Articles | Volume 30, issue 2
https://doi.org/10.5194/npg-30-129-2023
https://doi.org/10.5194/npg-30-129-2023
Research article
 | 
09 Jun 2023
Research article |  | 09 Jun 2023

Data-driven reconstruction of partially observed dynamical systems

Pierre Tandeo, Pierre Ailliot, and Florian Sévellec

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Latest update: 03 Mar 2024
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Short summary
The goal of this paper is to obtain probabilistic predictions of a partially observed dynamical system without knowing the model equations. It is illustrated using the three-dimensional Lorenz system, where only two components are observed. The proposed data-driven procedure is low-cost, is easy to implement, uses linear and Gaussian assumptions and requires only a small amount of data. It is based on an iterative linear Kalman smoother with a state augmentation.