A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7).

Coupling the Weather Research and Forecasting (WRF) model with wind farm parameterization can be effective in examining the performance of large-scale wind farms. However, the current scheme is not suitable for floating wind turbines. In this study, a new scheme is developed for floating wind farm parameterization (FWFP) in the WRF model. The impacts of the side columns of a semi-submersible floating wind turbine on waves are first parameterized in the spectral wave model (SWAN) where the key idea is to consider both inertial and drag forces on side columns. A machine learning model is trained using results from idealized high-resolution SWAN simulations and then implemented in the WRF to form the FWFP. The difference between our new scheme and the original scheme in a realistic case is investigated using a coupled atmosphere–wave model. The results show that the original scheme has a lower power output in most of the grids with an average of 12 % compared to the FWFP scheme. The upstream wind speed is increased slightly compared to the original scheme (<0.4 m s -1), while the downstream wind speed is decreased but by a much larger magnitude (<1.8 m s -1). The distribution of the difference in turbulent kinetic energy (TKE) corresponds well to that of the wind speed, and the TKE budget reveals that the difference in TKE in the rotor region between the two schemes is mainly due to vertical wind shear. This demonstrates that the FWFP is necessary for both predicting the wind power and evaluating the impact of floating wind farms on the surrounding environment. [ABSTRACT FROM AUTHOR]