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arXiv · 1207.0454

Non-Smooth Variational Data Assimilation with Sparse Priors

Ardeshir M. Ebtehaj·Efi Foufoula-Georgiou·Sara Q. Zhang·Arthur Y. Hou
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Abstract

This paper proposes an extension to the classical 3D variational data assimilation approach by explicitly incorporating as a prior information, the transform-domain sparsity observed in a large class of geophysical signals. In particular, the proposed framework extends the maximum likelihood estimation of the analysis state to the maximum a posteriori estimator, from a Bayesian perspective. The promise of the methodology is demonstrated via application to a 1D synthetic example.

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BibTeXRIS

Ardeshir M. Ebtehaj, Efi Foufoula-Georgiou, Sara Q. Zhang, Arthur Y. Hou. 2012-07-02. Non-Smooth Variational Data Assimilation with Sparse Priors. https://arxiv.org/abs/1207.0454

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