Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 493–507, 2022
https://doi.org/10.5194/gmd-15-493-2022
Geosci. Model Dev., 15, 493–507, 2022
https://doi.org/10.5194/gmd-15-493-2022

Model description paper 21 Jan 2022

Model description paper | 21 Jan 2022

Parameterization of the collision–coalescence process using series of basis functions: COLNETv1.0.0 model development using a machine learning approach

Camilo Fernando Rodríguez Genó and Léster Alfonso

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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 gmd-2021-125', Anonymous Referee #1, 15 Jul 2021
    • AC1: 'Reply on RC1', Lester Alfonso, 13 Aug 2021
  • RC2: 'Comment on gmd-2021-125', Anonymous Referee #2, 16 Jul 2021
    • AC2: 'Reply on RC2', Lester Alfonso, 13 Aug 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Lester Alfonso on behalf of the Authors (13 Aug 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (01 Sep 2021) by Sylwester Arabas
RR by Anonymous Referee #2 (23 Sep 2021)
RR by Anonymous Referee #1 (06 Oct 2021)
ED: Publish subject to minor revisions (review by editor) (14 Oct 2021) by Sylwester Arabas
AR by Lester Alfonso on behalf of the Authors (23 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (09 Nov 2021) by Sylwester Arabas
AR by Lester Alfonso on behalf of the Authors (19 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (08 Dec 2021) by Sylwester Arabas
AR by Lester Alfonso on behalf of the Authors (14 Dec 2021)  Author's response    Manuscript
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Short summary
The representation of the collision–coalescence process in models of different scales has been a great source of uncertainty for many years. The aim of this paper is to show that machine learning techniques can be a useful tool in order to incorporate this process by emulating the explicit treatment of microphysics. Our results show that the machine learning parameterization mimics the evolution of actual droplet size distributions very well.