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Deterministic treatment of model error in geophysical data assimilation

Carrassi, A. and Vannitsem, S. (2016) Deterministic treatment of model error in geophysical data assimilation. In: Ancona, F., Cannarsa, P., Jones, C. and Portaluri, A. (eds.) Mathematical Paradigms of Climate Science. Springer INdAM Series, 15. Springer, pp. 175-213.

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To link to this item DOI: 10.1007/978-3-319-39092-5_9

Abstract/Summary

This chapter describes a novel approach for the treatment of model error in geophysical data assimilation. In this method, model error is treated as a deterministic process correlated in time. This allows for the derivation of the evolution equations for the relevant moments of the model error statistics required in data assimilation procedures, along with an approximation suitable for application to large numerical models typical of environmental science. In this contribution we first derive the equations for the model error dynamics in the general case, and then for the particular situation of parametric error. We show how this deterministic description of the model error can be incorporated in sequential and variational data assimilation procedures. A numerical comparison with standard methods is given using low-order dynamical systems, prototypes of atmospheric circulation, and a realistic soil model. The deterministic approach proves to be very competitive with only minor additional computational cost. Most importantly, it offers a new way to address the problem of accounting for model error in data assimilation that can easily be implemented in systems of increasing complexity and in the context of modern ensemble-based procedures.

Item Type:Book or Report Section
Refereed:Yes
Divisions:No Reading authors. Back catalogue items
Faculty of Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:90359
Publisher:Springer

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