Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 30, issue 2
Articles | Volume 30, issue 2
https://doi.org/10.5194/npg-30-129-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-30-129-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 2
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

Viewed

Total article views: 2,152 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,638 419 95 2,152 68 71
  • HTML: 1,638
  • PDF: 419
  • XML: 95
  • Total: 2,152
  • BibTeX: 68
  • EndNote: 71
Views and downloads (calculated since 29 Nov 2022)
Cumulative views and downloads (calculated since 29 Nov 2022)

Viewed (geographical distribution)

Total article views: 2,152 (including HTML, PDF, and XML) Thereof 2,089 with geography defined and 63 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 01 Jul 2025
Download
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.
Read more
Share