Accounting for model error in Tempered Ensemble Transform Particle Filter and its application to non-additive model error

Autor: Dubinkina, Svetlana, Ruchi, Sangeetika

In this paper, we trivially extend Tempered (Localized) Ensemble Transform Particle Filter---T(L)ETPF---to account for model error. We examine T(L)ETPF performance for non-additive model error in a low-dimensional and a high-dimensional test problem.

Externí odkaz: http://arxiv.org/abs/1901.03144

Asymptotic forecast uncertainty and the unstable subspace in the presence of additive model error

Autor: Grudzien, Colin, Carrassi, Alberto, Bocquet, Marc

It is well understood that dynamic instability is among the primary drivers of forecast uncertainty in chaotic, physical systems. Data assimilation techniques have been designed to exploit this phenomena, reducing the effective dimension of the data

Externí odkaz: http://arxiv.org/abs/1707.08334

Asynchronous data assimilation with the EnKF in presence of additive model error.

Autor: Sakov, Pavel¹ pavel.sakov@bom.gov.au, Bocquet, Marc²

Publikováno v: Tellus: Series A. Dec2018, Vol. 70 Issue 1, p1-7. 7p.

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An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error

Autor: Serge Gratton, Marc Bocquet, Anthony Fillion, Pavel Sakov, Selime Gürol

Publikováno v: SIAM/ASA Journal on Uncertainty Quantification
SIAM/ASA Journal on Uncertainty Quantification, ASA, American Statistical Association, 2020, 8 (1), pp.198-228. ⟨10.1137/19M1244147⟩

International audience; Ensemble variational methods are being increasingly used in the field of geophysical data assimilation. Their efficiency comes from the combined use of ensembles, which provide statistics estimates, and a variational analysis,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3525acc88b6bf2f938a79e97968dcd7e
https://doi.org/10.1137/19m1244147