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.

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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
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Cited articles

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Bocquet, M. and Carrassi, A.: Four-dimensional ensemble variational data assimilation and the unstable subspace, Tellus A, 69, 1304504,, 2017. a
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.