Characterization of Turbulence in Wind Turbine Wakes under Different Stability Conditions from Static Doppler LiDAR Measurements  
[PERSON]  
1 Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Allegaten 70, 5007 Bergen, Norway; [EMAIL_ADDRESS] (V.K. ); [EMAIL_ADDRESS] (R.O.E. )1  
[PERSON]  
1 Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Allegaten 70, 5007 Bergen, Norway; [EMAIL_ADDRESS] (V.K. ); [EMAIL_ADDRESS] (R.O.E. )1  
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(e.g., [9]), wake recovery and downstream expansion (e.g., [2; 10; 11]) or wake meandering (e.g., [12]), in dependence of atmospheric stability. While wake measurements with scanning LiDAR systems lead to an increased understanding of wake characteristics inside the Atmospheric Boundary Layer (ABL), their advantage of spatial coverage is accompanied by limited temporal sampling at any one point in space. Therefore, we want to further investigate the capabilities and limitations of simple Doppler Beam Swinging (DBS) measurements that could complement scanning LiDAR measurements with higher sampling rates and smaller sampling volumes at certain positions in the wake. The higher temporal resolution at selected wake locations could be in particular beneficial for a spectral analysis of wind measurements in the wake. [PERSON] et al. [13] arrive in their review on turbulence estimation from Doppler LiDAR measurements at the conclusion that DBS measurements have to be interpreted carefully for the derivation of turbulence statistics, mainly due to the assumption of homogeneous flow at the positions of the four consecutive beam swings and the cross-contamination problem. The cross-contamination problem is related to the wind vector retrieval, which is originally based on the oversampling of wind vectors, but can be avoided if only one wind vector per measurement circle is further analyzed [14]. To avoid the assumption of homogeneous flow during one measurement cycle, the tendency is to go for multiple LiDAR approaches, measuring the wind vector at the same points in time and space. However, this requires the purchase of three LiDAR systems and a rather complex measurement design and data processing. [PERSON] et al. [15] and [PERSON] et al. [16] find good agreement with sonic measurement using the multi LiDAR approach. [PERSON] et al. [16] further show that, for frequencies greater than 0.1 Hz, the LiDAR probe volume averaging effect becomes significant as the level of energy starts to drop below the sonic reference measurements. Therefore, the interpretation of turbulence from multi-LiDAR measurements at frequencies higher than 0.1 Hz is still challenging. Despite the assumption of homogeneous flow, DBS measurements have shown their potential for studying wake characteristics, such as horizontal velocity deficits, increased turbulence levels and wake rotation [14; 17; 18]. [PERSON] et al. [19] present DBS measurement errors, by simulating DBS measurements with a large eddy simulation model, in the order of 2 m\(\cdot\)s\({}^{-1}\) and 1 m\(\cdot\)s\({}^{-1}\) in the near-wake region and further downstream at distances greater than three rotor Diameters (D). However, model uncertainties are not taken into account, and the simulated wind vector retrieval still includes cross-contamination effects. [PERSON] et al. [19] conclude that the accuracy of DBS wake characterization can be increased by the use of additional reference measurements. In this study, we want to highlight the performance of processed DBS measurements for the characterization of wind turbine wakes and focus on an analysis in the frequency domain. By processing the DBS data as described by [PERSON] et al. [14], we show that the cross-contamination effect can be largely eliminated, and LiDAR turbulence spectra are in good agreement with sonic anemometer measurements in free-stream and wake conditions.