Articles | Volume 15, issue 20
Geosci. Model Dev., 15, 7715–7730, 2022
https://doi.org/10.5194/gmd-15-7715-2022

Special issue: Modelling inland waters in a changing climate (GMD/ESD/TC...

Geosci. Model Dev., 15, 7715–7730, 2022
https://doi.org/10.5194/gmd-15-7715-2022
Model description paper
21 Oct 2022
Model description paper | 21 Oct 2022

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

Artur Safin 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 gmd-2021-305', Anonymous Referee #1, 22 Feb 2022
  • RC2: 'Comment on gmd-2021-305', Anonymous Referee #2, 24 Mar 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Artur Safin on behalf of the Authors (30 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (16 Jul 2022) by Wolfgang Kurtz
RR by Anonymous Referee #1 (28 Jul 2022)
ED: Reconsider after major revisions (10 Aug 2022) by Wolfgang Kurtz
AR by Artur Safin on behalf of the Authors (10 Sep 2022)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (16 Sep 2022) by Wolfgang Kurtz
RR by Anonymous Referee #1 (18 Sep 2022)
ED: Publish as is (22 Sep 2022) by Wolfgang Kurtz
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Short summary
Reconciling the differences between numerical model predictions and observational data is always a challenge. In this paper, we investigate the viability of a novel approach to the calibration of a three-dimensional hydrodynamic model of Lake Geneva, where the target parameters are inferred in terms of distributions. We employ a filtering technique that generates physically consistent model trajectories and implement a neural network to enable bulk-to-skin temperature conversion.