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Haben, S. A.; Lawless, A. S.; Nichols, N. K. (2011)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Implementations of incremental variational data assimilation require the iterative minimization of a series of linear least-squares cost functions. The accuracy and speed with which these linear minimization problems can be solved is determined by the condition number of the Hessian of the problem. In this study, we examine how different components of the assimilation system influence this condition number. Theoretical bounds on the condition number for a single parameter system are presented and used to predict how the condition number is affected by the observation distribution and accuracy and by the specified lengthscales in the background error covariance matrix. The theoretical results are verified in the Met Office variational data assimilation system, using both pseudo-observations and real data.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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