Articles | Volume 13, issue 12
Atmos. Meas. Tech., 13, 6579–6592, 2020
https://doi.org/10.5194/amt-13-6579-2020
Atmos. Meas. Tech., 13, 6579–6592, 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 et al.

Related authors

Experimental investigation of the stable water isotope distribution in an Alpine lake environment (L-WAIVE)
Patrick Chazette, Cyrille Flamant, Harald Sodemann, Julien Totems, Anne Monod, Elsa Dieudonné, Alexandre Baron, Andrew Seidl, Hans Christian Steen-Larsen, Pascal Doira, Amandine Durand, and Sylvain Ravier
Atmos. Chem. Phys., 21, 10911–10937, https://doi.org/10.5194/acp-21-10911-2021,https://doi.org/10.5194/acp-21-10911-2021, 2021
Short summary
Diurnal, synoptic and seasonal variability of atmospheric CO2 in the Paris megacity area
Irène Xueref-Remy, Elsa Dieudonné, Cyrille Vuillemin, Morgan Lopez, Christine Lac, Martina Schmidt, Marc Delmotte, Frédéric Chevallier, François Ravetta, Olivier Perrussel, Philippe Ciais, François-Marie Bréon, Grégoire Broquet, Michel Ramonet, T. Gerard Spain, and Christophe Ampe
Atmos. Chem. Phys., 18, 3335–3362, https://doi.org/10.5194/acp-18-3335-2018,https://doi.org/10.5194/acp-18-3335-2018, 2018
Short summary
Characterization of smoke and dust episode over West Africa: comparison of MERRA-2 modeling with multiwavelength Mie–Raman lidar observations
Igor Veselovskii, Philippe Goloub, Thierry Podvin, Didier Tanre, Arlindo da Silva, Peter Colarco, Patricia Castellanos, Mikhail Korenskiy, Qiaoyun Hu, David N. Whiteman, Daniel Pérez-Ramírez, Patrick Augustin, Marc Fourmentin, and Alexei Kolgotin
Atmos. Meas. Tech., 11, 949–969, https://doi.org/10.5194/amt-11-949-2018,https://doi.org/10.5194/amt-11-949-2018, 2018
Short summary
The first 1-year-long estimate of the Paris region fossil fuel CO2 emissions based on atmospheric inversion
Johannes Staufer, Grégoire Broquet, François-Marie Bréon, Vincent Puygrenier, Frédéric Chevallier, Irène Xueref-Rémy, Elsa Dieudonné, Morgan Lopez, Martina Schmidt, Michel Ramonet, Olivier Perrussel, Christine Lac, Lin Wu, and Philippe Ciais
Atmos. Chem. Phys., 16, 14703–14726, https://doi.org/10.5194/acp-16-14703-2016,https://doi.org/10.5194/acp-16-14703-2016, 2016
Retrieval of optical and physical properties of African dust from multiwavelength Raman lidar measurements during the SHADOW campaign in Senegal
I. Veselovskii, P. Goloub, T. Podvin, V. Bovchaliuk, Y. Derimian, P. Augustin, M. Fourmentin, D. Tanre, M. Korenskiy, D. N. Whiteman, A. Diallo, T. Ndiaye, A. Kolgotin, and O. Dubovik
Atmos. Chem. Phys., 16, 7013–7028, https://doi.org/10.5194/acp-16-7013-2016,https://doi.org/10.5194/acp-16-7013-2016, 2016
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Atmospheric tomography using the Nordic Meteor Radar Cluster and Chilean Observation Network De Meteor Radars: network details and 3D-Var retrieval
Gunter Stober, Alexander Kozlovsky, Alan Liu, Zishun Qiao, Masaki Tsutsumi, Chris Hall, Satonori Nozawa, Mark Lester, Evgenia Belova, Johan Kero, Patrick J. Espy, Robert E. Hibbins, and Nicholas Mitchell
Atmos. Meas. Tech., 14, 6509–6532, https://doi.org/10.5194/amt-14-6509-2021,https://doi.org/10.5194/amt-14-6509-2021, 2021
Short summary
Using vertical phase differences to better resolve 3D gravity wave structure
Corwin J. Wright, Neil P. Hindley, M. Joan Alexander, Laura A. Holt, and Lars Hoffmann
Atmos. Meas. Tech., 14, 5873–5886, https://doi.org/10.5194/amt-14-5873-2021,https://doi.org/10.5194/amt-14-5873-2021, 2021
Short summary
High-temporal-resolution wet delay gradients estimated from multi-GNSS and microwave radiometer observations
Tong Ning and Gunnar Elgered
Atmos. Meas. Tech., 14, 5593–5605, https://doi.org/10.5194/amt-14-5593-2021,https://doi.org/10.5194/amt-14-5593-2021, 2021
Short summary
Boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy
Mark T. Richardson, David R. Thompson, Marcin J. Kurowski, and Matthew D. Lebsock
Atmos. Meas. Tech., 14, 5555–5576, https://doi.org/10.5194/amt-14-5555-2021,https://doi.org/10.5194/amt-14-5555-2021, 2021
Short summary
GNSS-based water vapor estimation and validation during the MOSAiC expedition
Benjamin Männel, Florian Zus, Galina Dick, Susanne Glaser, Maximilian Semmling, Kyriakos Balidakis, Jens Wickert, Marion Maturilli, Sandro Dahlke, and Harald Schuh
Atmos. Meas. Tech., 14, 5127–5138, https://doi.org/10.5194/amt-14-5127-2021,https://doi.org/10.5194/amt-14-5127-2021, 2021
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.