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- W4242332487 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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 20â% (30â%) in the Lorenz-96 (QG) model." @default.
- W4242332487 created "2022-05-12" @default.
- W4242332487 date "2021-10-11" @default.
- W4242332487 modified "2023-10-16" @default.
- W4242332487 title "Comment on npg-2021-28" @default.
- W4242332487 doi "https://doi.org/10.5194/npg-2021-28-rc1" @default.
- W4242332487 hasPublicationYear "2021" @default.
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