Efficient Computation of Observation Impact in 4D-Var Data Assimilation

  • Alexandru Cioaca
  • Adrian Sandu
  • Eric De Sturler
  • Emil Constantinescu
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 377)


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.


Four dimensional variational data assimilation observation impact nonlinear optimization preconditioning adjoint model iterative linear solvers 


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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Alexandru Cioaca
    • 1
  • Adrian Sandu
    • 1
  • Eric De Sturler
    • 2
  • Emil Constantinescu
    • 3
  1. 1.Department of Computer ScienceVirginia TechBlacksburgUSA
  2. 2.Department of MathematicsVirginia TechBlacksburgUSA
  3. 3.Mathematics and Computer Science DivisionArgonne National LaboratoryArgonneUSA

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