Ensemble Riemannian data assimilation: towards large-scale dynamical systems

Tamang, S. K. ORCID: https://orcid.org/0000-0001-8301-3576, Ebtehaj, A., Van Leeuwen, P. J. ORCID: https://orcid.org/0000-0003-2325-5340, Lerman, G. and Foufoula-Georgiou, E. ORCID: https://orcid.org/0000-0003-1078-231X (2022) Ensemble Riemannian data assimilation: towards large-scale dynamical systems. Nonlinear Processes in Geophysics, 29 (1). pp. 77-92. ISSN 1607-7946 doi: 10.5194/npg-29-77-2022

Abstract/Summary

This paper presents the results of the ensemble Riemannian data assimilation for relatively highdimensional 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/109111
Identification Number/DOI	10.5194/npg-29-77-2022
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	European Geosciences Union
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