# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 4 Results
### Evaluation Based on Scanning Measurements  
The goodness of the filters must be evaluated in a broad range of applications. The previous staring study does not include the additional spatial effect given in scanning trajectories. Such validation work, is, however, limited by a missing reference. In effect, it is very costly to setup an experiment to validate a scanning LiDAR at least at some points within the trajectory. Therefore, such evaluations have to be done here only at the qualitative level. In this respect, we processed the nacelle-based PPI-scanned measurements analogous to the staring mode measurement data, with the exception of the application of the standard deviation filter and the spatial normalisation within the dynamic data filter. Due to lower spatial measurement frequency of \(f_{\text{PPI}}=0.0\overline{45}\) Hz compared to the staring mode measurements of \(f_{\text{stare}}=2\) Hz, we enlarged the selection of radial wind speed data in beam-wise and azimuthal direction to form an equivalent amount of data to calculate the standard deviation within a 10 min segment. All CNR-threshold based filters have been used with a parametrisation of \(\alpha_{le}=~{}-25\) dB and \(\alpha_{ue}=~{}-8\) dB. The normalisation of CNR and radial speed for PPI-measurements has been extended by calculating the temporal and spatial averages for azimuthal bins of \(1^{\circ}\). Thus, we expect to consider different characteristics of the wake regions and allow potential different backscattering properties due to the complex flow structure. All other filters were used as described in the referenced publications and were applied thereon range- and angle-wise. Next, we filtered the PPI scans in 10 min segments and interpolated them scan-wise to a regular Cartesian grid. We averaged the individual scans afterwards to 10 min means. In the visualisation of the unfiltered data, it can be seen that high CNR-structures (Figure 16r) correlate with structures in the wind speed (Figure 16p) and its standard deviation (Figure 16q). The probability of occurrence of those structures in a 10 min average is improbable. It is unphysical in the sense of a flow field that sharp, irregular structures emerge in the beam direction (Figure 16r). Therefore, we assume that these structures occur due to invalid measurements. However, to produce an interference-free data set, we tried to exclude those by filtering. We may explain those structures regarding the \(u_{r}\)-\(\alpha\) diagram and the functioning of the individual filters (Figure 17). The data accumulation of measurements points close to 0 m/s in a wide range of \(\alpha\) may appear due to partly shading of hard targets or unknown reason. Obstacles, such as meteorological masts, overhead transmission lines or rotor blades of other turbines influence the laser beam partly, complete or multiple times and affect the backscattering. Therefore, a second distinct peak, besides the one of the wind speed appears in the frequency spectrum. Thus, obstacles causing high-backscattering high-amplitude peaks are fitted as often as the wind speed peaks. Figure 17 gives an indication of the functioning of the different filters. It can be seen that only the dynamic data filters and the combined filter approach by [PERSON] et al. [17] managed to eliminate the high scattering of \(u_{r}\) in the \"comb\"-shaped data distribution and prior described the data accumulation close to 0 m/s. Regarding Figures 16 and 17, a relation between the mentioned exposed structures and the filtering can be made. Based on this test case of scanned data, we observed that dynamic data filters are capable to identify more outliers than the other filters. Figure 16: Influence of different filtering methods on a 10 min averaged horizontal LiDAR scans. **(1 st column)** radial speed, **(2 nd column)** CNR mapping, **(3 rd column)** standard deviation of radial speed.