Hankers, Mark Bitter, Thomas Feuerle, Helmut Schulz, Thomas Rausch, Maik Angermann, Alexander Schwithal, Jens Bange, Johannes Schulz-Stellenfleth, Thomas Neumann, and Stefan Emeis
Earth Syst. Sci. Data, 12, 935–946, https://doi.org/10.5194/essd-12-935-2020,https://doi.org/10.5194/essd-12-935-2020, 2020
Short summary

Related subject area

Atmospheric sciences
Emission ensemble approach to improve the development of multi-scale emission inventories
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024,https://doi.org/10.5194/gmd-17-3631-2024, 2024
Short summary
What is the relative impact of nudging and online coupling on meteorological variables, pollutant concentrations and aerosol optical properties?
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024,https://doi.org/10.5194/gmd-17-3645-2024, 2024
Short summary
Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024,https://doi.org/10.5194/gmd-17-3617-2024, 2024
Short summary
Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024,https://doi.org/10.5194/gmd-17-3579-2024, 2024
Short summary
Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024,https://doi.org/10.5194/gmd-17-3599-2024, 2024
Short summary

Cited articles

Abkar, M. and Porté-Agel, F.: A new wind-farm parameterization for large-scale atmospheric models, J. Renew. Sustain. Ener., 7, 013121, https://doi.org/10.1063/1.4907600, 2015. a
Ahsbahs, T., Badger, M., Volker, P., Hansen, K. S., and Hasager, C. B.: Applications of satellite winds for the offshore wind farm site Anholt, Wind Energ. Sci., 3, 573–588, https://doi.org/10.5194/wes-3-573-2018, 2018. 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, https://doi.org/10.1594/PANGAEA.902845, 2019. a
Bundesnetzagentur: Anlagenregister, available at: https://www.bundesnetzagentur.de/DE/Sachgebiete/ElektrizitaetundGas/Unternehmen_Institutionen/ErneuerbareEnergien/ZahlenDatenInformationen/EEG_Registerdaten, last access: 21 September 2019. a, b, c, d, e
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR0 MM5 Modeling System. Part I: Model Implementation and Sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001. a
Download
Short summary
Wind farms affect local weather and microclimates. These effects can be simulated in weather models, usually by removing momentum at the location of the wind farm. Some debate exists whether additional turbulence should be added to capture the enhanced mixing of wind farms. By comparing simulations to measurements from airborne campaigns near offshore wind farms, we show that additional turbulence is necessary. Without added turbulence, the mixing is underestimated during stable conditions.