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Time series-based wind studies are gaining more and more interest and importance within the wind resource community. Benefits of such time series-based analysis being, among others, more accurate production and loss estimations, better representation of ramp-up and extreme events, and improvements in the dimensioning of energy storage systems. While mesoscale Numerical Weather Prediction (NWP) models can simulate long-term winds that capture non-stationary weather patterns, it is known that they are not able to properly resolve sub-scale processes leading to a smoothing effect. Recent studies presented spectral models that describe the full atmospheric spectrum of boundary layer winds which connect the microscale turbulent movements to the large, mesoscale fluctuations. In this work, mesoscale simulations from the Weather Research and Forecasting (WRF) model are coupled with stochastic turbulence simulations using state-of-the-art full-scale boundary-layer spectra to efficiently bridge the spectral gap between the mesoscale and the turbulence fluctuations, without requiring any local measurements nor expensive CFD simulations. The study provides a practical step-by-step approach to generate long wind speed time series at high sampling rate at any desired location and height.

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