## 5 Conclusions  
The study describes a spectral analysis of Windcube v1 DBS data in the vicinity of wind turbine wakes. Data originate from the WINTWEX-W measurement campaign performed at ECN's Wind Turbine Test Site Wieringermeer in the Netherlands. By filtering Windcube v1 DBS data as described in Section 2, we could retrieve energy spectra of the along wind component and of TKE that are in good agreement with spectra from sonic anemometer measurements and wind tunnel experiment. Cross-wind spectra show a higher sensibility to beam locations and wind direction variations, but further research needs to be done to better understand LiDAR cross-wind spectra. Improvements for the spectra of the vertical component could be reached with a Windcube v2, which directly measures vertical winds.  
Despite the possible error source of probe volume averaging and the assumption of horizontal homogeneous flow, rotor-induced turbulences are captured and deliver information about wake peak frequencies dependent on prevailing stability conditions. Especially during stable conditions, wake effects are strongest, and different control strategies are needed. Improved control strategies could reduce the loads on wind turbines, as they are strongest during stable condition [31], and reduce the errors of annual energy production estimates, as the energy production is overestimated if stability conditions are not taken into account [32].  
At offshore sites, wake recovery and wake-induced loads can become a greater issue since the surface roughness is decreased, leading to a less effective mixing, especially during stable conditions. Wake peak frequencies are of importance for load response studies, as offshore turbines are usually larger. Bigger rotors are more sensitive to horizontal flow inhomogeneity and larger-scale turbulent eddies. This can become an issue for future floating offshore wind parks, since the observed wake peak frequencies would interfere with the eigenfrequencies of the floater. However, offshore campaigns are needed to investigate potential differences between onshore and offshore wake characteristics in more detail.  
**Acknowledgments:** The authors are very grateful to the Energy Centre of the Netherlands (ECN) for providing access to the test site Wieringermeer, in particular to [PERSON] and [PERSON] for their help and support in planning and realizing the campaign. Special thanks to our colleagues [PERSON] from ChristianMichelsen Research (CMR) and [PERSON] for their continuous efforts in installation, operation and data handling during and after the campaign.  
The research presented in this study was supported by and conducted under the umbrella of the Norwegian Center for Offshore Wind Energy (NORCOWE), funded by the Research Council of Norway (RCN) under Project Number 193821. The majority of the mobile LiDAR instrumentation used during the WINTWEX-W campaign was made available by the national Norwegian Research Infrastructure project OBLO (Offshore Boundary Layer Observatory), also founded by RCN (Project Number 27770). The PhD project of the first author has been funded by the collaboration agreement between BKK (Bergenshalveens Kommunale Kraftselskap) and the University of Bergen on renewable energy and energy transition.  
[PERSON] and [PERSON] conceived and designed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] wrote the paper with input form [PERSON] and [PERSON].  
The authors declare no conflict of interest.