Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 295–309, 2021
https://doi.org/10.5194/npg-28-295-2021
Nonlin. Processes Geophys., 28, 295–309, 2021
https://doi.org/10.5194/npg-28-295-2021

Research article 06 Jul 2021

Research article | 06 Jul 2021

Ensemble Riemannian data assimilation over the Wasserstein space

Sagar K. Tamang et al.

Viewed

Total article views: 1,913 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,479 411 23 1,913 15 7
  • HTML: 1,479
  • PDF: 411
  • XML: 23
  • Total: 1,913
  • BibTeX: 15
  • EndNote: 7
Views and downloads (calculated since 09 Mar 2021)
Cumulative views and downloads (calculated since 09 Mar 2021)

Viewed (geographical distribution)

Total article views: 1,814 (including HTML, PDF, and XML) Thereof 1,814 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Jan 2022
Download
Short summary
Data assimilation aims to improve hydrologic and weather forecasts by combining available information from Earth system models and observations. The classical approaches to data assimilation usually proceed with some preconceived assumptions about the shape of their probability distributions. As a result, when such assumptions are invalid, the forecast accuracy suffers. In the proposed methodology, we relax such assumptions and demonstrate improved performance.