Abstract : Data assimilation combines information from an imperfect model, sparse and noisy observations, and error statistics, to produce a best estimate of the state of a physical system. Different observational data points have different contributions to reducing the uncertainty with which the state is estimated. Quantifying the observation impact is important for analyzing the effectiveness of the assimilation system, for data pruning, and for designing future sensor networks. This paper is concerned with quantifying observation impact in the context of four dimensional variational data assimilation. The main computational challenge is posed by the solution of linear systems, where the system matrix is the Hessian of the variational cost function. This work discusses iterative strategies to efficiently solve this system and compute observation impacts.
Andrew M. Dienstfrey; Ronald F. Boisvert. 10th Working Conference on Uncertainty Quantification in Scientific Computing (WoCoUQ), Aug 2011, Boulder, CO, United States. Springer, IFIP Advances in Information and Communication Technology, AICT-377, pp.250-264, 2012, Uncertainty Quantification in Scientific Computing. 〈10.1007/978-3-642-32677-6_16〉
https://hal.inria.fr/hal-01518682
Contributeur : Hal Ifip
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Soumis le : vendredi 5 mai 2017 - 10:56:03
Dernière modification le : vendredi 5 mai 2017 - 10:57:09
Document(s) archivé(s) le : dimanche 6 août 2017 - 12:27:47
Alexandru Cioaca, Adrian Sandu, Eric Sturler, Emil Constantinescu. Efficient Computation of Observation Impact in 4D-Var Data Assimilation. Andrew M. Dienstfrey; Ronald F. Boisvert. 10th Working Conference on Uncertainty Quantification in Scientific Computing (WoCoUQ), Aug 2011, Boulder, CO, United States. Springer, IFIP Advances in Information and Communication Technology, AICT-377, pp.250-264, 2012, Uncertainty Quantification in Scientific Computing. 〈10.1007/978-3-642-32677-6_16〉. 〈hal-01518682〉
