Synthetic generation of long turbulent wind time series using hindcast model forcing for offshore wind farm simulation

Abstract. Offshore wind energy is crucial for the transition to a low-carbon society, and accurate modeling of turbulent wind fields is essential for the design and operation of offshore wind farms. This study aims to bridge the gap between mesoscale and microscale wind fluctuations to generate long time series that are statistically and spectrally representative of real observations, capturing the non-stationary nature of turbulence. Mesoscale data from NORA3 is combined with microscale spectra from Cheynet et al. (2018) using methodologies from Veers (1988); Sørensen et al. (2002); Chabaud (2024a) and the splicing technique introduced in Chabaud (2024b). The validation process uses observational data from the FINO1 weather mast. The model accurately reproduces the wind statistics. The along wind turbulence intensity is within a 85 % confidence interval of ±0.02 for 2 h simulations. The model is performing slightly better in stable conditions. The spectral representation is also good for periods between 2 min and 24 h. There, a mesoscale term is added to the microscale model following Larsén et al. (2013) —fitted parameters are provided— to bridge the gap between the hourly resolution of NORA3 and the typical minute-scale microscale range. The good performances and low computational needs of the presented methodology open new possibilities for the modeling of turbulence intensity, for instance for forecasting.