Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 31, issue 2
Articles | Volume 31, issue 2
https://doi.org/10.5194/npg-31-287-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-31-287-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 31, issue 2
Research article
 | 
01 Jul 2024
Research article |  | 01 Jul 2024

Improving ensemble data assimilation through Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC)

Man-Yau Chan

Viewed

Total article views: 3,573 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,500 932 141 3,573 325 211 287
  • HTML: 2,500
  • PDF: 932
  • XML: 141
  • Total: 3,573
  • Supplement: 325
  • BibTeX: 211
  • EndNote: 287
Views and downloads (calculated since 28 Nov 2023)
Cumulative views and downloads (calculated since 28 Nov 2023)

Viewed (geographical distribution)

Total article views: 3,573 (including HTML, PDF, and XML) Thereof 3,493 with geography defined and 80 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 05 Sep 2026
Download
Short summary
Forecasts have uncertainties. It is thus essential to reduce these uncertainties. Such reduction requires uncertainty quantification, which often means running costly models multiple times. The cost limits the number of model runs and thus the quantification’s accuracy. This study proposes a technique that utilizes users’ knowledge of forecast uncertainties to improve uncertainty quantification. Tests show that this technique improves uncertainty reduction.
Read more
Share