Articles | Volume 14, issue 5
Geosci. Model Dev., 14, 2635–2657, 2021
https://doi.org/10.5194/gmd-14-2635-2021

Special issue: Model infrastructure integration and interoperability (MI3)

Geosci. Model Dev., 14, 2635–2657, 2021
https://doi.org/10.5194/gmd-14-2635-2021

Development and technical paper 12 May 2021

Development and technical paper | 12 May 2021

Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0

Chao Sun et al.

Viewed

Total article views: 1,078 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
827 203 48 1,078 40 43
  • HTML: 827
  • PDF: 203
  • XML: 48
  • Total: 1,078
  • BibTeX: 40
  • EndNote: 43
Views and downloads (calculated since 11 May 2020)
Cumulative views and downloads (calculated since 11 May 2020)

Viewed (geographical distribution)

Total article views: 928 (including HTML, PDF, and XML) Thereof 927 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 01 Aug 2021
Download
Short summary
Data assimilation (DA) provides better initial states of model runs by combining observations and models. This work focuses on the technical challenges in developing a coupled ensemble-based DA system and proposes a new DA framework DAFCC1 based on C-Coupler2. DAFCC1 enables users to conveniently integrate a DA method into a model with automatic and efficient data exchanges. A sample DA system that combines GSI/EnKF and FIO-AOW demonstrates the effectiveness of DAFCC1.