Ensemble Riemannian data assimilation: towards large-scale dynamical systems

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Abstract 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 20 % (30 %) in the Lorenz-96 (QG) model.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Ensemble Riemannian data assimilation: towards large-scale dynamical systems ; volume:29 ; number:1 ; year:2022 ; pages:77-92 ; extent:16
Nonlinear processes in geophysics ; 29, Heft 1 (2022), 77-92 (gesamt 16)

Creator: Tamang, Sagar K.
Ebtehaj, Ardeshir
Van Leeuwen, Peter Jan
Lerman, Gilad
Foufoula-Georgiou, Efi

DOI: 10.5194/npg-29-77-2022

URN: urn:nbn:de:101:1-2022022404310740615857

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:36 AM CEST

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

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Ensemble Riemannian data assimilation: towards large-scale dynamical systems

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