# An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain
## 1 Introduction  
Over recent years, lidar (light detection and ranging) technology has quickly penetrated wind-energy applications. Especially in resource assessment, Doppler lidars are now widely used for wind measurements to predict the annual energy production of windfarms. In flat and homogeneous terrain, classical profiling lidars using the Doppler beam swinging (DBS) or velocity azimuth display (VAD) techniques achieve high accuracy [1]. Therefore, in recent years, their use has been adopted into national and international standards and guidelines [2; 3]. The application of Doppler lidars in complex terrain, however, remains difficult and is often associated with systematic errors in mean wind-speed estimations [4; 5]. If the flow is complex, the homogeneity assumption underlying the DBS and VAD techniques is often violated. First comparison studies have shown promising results in the capabilities of flow models in correcting this complex terrain error at individual sites [4; 6]. However, they also indicated a high sensitivity to model parameterisations [6] and remain limited to moderately complex terrain. Moreover, the application of corrections leads to an increased uncertainty. Another disadvantage of profiling lidars compared to mast-based measurements is that the estimation of turbulence characteristics, which are, e.g., relevant for wind-turbine loads, is not possible with the desired accuracy [7; 8; 9]. Most prominently, the estimation of the variance of the wind-vector components and the respective spectra suffer from systematic errors due to the effects of cross-contamination and volume averaging [9; 10]. [PERSON] and co-workers showed that some of the cross-contamination effect can be mitigated by applying a correction based on the measurement of the vertical variance [11]. However, their experimental results indicated that even after the application of the correction, a large deviation between lidar and mast-based measurements can remain. In general, a turbulence correction is difficult since it depends not just on lidar technology and measurement configuration but also the three-dimensional structure of the turbulence itself and, as a consequence, on atmospheric conditions [8; 9]. New scanning strategies thus need to be developed. A promising example is the six-beam method [12; 13], where turbulence parameters are directly derived from the radial velocity variances. Although the first encouraging experimental results have been reported in [12], other experiments indicated mixed results [11]. In complex terrain, the six-beam method might also be affected by an inhomogeneous turbulence field. Probably the most intuitive solution to reduce the potential errors in lidar measurements is to set up multiple lidars such that the various lidar beams intersect at a single point from different directions (muti-lidar technique or ML). This strongly reduces the need for flow homogeneity in lidar measurements. Moreover, intersecting lidar beams probably offer the best approach to derive highly resolved time series of the three-dimensional wind-vector and turbulence statistics [14]. In flat and homogeneous terrain, first experimental results intersecting three lidar beams next to a sonic anemometer showed encouraging results for mean wind speeds [15; 16; 17] and turbulence statistics [14; 16]. [PERSON] and co-authors showed good agreement between second-order turbulence statistics of the radial wind velocities from multiple lidar systems and a sonic anemometer measurement [14] which was corrected using the spectral tensor model of [18]. [PERSON] and co-authors achieved good agreement in second-order turbulence statistics between three intersecting lidars and a sonic anemometer to which they applied a simpler, pseudo-spatial filtering approach [16]. However, both studies were performed under idealised conditions in flat and homogeneous terrain and only report on very limited sample sizes.