Abstract. In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.

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 comprehensive understanding of the wake development of wind turbines is essential for improving the power yield of wind farms and for reducing the structural loading of the turbines.Reducing the overall negative impact of wake flows on individual turbines in a farm is one goal of wind farm control. We aim to demonstrate the applicability of yaw control for deflecting wind turbine wakes in a full-scale field experiment. For this purpose, we conducted a measurement campaign at a multimegawatt onshore wind turbine including inflow and wake flow measurements using ground-and nacelle-based long-range light detection and ranging devices. Yaw misalignments of the turbine with respect to the inflow direction of up to 20 • were investigated. We were able to show that under neutral atmospheric conditions, these turbine misalignments cause lateral deflections of its wake. Larger yaw misalignments resulted in greater wake deflection.Because of the inherent struggle in capturing complex and highly dynamic ambient conditions in the field using a limited number of sensors, we particularly focused on providing a comprehensive and comprehensible description of the measurement setup, including the identification of potential uncertainties. KEYWORDSatmospheric boundary layer, atmospheric inflow, lidar, wake deflection, wind farm control INTRODUCTIONWind turbines in a wind farm are typically subjected to mutual aerodynamic interactions due to their wakes. Depending on the farm layout and inflow direction, this can lead to unfavourable structural loading and a substantial reduction in the power yield over extended periods of time . 1-3 Reducing the overall negative impact of wake flows on individual turbines in a farm is one goal of wind farm control. However, a successful implementation of control mechanisms requires a broad understanding of the flow development for various ambient conditions and the interaction between turbines and the flow.One specific approach that has proven its potential in simulations and wind tunnel experiments is wake deflection through turbine operation under yaw misalignment. In this case, an offset between the inflow direction and the orientation of a turbine is deliberately introduced to alter its wake trajectory. Hereinafter, we refer to this method as yaw control for the sake of simplicity. It aims to generate more favourable inflow conditions for downstream turbines by reducing the wake effects. This can, for example, be used to maximize the power output of a wind farm, to mitigate power fluctuation or to reduce turbine loads. With respect to power maximization, it should be considered that misaligned wind turbines generate less power. Therefore, it must be ensured that the power increase generated by the downstream turbines is sufficient for improving the overall power yield of the wind farm.The concept of wake deflection has already been successfully applied in wind tunnel experiments by Clayton and Filby in 1982. 4 Further investigations of the power yield and characteristics of the downstream d...

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 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 to the wake deflection estimation increases for smaller temporal averaging intervals. We also consider the choice of the method to define the wake center as an uncertainty as it modifies the result. The variance of the wake deflection estimation increases with decreasing atmospheric stability. A control of the wake position in a highly convective environment is therefore not recommended.