Abstract. An intentional yaw misalignment of wind turbines is currently discussed as one possibility to increase the overall energy yield of wind farms. The idea behind this control is to decrease wake losses of downstream turbines by altering the wake trajectory of the controlled upwind turbines. For an application of such an operational control, precise knowledge about the inflow wind conditions, the magnitude of wake deflection by a yawed turbine and the propagation of the wake is crucial. The dependency of the wake deflection on the ambient wind conditions as well as the uncertainty of its trajectory are not sufficiently covered in current wind farm control models. In this study we analyze multiple sources that contribute to the uncertainty of the estimation of the wake deflection downstream of yawed wind turbines in different ambient wind conditions. We find that the wake shapes and the magnitude of deflection differ in the three evaluated atmospheric boundary layers of neutral, stable and unstable thermal stability. Uncertainty in the wake deflection estimation increases for smaller temporal averaging intervals. We also consider the choice of the method to define the wake center as a source of uncertainty as it modifies the result. The variance of the wake deflection estimation increases with decreasing atmospheric stability. Control of the wake position in a highly convective environment is therefore not recommended.

A large-eddy simulation (LES) study is presented that investigates the spatial variability of temporal eddy covariance fluxes and the systematic underestimation of representative fluxes linked to them. It extends a prior numerical study by performing high resolution simulations that allow for virtual measurements down to 20 m in a convective boundary layer, so that conditions for small tower measurement sites can be analysed. It accounts for different convective regimes as the wind speed and the near-surface heat flux are varied. Moreover, it is the first LES imbalance study that extends to the stable boundary layer. It reveals shortcomings of single site measurements and the necessity of using horizontally-distributed observation networks. The imbalances in the convective case are attributed to a locally non-vanishing mean vertical advection due to turbulent organised structures (TOS). The strength of the TOS and thus the imbalance magnitude depends on height, the horizontal mean wind and the convection type. Contrary to the results of a prior study, TOS cannot generally be responsible for large energy imbalances: at low observation heights (corresponding to small towers and near-surface energy balance stations) the TOS related imbalances are generally about one order of magnitude smaller than those in field experiments. However, TOS may cause large imbalances at large towers not only in the case of cellular convection and low wind speeds, as found in the previous study, but also in the case of roll convection at large wind speeds.In the stably stratified boundary layer for all observation heights neither TOS nor significant imbalances are observed. Boundary-Layer Meteorol (2007) 123:77-98 Attempting to reduce imbalances in convective situations by applying the conventional linear detrending method increases the systematic flux underestimation. Thus, a new filter method is proposed.

a b s t r a c tIn terms of predicting wind turbine wakes, the stably stratified atmospheric boundary layer (SABL) is taking an exceptional position as wake effects and thus loads on subsequent turbines are stronger. In this study we show the impact of the SABL on power production and wake effects (power deficits) in offshore wind farms by means of measurements as well as large-eddy simulations (LES). Measurements show enhanced wake effects in the SABL compared to the unstable situation. Another influence on the power generation of an offshore wind farm is the distance of the wind farm to the shore. This is accounted for in the LES by a modification of surface characteristics at the coastal discontinuity. In addition to the effect of the coast, the numerical case study also shows the existence of local jets between the turbines of the wind farm.

A Lagrangian stochastic (LS) model, which is embedded into a parallelised largeeddy simulation (LES) model, is used for dispersion and footprint evaluations. For the first time an online coupling between LES and LS models is applied. The new model reproduces concentration patterns, which were obtained in prior studies, provided that subgrid-scale turbulence is included in the LS model. Comparisons with prior studies show that the model evaluates footprints successfully. Streamwise dispersion leads to footprint maxima that are situated less far upstream than previously reported. Negative flux footprints are detected in the convective boundary layer (CBL). The wide range of applicability of the model is shown by applying it under neutral and stable stratification. It is pointed out that the turning of the wind direction with height leads to a considerable dependency of source areas on height. First results of an application to a heterogeneously heated CBL are presented, which emphasize that footprints are severely affected by the inhomogeneity.

Previous research has revealed the need for a validation study that considers several wake quantities and code types so that decisions on the trade-off between accuracy and computational cost can be well informed and appropriate to the intended application. In addition to guiding code choice and setup, rigorous model validation exercises are needed to identify weaknesses and strengths of specific models and guide future improvements. Here, we consider 13 approaches to simulating wakes observed with a nacelle-mounted lidar at the Scaled Wind Technology Facility (SWiFT) under varying atmospheric conditions. We find that some of the main challenges in wind turbine wake modeling are related to simulating the inflow. In the neutral benchmark, model performance tracked as expected with model fidelity, with large-eddy simulations performing the best. In the more challenging stable case, steady-state Reynolds-averaged Navier-Stokes simulations were found to outperform other model alternatives because they provide the ability to more easily prescribe noncanonical inflows and their low cost allows for simulations to be repeated as needed. Dynamic measurements were only available for the unstable benchmark at a single downstream distance. These dynamic analyses revealed that differences in the performance of time-stepping models come largely from differences in wake meandering. This highlights the need for more validation exercises that take into account wake dynamics and are able to identify where these differences come from: mesh setup, inflow, turbulence models, or wake-meandering parameterizations. In addition to model validation findings, we summarize lessons learned and provide recommendations for future benchmark exercises.