Strong low-altitude winds, known as low-level jets (LLJs), significantly impact offshore wind turbines. We analyzed LLJs at the FINO1 site using LiDAR observations and reanalysis data. Our results show that models tend to underestimate LLJ intensity. To address this, we introduced a new method to characterize wind profiles and applied a correction to 50 years of reanalysis data, yielding a more accurate long-term representation of these wind features.

We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.

Wind turbine wake studies often rely on engineering models, which consist of simple analytical expressions. We review an atmospheric event resulting in a strong wind flow change within tens of minutes and apply several wake models to see how they respond to new conditions. We find that two models are consistent with their predictions, and one of them, the super-Gaussian model, predicts the wake shape particularly well; more attention should be paid to its development and implementation.

Local refinement of the grid is a powerful method allowing us to reduce the computational time while preserving the accuracy in the area of interest. Depending on the implementation, the local refinement may introduce unwanted numerical effects into the results. We study the wind speed common to the wind turbine operational speeds and confirm strong alteration of the result when the heat fluxes are present, except for the specific refinement scheme used.

We described a new automated method to separate the wind turbine wake from the undisturbed flow. The method relies on the wind speed distribution in the measured wind field to select one specific threshold value and split the measurements into wake and background points. The purpose of the method is to reduce the amount of data required – the proposed algorithm does not need precise information on the wind speed or direction and can run on the image instead of the measured data.