# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 1 Introduction
[18] investigated the adaption of a despiking method from stationary to scanning situations and thus took an important step towards the filtering of scanned LiDAR measurements. Nevertheless, those methods were not specifically designed for an application in LiDAR remote sensing and represent more or less a best practice for general time series processing. Despite these occasional studies, LiDAR data filtering and addressing their impact remain a vacant topic. Each filter discussed in the following of this paper is based on an assumption to distinguish the validity. Namely, the CNR-threshold filter is based on the accuracy of the radial velocity with respect to the CNR, the interquartile-range filter is based on the data distribution and the standard deviation filters on the assumption of normal distribution. All these assumptions, however, do not rely on factors which affect specifically LiDAR measurements. Atmospheric conditions, but also location-specific incidents such as hard targets, terrain topography or measurement properties such as the trajectory, magnitude of measurement velocity, pulse length and accumulation time influence the data distribution and thereby the filter approach. In consequence, it seems logical to pre-filter measurement data on the basis of purpose. For example, velocity azimuth display (VAD) or Doppler beam swinging (DBS) measurements, which are designed for the calculation of wind speed and wind direction distributions easily exceed the mixing layer height and measure above the ABL. With the knowledge of a significant CNR drop at a certain height, an effective filter approach needs to behave different than a filter for stationary measurement over a constant height. With the increase of LiDAR devices for research applications and in the future for a stronger commercial use, the amount of data will exceed the capacities of manual verification of processing/filtering results and lead to the need of robust, accurate and highly adaptable routines. Because the measurement conditions differ with each device, location and time, it seems sensible and necessary to filter LiDAR data in a dynamically adaptive way to ensure high data availability and accuracy of the data set. The simultaneous use of different filter combinations is limited by the available computational power; thus, universal filters are favoured. While within combined filter approaches methods are applied successively we believe that all measurements outputs may and should be used in a multi-variate manner to satisfy their specific behaviour to determine the measurement data validity. One assumption, we find that adapt to atmospheric and external influences is the self-similarity of the measurement data. To the best of our knowledge, this approach has not been used so far to filter LiDAR data, wherefore we explain this assumption, the advantages and disadvantages in the following of this work. We introduce a highly self-adapting methodology that demonstrate how line-of-sight velocity measurements of pulsed long-range LiDAR devices can be filtered dynamically to maximise accuracy and data availability of mean radial velocities. The filter approach is designed for determining the mean velocity, and may not be appropriate for turbulence measurement applications. Further, we show that it is possible to decouple the commonly associated data availability of valid measurement data with increasing distances on the assumption of self-similarity using a temporal and spatial normalisation. A validation of the new filter approach based on temporal high resolved, low elevated Leosphere Windcube 200s data in the range of 2864 m has been carried out against ultrasonic anemometer data captured at an offshore meteorological mast in comparison to commonly established and research filters.