Articles | Volume 15, issue 2
Atmos. Meas. Tech., 15, 279–295, 2022
https://doi.org/10.5194/amt-15-279-2022
Atmos. Meas. Tech., 15, 279–295, 2022
https://doi.org/10.5194/amt-15-279-2022

Research article 20 Jan 2022

Research article | 20 Jan 2022

Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network

Heike Kalesse-Los et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-60', Anonymous Referee #1, 03 Jul 2021
  • RC2: 'Comment on amt-2021-60', Anonymous Referee #2, 02 Aug 2021
    • AC2: 'Reply on RC2', Heike Kalesse-Los, 01 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Heike Kalesse-Los on behalf of the Authors (01 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (07 Nov 2021) by Alexis Berne
RR by Anonymous Referee #1 (19 Nov 2021)
RR by Anonymous Referee #2 (30 Nov 2021)
ED: Publish as is (09 Dec 2021) by Alexis Berne
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Short summary
It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase clouds. Here, an artificial neural network (ANN) previously developed for Arctic clouds is applied to a mid-latitudinal cloud radar data set. The performance of this technique is contrasted to the Cloudnet target classification. For thick/multi-layer clouds, the machine learning technique is better at detecting liquid than Cloudnet, but if lidar data are available Cloudnet is at least as good as the ANN.