Non-Smooth Variational Data Assimilation with Sparse Priors
Data Analysis, Statistics and Probability
2012-07-03 v1 Geophysics
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.
Cite
@article{arxiv.1207.0454,
title = {Non-Smooth Variational Data Assimilation with Sparse Priors},
author = {Ardeshir M. Ebtehaj and Efi Foufoula-Georgiou and Sara Q. Zhang and Arthur Y. Hou},
journal= {arXiv preprint arXiv:1207.0454},
year = {2012}
}