Articles | Volume 14, issue 10
Atmos. Meas. Tech., 14, 6851–6866, 2021
https://doi.org/10.5194/amt-14-6851-2021
Atmos. Meas. Tech., 14, 6851–6866, 2021
https://doi.org/10.5194/amt-14-6851-2021

Research article 25 Oct 2021

Research article | 25 Oct 2021

Reconstruction of the mass and geometry of snowfall particles from multi-angle snowflake camera (MASC) images

Jussi Leinonen 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-2021-176', Davide Ori, 03 Aug 2021
    • AC1: 'Reply on RC1', Jacopo Grazioli, 03 Sep 2021
  • RC2: 'Comment on amt-2021-176', Anonymous Referee #2, 06 Aug 2021
    • AC2: 'Reply on RC2', Jacopo Grazioli, 03 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jacopo Grazioli on behalf of the Authors (06 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (08 Sep 2021) by Maximilian Maahn
RR by Davide Ori (08 Sep 2021)
RR by Adam Hicks (24 Sep 2021)
ED: Publish subject to technical corrections (27 Sep 2021) by Maximilian Maahn
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Short summary
Measuring the shape, size and mass of a large number of snowflakes is a challenging task; it is hard to achieve in an automatic and instrumented manner. We present a method to retrieve these properties of individual snowflakes using as input a triplet of images/pictures automatically collected by a multi-angle snowflake camera (MASC) instrument. Our method, based on machine learning, is trained on artificially generated snowflakes and evaluated on 3D-printed snowflake replicas.