Ensemble Riemannian data assimilation: Towards large-scale dynamical systems

Sagar K Tamang, Ardeshir Ebtehaj, Peter Jan Van Leeuwen, Gilad Lerman, Efi Foufoula

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This paper presents the results of the ensemble Riemannian data assimilation for relatively high-dimensional nonlinear dynamical systems, focusing on the chaotic Lorenz-96 model and a two-layer quasi-geostrophic (QG) model of atmospheric circulation. The analysis state in this approach is inferred from a joint distribution that optimally couples the background probability distribution and the likelihood function, enabling formal treatment of systematic biases without any Gaussian assumptions. Despite the risk of the curse of dimensionality in the computation of the coupling distribution, comparisons with the classic implementation of the particle filter and the stochastic ensemble Kalman filter demonstrate that, with the same ensemble size, the presented methodology could improve the predictability of dynamical systems. In particular, under systematic errors, the root mean squared error of the analysis state can be reduced by 20ĝ€¯% (30ĝ€¯%) in the Lorenz-96 (QG) model.

Original languageEnglish (US)
Pages (from-to)77-92
Number of pages16
JournalNonlinear Processes in Geophysics
Issue number1
StatePublished - Feb 18 2022

Bibliographical note

Funding Information:
Financial support. This research has been supported by the Na-

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© 2022 The Author(s).


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