Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3141-2021
https://doi.org/10.5194/gmd-14-3141-2021
Model evaluation paper
 | 
02 Jun 2021
Model evaluation paper |  | 02 Jun 2021

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

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Cited articles

4Coffshore: Global Offshore Wind Farms, available at: http://www.4coffshore.com, last access: 29 May 2021. a
Abkar, M., Sharifi, A., and Porté-Agel, F.: Wake flow in a wind farm during a diurnal cycle, J. Turbulence, 17, 420–441, https://doi.org/10.1080/14685248.2015.1127379, 2016. a
Archer, C. L., Wu, S., and Ma, Y.: 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, c, d, e, f, g, h, i, j
Badger, J., Imberger, M., Volker, P., A. Kleidon, S. G., and Minz, J.: Making the most of offshore wind – re-evaluating the potential of offshore wind in the German North Sea, available at: https://www.agora-energiewende.de/en/publications/making-the-most-of-offshore-wind/ (last access: 29 May 2021), 2020. 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, https://doi.org/10.1594/PANGAEA.902845, 2019a. a, b, c, d, e, f, g
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Short summary
For the first time, turbulent kinetic energy (TKE) calculated from the explicit wake parameterization (EWP) in WRF is examined using high-frequency measurements over a wind farm and compared with that calculated using the Fitch et al. (2012) scheme. We examined the effect of farm-induced TKE advection in connection with the Fitch scheme. Through a case study with a low-level jet (LLJ), we analyzed the key features of LLJs and raised the issue of interaction between wind farms and LLJs.
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