Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 12
Articles | Volume 13, issue 12
https://doi.org/10.5194/amt-13-6579-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-6579-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 12
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

Viewed

Total article views: 2,739 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,731 911 97 2,739 122 126
  • HTML: 1,731
  • PDF: 911
  • XML: 97
  • Total: 2,739
  • BibTeX: 122
  • EndNote: 126
Views and downloads (calculated since 30 Apr 2020)
Cumulative views and downloads (calculated since 30 Apr 2020)

Viewed (geographical distribution)

Total article views: 2,739 (including HTML, PDF, and XML) Thereof 2,564 with geography defined and 175 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
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.
Read more
Share