Articles | Volume 13, issue 5
Atmos. Meas. Tech., 13, 2219–2239, 2020
https://doi.org/10.5194/amt-13-2219-2020
Atmos. Meas. Tech., 13, 2219–2239, 2020
https://doi.org/10.5194/amt-13-2219-2020

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
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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.