Articles | Volume 15, issue 2
Atmos. Meas. Tech., 15, 365–381, 2022
https://doi.org/10.5194/amt-15-365-2022

Special issue: Fusion of radar polarimetry and numerical atmospheric modelling...

Atmos. Meas. Tech., 15, 365–381, 2022
https://doi.org/10.5194/amt-15-365-2022

Research article 24 Jan 2022

Research article | 24 Jan 2022

Using artificial neural networks to predict riming from Doppler cloud radar observations

Teresa Vogl 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-2021-137', Anonymous Referee #1, 28 Jun 2021
    • AC1: 'Reply to RC1', Teresa Vogl, 08 Oct 2021
  • RC2: 'Comment on amt-2021-137', Anonymous Referee #2, 10 Jul 2021
    • AC1: 'Reply to RC1', Teresa Vogl, 08 Oct 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Teresa Vogl on behalf of the Authors (08 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (25 Oct 2021) by Patric Seifert
RR by Anonymous Referee #2 (31 Oct 2021)
RR by Anonymous Referee #1 (05 Nov 2021)
ED: Publish subject to minor revisions (review by editor) (21 Nov 2021) by Patric Seifert
AR by Teresa Vogl on behalf of the Authors (23 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (02 Dec 2021) by Patric Seifert
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Short summary
We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal. Our novel technique can be applied to different radar systems and different weather conditions.