Articles | Volume 13, issue 12
https://doi.org/10.5194/amt-13-6579-2020
https://doi.org/10.5194/amt-13-6579-2020
Research article
 | 
07 Dec 2020
Research article |  | 07 Dec 2020

Detecting turbulent structures on single Doppler lidar large datasets: an automated classification method for horizontal scans

Ioannis Cheliotis, Elsa Dieudonné, Hervé Delbarre, Anton Sokolov, Egor Dmitriev, Patrick Augustin, and Marc Fourmentin

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Cited articles

Adrian, R. J.: Hairpin vortex organization in wall turbulence, Phys. Fluids, 19, 41301, https://doi.org/10.1063/1.2717527, 2007. 
Alparone, L., Benelli, G., and Vagniluca, A.: Texture-based analysis techniques for the classification of radar images, IET Digital Library, IEE Proc. F, 137, 276–282, https://doi.org/10.1049/ip-f-2.1990.0041, 1990. 
Aouizerats, B., Tulet, P., Pigeon, G., Masson, V., and Gomes, L.: High resolution modelling of aerosol dispersion regimes during the CAPITOUL field experiment: from regional to local scale interactions, Atmos. Chem. Phys., 11, 7547–7560, https://doi.org/10.5194/acp-11-7547-2011, 2011. 
Banta, R. M., Newsom, R. K., Lundquist, J. K., Pichugina, Y. L., Coulter, R. L., and Mahrt, L.: Nocturnal low-level jet characteristics over Kansas during cases-99, Bound.-Lay. Meteorol., 105, 221–252, https://doi.org/10.1023/A:1019992330866, 2002. 
Barthlott, C., Drobinski, P., Fesquet, C., Dubos, T., and Pietras, C.: Long-term study of coherent structures in the atmospheric surface layer, Bound.-Lay. Meteorol., 125, 1–24, https://doi.org/10.1007/s10546-007-9190-9, 2007. 
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Short summary
The current study presents an automated method to classify coherent structures near the surface, based on the observations recorded by a single scanning Doppler lidar. This methodology combines texture analysis with a supervised machine-learning algorithm in order to study large datasets. The algorithm classified correctly about 91 % of cases of a training ensemble (150 scans). Furthermore the results of a 2-month classified dataset (4577 scans) by the algorithm are presented.
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