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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1316', Anonymous Referee #1, 16 Jan 2023
  • RC2: 'Comment on egusphere-2022-1316', Anonymous Referee #2, 31 Jan 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Pierre Tandeo on behalf of the Authors (14 Apr 2023)  Author's response 
EF by Sarah Buchmann (17 Apr 2023)  Manuscript   Author's tracked changes 
ED: Publish subject to technical corrections (02 May 2023) by Natale Alberto Carrassi
AR by Pierre Tandeo on behalf of the Authors (05 May 2023)  Manuscript 
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.