Articles | Volume 22, issue 2
https://doi.org/10.5194/npg-22-205-2015
https://doi.org/10.5194/npg-22-205-2015
Research article
 | 
07 Apr 2015
Research article |  | 07 Apr 2015

Improved variational methods in statistical data assimilation

J. Ye, N. Kadakia, P. J. Rozdeba, H. D. I. Abarbanel, and J. C. Quinn

Viewed

Total article views: 2,666 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,410 1,107 149 2,666 156 156
  • HTML: 1,410
  • PDF: 1,107
  • XML: 149
  • Total: 2,666
  • BibTeX: 156
  • EndNote: 156
Views and downloads (calculated since 10 Oct 2014)
Cumulative views and downloads (calculated since 10 Oct 2014)

Cited

Saved (final revised paper)

Latest update: 28 Mar 2024
Download
Short summary
We propose an improved method of data assimilation, in which measured data are incorporated into a physically based model. In data assimilation, one typically seeks to minimize some cost function; here, we discuss a variational approximation in which model and measurement errors are Gaussian, combined with an annealing method, to consistently identify a global minimum of this cost function. We illustrate this procedure with archetypal chaotic systems, and discuss higher-order corrections.