Model error and sequential data assimilation: a deterministic formulation

Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Vannitsem, S. and Nicolis, C. (2008) Model error and sequential data assimilation: a deterministic formulation. Quarterly Journal of the Royal Meteorological Society, 134 (634). pp. 1297-1313. ISSN 1477-870X doi: 10.1002/qj.284

Abstract/Summary

Data assimilation schemes are confronted with the presence of model errors arising from the imperfect description of atmospheric dynamics. These errors are usually modelled on the basis of simple assumptions such as bias, white noise, and first‐order Markov process. In the present work, a formulation of the sequential extended Kalman filter is proposed, based on recent findings on the universal deterministic behaviour of model errors in marked contrast with previous approaches. This new scheme is applied in the context of a spatially distributed system proposed by Lorenz. First, it is found that, for short times, the estimation error is accurately approximated by an evolution law in which the variance of the model error (assumed to be a deterministic process) evolves according to a quadratic law, in agreement with the theory. Moreover, the correlation with the initial condition error appears to play a secondary role in the short‐time dynamics of the estimation error covariance. Second, the deterministic description of the model error evolution, incorporated into the classical extended Kalman filter equations, reveals that substantial improvements of the filter accuracy can be gained compared with the classical white‐noise assumption. The universal short‐time quadratic law for the evolution of the model error covariance matrix seems very promising for modelling estimation error dynamics in sequential data assimilation. Copyright © 2008 Royal Meteorological Society

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/89724
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Royal Meteorological Society
Download/View statistics	View download statistics for this item