Articles | Volume 13, issue 11
Atmos. Meas. Tech., 13, 6237–6254, 2020
Atmos. Meas. Tech., 13, 6237–6254, 2020

Research article 20 Nov 2020

Research article | 20 Nov 2020

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

Leonardo Alcayaga

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Cited articles

Alcayaga, L.: Lidar data filtering algorithms, Zenodo,, 2020. a
Ankerst, M., Breunig, M. M., Kriegel, H.-P., and Sander, J.: OPTICS: Ordering Points To Identify the Clustering Structure, in: Proc. ACM SIGMOD'99 Int. Conf. on Management of Data, 1–3 June 1999, Philadelphia, Pennsylvania, USA, pp. 49–60, ACM Press, 1999. a
Backer, E.: Computer-assisted Reasoning in Cluster Analysis, Prentice Hall International (UK) Ltd., Hertfordshire, UK, 1995. a
Banakh, V. A. and Smalikho, I. N.: Estimation of the Turbulence Energy Dissipation Rate from the Pulsed Doppler Lidar Data, Atmos. Ocean. Opt., 10, 957–965, 1997. a
Beck, H. and Kühn, M.: Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurement, Remote Sens., 9, 561,, 2017. a, b
Short summary
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. The common carrier-to-noise ratio and median filters are compared to the DBSCAN clustering algorithm to find improved data quality and recovery rate.