Assessment of five wind-farm parameterizations in the Weather Research and Forecasting model: A case study of wind farms in the North Sea

To simulate the large-scale impacts of wind farms, wind turbines are parameterized within mesoscale models in which grid sizes are typically much larger than turbine scales. Five wind-farm parameterizations were implemented in the Weather Research and Forecasting (WRF) model v4.3.3 to simulate multiple operational wind farms in the North Sea, which were verified against a satellite image, airborne measurements, and the FINO-1 meteorological mast data on 14 October 2017. The parameterization by Volker et al. underestimated turbulence and wind-speed deficit compared to measurements and to the parameterization of Fitch et al., which is the default in WRF. The Abkar and Porté-Agel parameterization gave close predictions of wind speed to that of Fitch et al. with lower magnitude of predicted turbulence, although the parameterization was sensitive to a tunable constant. The parameterization by Pan and Archer resulted in turbine-induced thrust and turbulence that were slightly less than that of Fitch et al., but resulted in a substantial drop in power generation due to the magnification of wind-speed differences in power calculation. The parameterization by Redfern et al. was not substantially different from Fitch et al. in the absence of conditions such as strong wind veer. The simulations indicated the need for a turbine-induced turbulence source within a wind-farm parameterization for improved prediction of near-surface wind speed, near-surface temperature, and turbulence. The induced turbulence was responsible for enhancing turbulent momentum flux near the surface, causing a local speed-up of near-surface wind speed inside a wind farm. Our findings highlighted that wakes from large offshore wind farms could extend 100 km downwind, reducing downwind power production as in the case of the 400-MW Bard Offshore 1 wind farm whose power output was reduced by the wakes of the 402-MW Veja Mate wind farm for this case study.