Articles | Volume 27, issue 1
Nonlin. Processes Geophys., 27, 51–74, 2020
Nonlin. Processes Geophys., 27, 51–74, 2020

Research article 19 Feb 2020

Research article | 19 Feb 2020

Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems

Courtney Quinn et al.


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Courtney Quinn on behalf of the Authors (20 Dec 2019)  Author's response    Manuscript
ED: Publish as is (17 Jan 2020) by Alberto Carrassi
Short summary
This study presents a novel method for reduced-rank data assimilation of multiscale highly nonlinear systems. Time-varying dynamical properties are used to determine the rank and projection of the system onto a reduced subspace. The variable reduced-rank method is shown to succeed over other fixed-rank methods. This work provides implications for performing strongly coupled data assimilation with a limited number of ensemble members on high-dimensional coupled climate models.