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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-82
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-82
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 30 Apr 2020

Submitted as: research article | 30 Apr 2020

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This preprint is currently under review for the journal AMT.

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

Ioannis Cheliotis1, Elsa Dieudonné1, Hervé Delbarre1, Anton Sokolov1, Egor Dmitriev2, Patrick Augustin1, and Marc Fourmentin1 Ioannis Cheliotis et al.
  • 1Laboratoire de Physico-Chimie de l’Atmosphère (LPCA), UR 4493, Université du Littoral Côte d’Opale (ULCO), Dunkirk, France
  • 2Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia

Abstract. Turbulent structures can be observed using horizontal scans from single Doppler lidar or radar systems. Despite the ability to detect the structures manually on the images, this method would be time-consuming on large datasets, thus limiting the possibilities to perform studies of the turbulent structures properties over more than a few days. In order to overcome this problem, an automated classification method was developed, based on the observations recorded by a scanning Doppler lidar (LEOSPHERE WLS100) and installed atop a 75-m tower in Paris city centre (France) during a 2-months campaign (September-October 2014). The lidar recorded 4577 quasi-horizontal scans for which the turbulent component of the radial wind speed was determined using the velocity azimuth display method. Three turbulent structures types were identified by visual examination of the wind fields: unaligned thermals, rolls and streaks. A learning ensemble of 150 turbulent patterns was classified manually relying on in-situ and satellite data. The differences between the three types of structures were highlighted by enhancing the contrast of the images and computing four texture parameters (correlation, contrast, homogeneity and energy) that were provided to the supervised machine learning algorithm (quadratic discriminate analysis). Using the 10-fold cross validation method, the classification error was estimated to be about 9.2 % for the training ensemble and 3.3 % in particular for streaks. The trained algorithm applied to the whole scan ensemble detected turbulent structures on 54 % of the scans, among which 34 % were coherent turbulent structures (rolls, streaks).

Ioannis Cheliotis et al.

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Ioannis Cheliotis et al.

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Short summary
The current study presents an automated method to classify turbulent 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 % cases of a training ensemble (150 scans). Furthermore the results of a two-months classified dataset (4577 scans) by the algorithm are presented.
The current study presents an automated method to classify turbulent structures near the...
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