Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.558 IF 1.558
  • IF 5-year value: 1.475 IF 5-year
    1.475
  • CiteScore value: 2.8 CiteScore
    2.8
  • SNIP value: 0.921 SNIP 0.921
  • IPP value: 1.56 IPP 1.56
  • SJR value: 0.571 SJR 0.571
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 55 Scimago H
    index 55
  • h5-index value: 22 h5-index 22
Volume 24, issue 3
Nonlin. Processes Geophys., 24, 329–341, 2017
https://doi.org/10.5194/npg-24-329-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 24, 329–341, 2017
https://doi.org/10.5194/npg-24-329-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 03 Jul 2017

Research article | 03 Jul 2017

An estimate of the inflation factor and analysis sensitivity in the ensemble Kalman filter

Guocan Wu and Xiaogu Zheng

Viewed

Total article views: 1,467 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
820 501 146 1,467 119 140
  • HTML: 820
  • PDF: 501
  • XML: 146
  • Total: 1,467
  • BibTeX: 119
  • EndNote: 140
Views and downloads (calculated since 04 Oct 2016)
Cumulative views and downloads (calculated since 04 Oct 2016)

Viewed (geographical distribution)

Total article views: 1,397 (including HTML, PDF, and XML) Thereof 1,389 with geography defined and 8 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

No saved metrics found.

Saved (preprint)

No saved metrics found.

Discussed (final revised paper)

No discussed metrics found.

Discussed (preprint)

No discussed metrics found.
Latest update: 15 Aug 2020
Publications Copernicus
Download
Short summary
The accuracy of the assimilation results crucially relies on the estimate accuracy of forecast error covariance matrix in data assimilation. Ensemble Kalman filter estimates the forecast error covariance matrix as the sampling covariance matrix of the ensemble forecast states, which need to be further inflated. The experiment results on the Lorenz-96 model show that the analysis error is reduced and the analysis sensitivity to observations is improved using the proposed inflation technique.
The accuracy of the assimilation results crucially relies on the estimate accuracy of forecast...
Citation