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A review of operational methods of variational and ensemble-variational data assimilation

Bannister, R. N. (2017) A review of operational methods of variational and ensemble-variational data assimilation. Quarterly Journal of the Royal Meteorological Society, 143 (703). pp. 607-633. ISSN 1477-870X

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To link to this item DOI: 10.1002/qj.2982


Variational and ensemble methods have been developed separately by various research and development groups and each brings its own benefits to data assimilation. In the last decade or so various ways have been developed to combine these methods, especially with the aims of improving the background error covariance matrices and of improving efficiency. The field has become confusing, even to many specialists, and so there is now a need to summarise the methods in order to show how they work, how they are related, what benefits they bring, why they have been developed, how they perform, and what improvements are pending. This paper starts with a reminder of basic variational and ensemble techniques and shows how they can be combined to give the emerging ensemble-variational (EnVar) and hybrid methods. A key part of the paper includes details of how localisation is commonly represented. There has been a particular push to develop four-dimensional methods that are free of linearised forecast models. This paper attempts to provide derivations of the formulations of most popular schemes. These are otherwise scattered throughout the literature or absent. It builds on the nomenclature used to distinguish between methods, and discusses further possible developments to the methods, including the representation of model error.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:68685
Uncontrolled Keywords:Variational data assimilation; Ensemble data assimilation, Hybrid data assimilation; Flow dependent background error covariances; Localization; Model error; Nomenclature
Publisher:Royal Meteorological Society


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