Articles | Volume 29, issue 1
Nonlin. Processes Geophys., 29, 37–52, 2022
https://doi.org/10.5194/npg-29-37-2022
Nonlin. Processes Geophys., 29, 37–52, 2022
https://doi.org/10.5194/npg-29-37-2022

Research article 16 Feb 2022

Research article | 16 Feb 2022

Direct Bayesian model reduction of smaller scale convective activity conditioned on large-scale dynamics

Robert Polzin 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 npg-2021-26', Anonymous Referee #1, 22 Aug 2021
    • AC1: 'Reply on RC1', Robert Malte Polzin, 20 Sep 2021
  • RC2: 'Comment on npg-2021-26', Anonymous Referee #2, 14 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Robert Malte Polzin on behalf of the Authors (07 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (08 Jan 2022) by Jürgen Kurths
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Short summary
In this study, a recent algorithmic framework called Direct Bayesian Model Reduction (DBMR) is applied which provides a scalable probability-preserving identification of reduced models directly from data. The stochastic method is tested in a meteorological application towards a model reduction to latent states of smaller scale convective activity conditioned on large-scale atmospheric flow.