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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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AMT | Articles | Volume 13, issue 5
Atmos. Meas. Tech., 13, 2219–2239, 2020
https://doi.org/10.5194/amt-13-2219-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 13, 2219–2239, 2020
https://doi.org/10.5194/amt-13-2219-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 08 May 2020

Research article | 08 May 2020

A convolutional neural network for classifying cloud particles recorded by imaging probes

Georgios Touloupas 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 Anna Mirena Feist-Polner on behalf of the Authors (23 Dec 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (10 Jan 2020) by Wiebke Frey
RR by Darrel Baumgardner (12 Jan 2020)
RR by Anonymous Referee #2 (05 Mar 2020)
ED: Publish as is (05 Mar 2020) by Wiebke Frey
Publications Copernicus
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Short summary
Images of cloud particles give important information for improving our understanding of microphysical cloud processes. For phase-resolved measurements, a large number of water droplets and ice crystals need to be classified by an automated approach. In this study, a convolutional neural network was designed, which exceeds the classification ability of traditional methods and therefore shortens the analysis procedure of cloud particle images.
Images of cloud particles give important information for improving our understanding of...
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