# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 5 Conclusions  
We introduced a new approach to filter line-of-sight long-range Doppler LiDAR data dynamically. This considers the influences of atmospheric conditions, device dependencies and the measurement setup. The new methods take into account the radial velocity and the signal quality in a bi-variate manner based upon the assumption of self-similarity of valid data. Here we performed a benchmark of two implementations of the new dynamic filtering approach together with five state-of-the-art filter methods used in research and industry applications. First, a temporal high resolved time series of approximately 1.5 weeks measured in a distance of 2864 m by a minimal inclined long-range LiDAR was compared against an ultrasonic anemometer with means of 10 min to make a quantitative evaluation. Second, we performed a qualitative analysis to infer filter performance for cases of scanning interrogation of the wind field. Within this study the combined research filter approaches by [PERSON] et al., and [PERSON] et al. have been ported to a Leosphere Windcube 200s dataset. This study demonstrates, that the common practice of using fixed CNR-threshold based filters may lead to unnecessarily reduced data availability. This limitation can be overcome by more elaborated methods, which implementation is technically feasible with low computational cost. We were conditionally able to decouple the commonly associated distance dependent data availability on the CNR by introducing a temporal and spatial normalisation of measurement properties within the dynamic data filter approach that are also capable of complex changing flow situations and variations of the CNR over time. However, their general application must be thoroughly studied. Regarding the mean velocity errors, it is shown that high data availabilities do not necessarily lead to good accuracies and lower data availabilities not imply poor agreement with the reference. The resulting errors of this test case are in the range from 0.30 m/s to 0.76 m/s for the average velocity, from 2.1 m/s to 3.1 m/s for the RMS velocity error, from 0.00 m/s to 2.17 m/s for the standard deviation error and from 0.9 m/s to 4.1 m/s for the RMS velocity standard deviation error. The overall results of all filters and the parametrisation study of the Gaussian kernel based dynamic data filter indicates, that filtering can be done with the focus on the velocity dynamics in terms of the standard deviation or the average velocity. Moreover, the error evaluation varies whether the average error or the RMSE is considered. In comparison to all filters, both implementations of the new approach produce the smallest error in three of four error calculation categories whereas the combined filter approach by [PERSON] et al. was able to diminish the standard deviation velocity error to 0.0 m/s. Depending on the discipline, the application of wind LiDAR filters and the magnitude of commonly accepted errors vary, wherefore the here shown differences in the results should not be underestimated. Even small differences in the average wind speed can be the decisive argument in the resource assessment with respect to the realisation of a wind park. It is up to each user to balance  
Figure 18: Visualisation of the data density distribution of Stream Line XR PPI data from 31.10.2016 00:00 h (UTC) till 31.10.2016 00:30 h (UTC) in (**a**) \(u_{r}\)-a diagram and (**b**) in the normalised reference frame. the computational effort with the needed accuracy. The selection of a filter should comply with the analysis requirements. While the commonly used fixed CNR-threshold filter is used for fast and robust results, the histogram based dynamic data filter can be used to increase the data availability while maintaining a high accuracy. Critical applications in which a certain maximum error may not be exceeded require a more stringent filter than applications where the frequency of certain errors is a relevant criterion.