A low-rank approach to the solution of weak constraint variational data assimilation problems

Abstract Weak constraint four-dimensional variational data assimilation is an important method for incorporating data (typically observations) into a model. The linearised system arising within the minimisation process can be formulated as a saddle point problem. A disadvantage of this formulation is the large storage requirements involved in the linear system. In this paper, we present a low-rank approach which exploits the structure of the saddle point system using techniques and theory from solving large scale matrix equations. Numerical experiments with the linear advection–diffusion equation, and the non-linear Lorenz-95 model demonstrate the effectiveness of a low-rank Krylov subspace solver when compared to a traditional solver.

• Publication year

  2017

• Venue

  Journal of Computational Physics

• Publication date

  2017-02-23

• Fields of study

  Mathematics, Physics, Computer Science, Environmental Science

• Identifiers
  DOI 10.1016/j.jcp.2017.12.039arXiv 1702.07278
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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