Hessian-based covariance approximations in variational data assimilation

Igor Gejadze (1) , Victor P. Shutyaev (2, 3) , François-Xavier Le Dimet (4)

1 UMR G-EAU - Gestion de l'Eau, Acteurs, Usages
2 Moscow Institute for Physics and Technology
3 INM-RAS - Institute of Numerical Mathematics [Moscou]
4 AIRSEA - Mathematics and computing applied to oceanic and atmospheric flows

The problem of variational data assimilation (estimation) for a nonlinear model is considered in general operator formulation. Hessian-based methods are presented to compute the estimation error covariances. The importance of dynamic formulation and the role of the Hessian and its inverse are discussed.

Mots clés: data acquisition; models; data analysis; meteorology; water quality

Mots clés: météorologie; mathématiques; acquisition de données; modèle; analyse de données; qualité de l'eau

Domaines: Mathématiques [math]; Sciences de l'environnement

Igor Gejadze, Victor P. Shutyaev, François-Xavier Le Dimet. Hessian-based covariance approximations in variational data assimilation. Russian Journal of Numerical Analysis and Mathematical Modelling, 2018, 33 (1), pp.25-39. ⟨10.1515/rnam-2018-0003⟩. ⟨hal-02068763⟩