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

Related authors

From CO2 emissions to atmospheric NO2 mixing ratios: simulating chemical processes and their impacts on TROPOMI retrievals over the Middle East
Ioannis Cheliotis, Thomas Lauvaux, Jinghui Lian, Theodoros Christoudias, George Georgiou, Alba Badia, Frédéric Chevallier, Pramod Kumar, Yathin Kudupaje, Ruixue Lei, and Philippe Ciais
EGUsphere, https://doi.org/10.5194/egusphere-2023-2487,https://doi.org/10.5194/egusphere-2023-2487, 2023
Preprint withdrawn
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Global sensitivity analysis of simulated remote sensing polarimetric observations over snow
Matteo Ottaviani, Gabriel Harris Myers, and Nan Chen
Atmos. Meas. Tech., 17, 4737–4756, https://doi.org/10.5194/amt-17-4737-2024,https://doi.org/10.5194/amt-17-4737-2024, 2024
Short summary
Improving the Gaussianity of radar reflectivity departures between observations and simulations using symmetric rain rates
Yudong Gao, Lidou Huyan, Zheng Wu, and Bojun Liu
Atmos. Meas. Tech., 17, 4675–4686, https://doi.org/10.5194/amt-17-4675-2024,https://doi.org/10.5194/amt-17-4675-2024, 2024
Short summary
On the temperature stability requirements of free-running Nd:YAG lasers for atmospheric temperature profiling through the rotational Raman technique
José Alex Zenteno-Hernández, Adolfo Comerón, Federico Dios, Alejandro Rodríguez-Gómez, Constantino Muñoz-Porcar, Michaël Sicard, Noemi Franco, Andreas Behrendt, and Paolo Di Girolamo
Atmos. Meas. Tech., 17, 4687–4694, https://doi.org/10.5194/amt-17-4687-2024,https://doi.org/10.5194/amt-17-4687-2024, 2024
Short summary
Limitations in wavelet analysis of non-stationary atmospheric gravity wave signatures in temperature profiles
Robert Reichert, Natalie Kaifler, and Bernd Kaifler
Atmos. Meas. Tech., 17, 4659–4673, https://doi.org/10.5194/amt-17-4659-2024,https://doi.org/10.5194/amt-17-4659-2024, 2024
Short summary
A new non-linearity correction method for the spectrum from the Geostationary Inferometric Infrared Sounder on board Fengyun-4 satellites and its preliminary assessments
Qiang Guo, Yuning Liu, Xin Wang, and Wen Hui
Atmos. Meas. Tech., 17, 4613–4627, https://doi.org/10.5194/amt-17-4613-2024,https://doi.org/10.5194/amt-17-4613-2024, 2024
Short summary

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. 
Download
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.