Abstract. Wind lidars present advantages over meteorological masts, including simultaneous multipoint 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.