Filtering of pulsed lidars data using spatial information and a clustering algorithm

Abstract

Abstract. Wind lidars present advantages over meteorological masts, including simultaneous multi-point observations, flexibility in measuring geometry, and reduced installation cost; but wind lidars come with the cost of increased complexity in terms of data quality and analysis. Carrier-to-noise ratio (CNR) has been the metric most commonly-used to recover reliable observations from lidar measurements, but with severely reduced data recovery. In this work we apply a clustering technique to identify unreliable measurements from pulsed lidars scanning a horizontal plane, taking advantage of all data available from the lidars–not only CNR, but also line-of-sight wind speed (VLOS), spatial position, and VLOS smoothness. The performance of this data filtering technique is evaluated in terms of data recovery and data quality, against both a median-like filter and a pure CNR-threshold filter. The results show that the clustering filter is capable of recovering more reliable data in noisy regions of the scans, increasing the data recovery up to 38 % and reducing by at least two thirds the acceptance of unreliable measurements, relative to the commonly used CNR-threshold. Along with this, the need for user intervention in the setup of data filtering is reduced considerably, which is a step towards a more automated and robust filter.