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dc.contributor.authorCarrassi, A.
dc.contributor.authorVannitsem, S.
dc.coverage.temporal21st century
dc.descriptionIn data assimilation, observations are combined with the dynamics to get an estimate of the actual state of a natural system. The knowledge of the dynamics, under the form of a model, is unavoidably incomplete and model error affects the prediction accuracy together with the error in the initial condition. The variational assimilation theory provides a framework to deal with model error along with the uncertainties coming from other sources entering the state estimation. Nevertheless, even if the problem is formulated as Gaussian, accounting for model error requires the estimation of its covariances and correlations, which are difficult to estimate in practice, in particular because of the large system dimension and the lack of enough observations. Model error has been therefore either neglected or assumed to be an uncorrelated noise. In the present work, an approach to account for a deterministic model error in the variational assimilation is presented. Equations for its correlations are first derived along with an approximation suitable for practical applications. Based on these considerations, a new four-dimensional variational data assimilation (4DVar) weak-constraint algorithm is formulated and tested in the context of a linear unstable system and of the three-component Lorenz model, which has chaotic dynamics. The results demonstrate that this approach is superior in skill to both the strong-constraint and a weak-constraint variational assimilation that employs the uncorrelated noise model error assumption.
dc.relation.ispartofseriesMonthly Weather Review - 138
dc.titleAccounting for Model Error in Variational Data Assimilation: A deterministic formulation
dc.subject.frascatiEarth and related Environmental sciences
dc.audienceGeneral Public
dc.subject.freeData Assimilation
dc.subject.freeVariational analysis
dc.subject.freeModel errors
dc.source.issueMonthly Weather Review - 138
Orfeo.peerreviewedNot pertinent

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