Articles | Volume 17, issue 9
Development and technical paper
16 May 2024
Development and technical paper |  | 16 May 2024

Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)

Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2047', Anonymous Referee #1, 14 Dec 2023
  • RC2: 'Comment on egusphere-2023-2047', Paul Bowen, 14 Dec 2023
  • AC1: 'Comment on egusphere-2023-2047', Shivani Sharma, 01 Mar 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Shivani Sharma on behalf of the Authors (01 Mar 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (05 Mar 2024) by Sylwester Arabas
RR by Anonymous Referee #1 (14 Mar 2024)
ED: Publish subject to technical corrections (17 Mar 2024) by Sylwester Arabas
AR by Shivani Sharma on behalf of the Authors (18 Mar 2024)  Author's response   Manuscript 
Short summary
In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.