Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 20
Articles | Volume 18, issue 20
https://doi.org/10.5194/gmd-18-7815-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-7815-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 20
Development and technical paper
 | 
27 Oct 2025
Development and technical paper |  | 27 Oct 2025

Multigrid beta filter for faster computation of ensemble covariance localization

Sho Yokota, Miodrag Rancic, Ting Lei, R. James Purser, and Manuel S. F. V. De Pondeca

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 6,918 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
6,630 188 100 6,918 72 80
  • HTML: 6,630
  • PDF: 188
  • XML: 100
  • Total: 6,918
  • BibTeX: 72
  • EndNote: 80
Views and downloads (calculated since 12 May 2025)
Cumulative views and downloads (calculated since 12 May 2025)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 6,918 (including HTML, PDF, and XML) Thereof 6,905 with geography defined and 13 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 09 Aug 2026
Download
Short summary
Covariance localization to mitigate sampling error of ensemble-based forecast error covariances is one of the main parts of the calculation in ensemble-variational data assimilation for the atmosphere. This study clarifies that the multigrid beta filter-based localization makes it several times faster than the conventional recursive filter-based one without significantly changing the analysis if a coarser filter grid is applied and filters except for the coarsest resolution are omitted.
Read more
Share