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.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Ioannis Cheliotis on behalf of the Authors (04 Aug 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (06 Sep 2020) by Marcos Portabella
RR by Anonymous Referee #1 (25 Sep 2020)
ED: Publish subject to minor revisions (review by editor) (05 Oct 2020) by Marcos Portabella
AR by Ioannis Cheliotis on behalf of the Authors (13 Oct 2020)  Author's response    Manuscript
ED: Publish as is (25 Oct 2020) by Marcos Portabella
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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.