# An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain
## 4 Conclusions  
Two of the biggest challenges for ground-based remote sensing in wind energy are the systematic errors in the mean wind speed which can be introduced by complex flow in complex terrain and the inability of traditional profiling devices to accurately measure turbulence. This paper experimentally explores the improvements in these two areas which can be made using the multi-lidar (ML) technique. For this purpose, results from the _Kassel 2014 Experiment_ are presented, during which multiple WindScanners and a Doppler beam swinging (DBS) lidar were operated surrounding a reference mast in complex terrain. The comparison of the radial wind velocities between the WindScanner measurements and a reference sonic shows that, given an accurate setup, an excellent agreement with the reference sonic could be achieved for individual systems, even if they are positioned several kilometres from the mast. Combination of multiple lidars to an ML measurement demonstrate that this also holds true for the derivation of the horizontal wind speed. ML wind speeds were considerably closer to the mast reference than the DBS lidar at this complex terrain site. The significantly reduced scatter and the smaller directional deviation in comparison with the reference sonic clearly demonstrate the advantages of focusing multiple lidar beams at one point over the classical DBS-profiling method. Correction methods (based on e.g., flow models) can be avoided, which can significantly reduce the uncertainty in applications like wind resource assessments or site calibrations. Exploiting the flexibility of the WindScanner technology, the measurements presented here could be extended to a scan of multiple measurement locations within a planned windfarm site. Especially in complex \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **SE SW EE** & **SW SE** & **SE EE** & **SW EE** & **WC** \\ \hline \multicolumn{6}{c}{\(\overline{\mathbf{u^{\prime}}^{2}}\)} \\ \hline \(m\) & 0.796 (0.829) & 0.790 (0.816) & 1.008 (1.026) & 0.990 (0.916) & 1.651 (1.531) \\ \(b\) (m\(\cdot\)s\({}^{-1}\)) & 0.008 (0.006) & 0.008 (0.009) & \(-\)0.006 (-0.011) & 0.008 (0.006) & 0.104 (0.070) \\ \(R^{2}\) & 0.951 (0.963) & 0.954 (0.967) & 0.887 (0.896) & 0.782 (0.865) & 0.678 (0.796) \\ \hline \multicolumn{6}{c}{\(\overline{\mathbf{v^{\prime}}^{2}}\)} \\ \hline \(m\) & 0.825 (0.800) & 0.822 (0.800) & 0.884 (0.861) & 0.883 (0.890) & 1.822 (1.731) \\ \(b\) (m\(\cdot\)s\({}^{-1}\)) & 0.003 (0.008) & 0.004 (0.009) & -0.006 (-0.007) & 0.020 (0.018) & 0.076 (0.038) \\ \(R^{2}\) & 0.962 (0.963) & 0.966 (0.966) & 0.903 (0.930) & 0.861 (0.901) & 0.689 (0.737) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the linear-regression statistics for \(\overline{u^{\prime 2}}\) and \(\overline{v^{\prime 2}}\) for different lidar configurations; values in brackets are statistics for which periods with \(\overline{u}_{sonic}<4\) m\(\cdot\)s\({}^{-1}\) have been excluded; \(R^{2}\) is the goodness of the fit, \(m\) is the slope and \(b\) the intersect of the of the linear regression. terrain this could significantly reduce the uncertainty associated with the horizontal extrapolation of the wind resource. For research applications like flow model validation, the ML technique removes the constraints of a mast measurement for an accurate measurement of the horizontal wind speed. The ML measurements of \(\overline{\
u^{2}_{r}}\), \(\overline{u^{\prime 2}}\) and \(\overline{\
u^{\prime 2}}\) (for SW/SE/EE and SW/SE) showed a high correlation to the reference sonic. However, while larger scale turbulence was accurately captured, due to the relatively large measurement volume and the associated path averaging, attenuation for higher wave numbers could be observed. This results in an overall underestimation of the variances. An obvious way forward would be to reduce the measurement volume. With currently available lidar technology, however, this comes at the cost of a reduced measurement range and, therefore, reduces the flexibility of ML measurements.