Articles | Volume 22, issue 6
https://doi.org/10.5194/npg-22-723-2015
https://doi.org/10.5194/npg-22-723-2015
Research article
 | 
03 Dec 2015
Research article |  | 03 Dec 2015

Multivariate localization methods for ensemble Kalman filtering

S. Roh, M. Jun, I. Szunyogh, and M. G. Genton

Viewed

Total article views: 2,353 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,218 991 144 2,353 179 159
  • HTML: 1,218
  • PDF: 991
  • XML: 144
  • Total: 2,353
  • BibTeX: 179
  • EndNote: 159
Views and downloads (calculated since 08 May 2015)
Cumulative views and downloads (calculated since 08 May 2015)

Cited

Latest update: 28 Mar 2024
Download
Short summary
This paper shows how statistical methods can be applied to the ensemble Kalman filtering technique, a widely used method in atmospheric science and other related fields. Traditional methods for covariance localization, commonly done in the field with multiple variables, are compared to the newly proposed method with the use of a parametric localization function (proposed in statistics as a class of multivariate covariance functions) in the ensemble Kalman filter system with multiple variables.