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
NPG | Articles | Volume 25, issue 4
Nonlin. Processes Geophys., 25, 765–807, 2018
https://doi.org/10.5194/npg-25-765-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Numerical modeling, predictability and data assimilation in...

Nonlin. Processes Geophys., 25, 765–807, 2018
https://doi.org/10.5194/npg-25-765-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review article 12 Nov 2018

Review article | 12 Nov 2018

Review article: Comparison of local particle filters and new implementations

Alban Farchi and Marc Bocquet

Viewed

Total article views: 2,476 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,759 651 66 2,476 96 76
  • HTML: 1,759
  • PDF: 651
  • XML: 66
  • Total: 2,476
  • BibTeX: 96
  • EndNote: 76
Views and downloads (calculated since 05 Mar 2018)
Cumulative views and downloads (calculated since 05 Mar 2018)

Viewed (geographical distribution)

Total article views: 1,924 (including HTML, PDF, and XML) Thereof 1,915 with geography defined and 9 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: 10 Aug 2020
Publications Copernicus
Download
Short summary
Data assimilation looks for an optimal way to learn from observations of a dynamical system to improve the quality of its predictions. The goal is to filter out the noise (both observation and model noise) to retrieve the true signal. Among all possible methods, particle filters are promising; the method is fast and elegant, and it allows for a Bayesian analysis. In this review paper, we discuss implementation techniques for (local) particle filters in high-dimensional systems.
Data assimilation looks for an optimal way to learn from observations of a dynamical system to...
Citation