Articles | Volume 29, issue 2
Nonlin. Processes Geophys., 29, 171–181, 2022
https://doi.org/10.5194/npg-29-171-2022
Nonlin. Processes Geophys., 29, 171–181, 2022
https://doi.org/10.5194/npg-29-171-2022
Research article
 | Highlight paper
02 May 2022
Research article  | Highlight paper | 02 May 2022

Using neural networks to improve simulations in the gray zone

Raphael Kriegmair et al.

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2021-20', Julien Brajard, 15 Jun 2021
    • AC1: 'Reply on RC1', Yvonne Ruckstuhl, 06 Sep 2021
  • RC2: 'Comment on npg-2021-20', Davide Faranda, 21 Jul 2021
    • AC2: 'Reply on RC2', Yvonne Ruckstuhl, 06 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Yvonne Ruckstuhl on behalf of the Authors (06 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (25 Sep 2021) by Stéphane Vannitsem
RR by Davide Faranda (18 Oct 2021)
RR by Julien Brajard (18 Feb 2022)
ED: Publish as is (27 Feb 2022) by Stéphane Vannitsem
Download
Short summary
Our regional numerical weather prediction models run at kilometer-scale resolutions. Processes that occur at smaller scales not yet resolved contribute significantly to the atmospheric flow. We use a neural network (NN) to represent the unresolved part of physical process such as cumulus clouds. We test this approach on a simplified, yet representative, 1D model and find that the NN corrections vastly improve the model forecast up to a couple of days.