# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 1 Introduction  
The basis of any empirical work, whether in the commercial or scientific context, is data that have been acquired through a measurement process. Recording measurement data needs a carefully planned measurement campaign, the selection of suitable instruments with sufficient resolution for the desired purpose and an adequate measurement period. In recent years, the scanning aerosol heterodyne Doppler LiDAR--hereafter LiDAR--has become a standard device when flexible, versatile measurements are needed that go beyond standard point measurements in the wind energy sector [1; 2; 3; 4; 5; 6]. Due to the measurement method of pulsed devices, it is possible to capture a plurality of quasi-instantaneously measurements along the laser beam. The internal processing of the raw measurement data in commercial LiDAR systems can mainly be seen as a black box for standard users. Although the general principle is known [7], manufacturers tend not to publish their exact processing algorithms. Invalid measurement data are occurring due to device-dependent reasons, measuring-dependent influences such as hard targets, measurements outside of the permissible parameter range and those appearing for unknown reasons. Once the measurements are conducted, it is no longer possible to determine whether physical or technical reasons formed the source of errors [8]. Thus, seemingly random outliers can arise despite good measuring conditions. Independently of the objective of analysis, it is necessary to filter valid from invalid measurements to produce accurate results. While the primary measurement value is the radial speed, the LiDAR devices measure the backscattering intensity as a secondary value. Based on the manufacture, the backscattering is calculated as carrier-to-noise-ratio (CNR), respectively signal-to-noise-ratio (SNR), that can be interpreted as a quality indicator for the calculation of the radial speed from the spectral raw data. [PERSON] [9] state that the accuracy of the radial velocity determination decreases with decreasing mean CNR level. While this conclusion from [PERSON] represent a stochastic statement, this does not imply that individual measurement points with low CNR values must be inaccurate or invalid. Our experiences with LiDAR data show that the CNR baseline is a critical criterion. LiDAR measurements carried out after rain are characterised by low CNR values whereas measurements tend to have increased backscattering in foggy situations because of temporal and spatial variations of the aerosol concentration. [PERSON] et al. [10; 11] state that the aerosol transport and distribution depend on the atmospheric boundary layer (ABL). In combination with local environmental influences, the aerosol distribution varies on timescales in the magnitude from seconds to months and thus represents an influence which justifies the need for an adaptive filtering. The most common method for filtering LiDAR data is the fixed CNR-threshold filtering based on recommended values [12; 13; 14]. Due to the simplicity and the establishment of common filtering methods, there have been very few studies dealing with the effects of LiDAR filtering to date. The first critical examination of the influence of CNR-filtering on wind speed distributions was presented by [PERSON] et al. [15]. From [PERSON] et al. [15] and [PERSON] et al. [10; 11], we interpret that LiDAR data filtering based on a rigid CNR-threshold can lead to inaccurate velocity determination. For quality assurance of the measurement data, a variety of filters may be combined to obtain an outlier free data set [16; 17]. Although many of the filters that [PERSON] et al. [16] and [PERSON] et al. [17] used, are designed, not explicitly for stationary measurements, but are applied point-wise, the question arises how smaller amount of data (for a point in space) influences the filtering in case of non-stationarity. While combinations of filters seem to be a promising approach, their application can mainly be found in scientific related work. [PERSON] et al.