Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 5
Articles | Volume 14, issue 5
https://doi.org/10.5194/gmd-14-2635-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-14-2635-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 5
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, Li Liu, Ruizhe Li, Xinzhu Yu, Hao Yu, Biao Zhao, Guansuo Wang, Juanjuan Liu, Fangli Qiao, and Bin Wang

Viewed

Total article views: 2,319 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,589 622 108 2,319 76 79
  • HTML: 1,589
  • PDF: 622
  • XML: 108
  • Total: 2,319
  • BibTeX: 76
  • EndNote: 79
Views and downloads (calculated since 11 May 2020)
Cumulative views and downloads (calculated since 11 May 2020)

Viewed (geographical distribution)

Total article views: 2,319 (including HTML, PDF, and XML) Thereof 2,122 with geography defined and 197 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Nov 2024
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.
Read more
Special issue
Model infrastructure integration and interoperability (MI3)