Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 33, issue 3
Articles | Volume 33, issue 3
https://doi.org/10.5194/npg-33-401-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-33-401-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 33, issue 3
Research article
 | 
11 Aug 2026
Research article |  | 11 Aug 2026

Elucidating the performance of data assimilation neural networks for chaotic dynamics

Marc Bocquet, Tobias Sebastian Finn, Sibo Cheng, and Alban Farchi

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Short summary
Deep learning has been used to discover new data assimilation methods to track chaotic dynamical systems. Strikingly, these data assimilation networks can match the accuracy of ensemble-based methods using only a single state forecast. This paper first investigates the reasons for this efficacy, and then shows that their performance in more nonlinear regimes matches that of the most accurate scalable data assimilation methods, with greater efficiency and robustness.
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