Preprints
https://doi.org/10.48550/arXiv.2403.03166
https://doi.org/10.48550/arXiv.2403.03166
09 Dec 2024
 | 09 Dec 2024

Long-window hybrid variational data assimilation methods for chaotic climate models tested with the Lorenz 63 system

Philip David Kennedy, Abhirup Banerjee, Armin Köhl, and Detlef Stammer

Abstract. A hybrid 4D-variational data assimilation method for chaotic climate models is introduced using the Lorenz '63 model. This approach aims to optimise an Earth system model (ESM), for which no adjoint exists, by utilising an adjoint model of a different, potentially simpler ESM. The technique relies on synchronisation of the model to observed time series data employing the dynamical state and parameter estimation (DSPE) method to stabilise the tangent linear system by reducing all positive Lyapunov exponents to negative values. Therefore, long windows can be used to improve parameter estimation. In this new extension a second layer of synchronisation is added between the two models, with and without an adjoint, to facilitate linearisation around the trajectory of the model without an adjoint. The method is conceptually demonstrated by synchronising two Lorenz '63 systems, representing two ESMs, one with and the other without an adjoint model. Results are presented for an idealised case of identical, perfect models and for a more realistic case in which they differ from one another. If employed with a coarser ESM with an adjoint, the method will save computational power as only one forward run with the full ESM per iteration needs to be carried out. It is demonstrated that there is negligible error and uncertainty change compared to the 'traditional' optimisation of full ESM with an adjoint. In a variation of the method outlined, synchronisation between two identical models can be used to filter noisy data. This reduces optimised parametric model uncertainty by approximately one third. Such a precision gain could prove valuable for seasonal, annual, and decadal predictions.

Share

Journal article(s) based on this preprint

24 Sep 2025
Long-window tandem variational data assimilation methods for chaotic climate models tested with the Lorenz 63 system
Philip David Kennedy, Abhirup Banerjee, Armin Köhl, and Detlef Stammer
Nonlin. Processes Geophys., 32, 353–365, https://doi.org/10.5194/npg-32-353-2025,https://doi.org/10.5194/npg-32-353-2025, 2025
Short summary
Philip David Kennedy, Abhirup Banerjee, Armin Köhl, and Detlef Stammer

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-3613', Anonymous Referee #1, 07 Jan 2025
  • RC2: 'Comment on egusphere-2024-3613', Anonymous Referee #2, 21 Jan 2025
  • AC3: 'Supplementary latexdiff file', Philip David Kennedy, 18 Apr 2025

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-3613', Anonymous Referee #1, 07 Jan 2025
  • RC2: 'Comment on egusphere-2024-3613', Anonymous Referee #2, 21 Jan 2025
  • AC3: 'Supplementary latexdiff file', Philip David Kennedy, 18 Apr 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Philip David Kennedy on behalf of the Authors (18 Apr 2025)  Author's response 
EF by Mario Ebel (22 Apr 2025)  Manuscript 
EF by Mario Ebel (22 Apr 2025)  Author's tracked changes 
ED: Referee Nomination & Report Request started (26 May 2025) by Wansuo Duan
RR by Anonymous Referee #2 (28 May 2025)
RR by Anonymous Referee #1 (07 Jun 2025)
ED: Publish as is (09 Jun 2025) by Wansuo Duan
AR by Philip David Kennedy on behalf of the Authors (17 Jun 2025)

Journal article(s) based on this preprint

24 Sep 2025
Long-window tandem variational data assimilation methods for chaotic climate models tested with the Lorenz 63 system
Philip David Kennedy, Abhirup Banerjee, Armin Köhl, and Detlef Stammer
Nonlin. Processes Geophys., 32, 353–365, https://doi.org/10.5194/npg-32-353-2025,https://doi.org/10.5194/npg-32-353-2025, 2025
Short summary
Philip David Kennedy, Abhirup Banerjee, Armin Köhl, and Detlef Stammer
Philip David Kennedy, Abhirup Banerjee, Armin Köhl, and Detlef Stammer

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 503 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
498 0 5 503 0 0
  • HTML: 498
  • PDF: 0
  • XML: 5
  • Total: 503
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 09 Dec 2024)
Cumulative views and downloads (calculated since 09 Dec 2024)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 503 (including HTML, PDF, and XML) Thereof 503 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Sep 2025
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
This work introduces and evaluates two hybrid data assimilation techniques. The first uses two syncronsied forward model runs before a single adjoint model run to consistently increase the precision of the parameter estimation. The second uses a lower resolution model with adjoint equations to drive a higher resolution ‘target’ model through data assimilation with no loss in precision compared to data assimilation without hybrid methods.
Share