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https://doi.org/10.5194/egusphere-2022-1316
https://doi.org/10.5194/egusphere-2022-1316
29 Nov 2022
 | 29 Nov 2022

Data-driven Reconstruction of Partially Observed Dynamical Systems

Pierre Tandeo, Pierre Ailliot, and Florian Sévellec

Abstract. The state of the atmosphere, or of the ocean, cannot be exhaustively observed. Crucial parts might remain out of reach of proper monitoring. Also, defining the exact set of equations driving the atmosphere and ocean is virtually impossible because of their complexity. Hence, the goal of this paper is to obtain predictions of a partially observed dynamical system, without knowing the model equations. In this data-driven context, the article focuses on the Lorenz-63 system, where only the second and third components are observed, and access to the equations is not allowed. To account to those strong constraints, a combination of machine learning and data assimilation techniques is proposed. The key aspects are the following: the introduction of latent variables, a linear approximation of the dynamics, and a database that is updated iteratively, maximising the innovation likelihood. We find that the latent variables inferred by the procedure are related to the successive derivatives of the observed components of the dynamical system. The method is also able to reconstruct accurately the local dynamics of the partially observed system. Overall, the proposed methodology is simple, easy to code, and gives promising results, even in the case of small amounts of observations.

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Journal article(s) based on this preprint

09 Jun 2023
Data-driven reconstruction of partially observed dynamical systems
Pierre Tandeo, Pierre Ailliot, and Florian Sévellec
Nonlin. Processes Geophys., 30, 129–137, https://doi.org/10.5194/npg-30-129-2023,https://doi.org/10.5194/npg-30-129-2023, 2023
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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The goal of this paper is to obtain probabilistic predictions of a partially observed dynamical...
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