State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics,
Chinese Academy of Sciences, Beijing, China
Fangli Qiao
First Institute of Oceanography, Ministry of Natural Resources,
Qingdao, China
Key Lab of Marine Science and Numerical Modeling, Ministry of
Natural Resources, Qingdao, China
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics,
Chinese Academy of Sciences, Beijing, China
Viewed
Total article views: 2,210 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,519
593
98
2,210
71
71
HTML: 1,519
PDF: 593
XML: 98
Total: 2,210
BibTeX: 71
EndNote: 71
Views and downloads (calculated since 11 May 2020)
Cumulative views and downloads
(calculated since 11 May 2020)
Total article views: 1,358 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
984
318
56
1,358
31
28
HTML: 984
PDF: 318
XML: 56
Total: 1,358
BibTeX: 31
EndNote: 28
Views and downloads (calculated since 12 May 2021)
Cumulative views and downloads
(calculated since 12 May 2021)
Total article views: 852 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
535
275
42
852
40
43
HTML: 535
PDF: 275
XML: 42
Total: 852
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: 2,210 (including HTML, PDF, and XML)
Thereof 2,013 with geography defined
and 197 with unknown origin.
Total article views: 1,358 (including HTML, PDF, and XML)
Thereof 1,309 with geography defined
and 49 with unknown origin.
Total article views: 852 (including HTML, PDF, and XML)
Thereof 704 with geography defined
and 148 with unknown origin.
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.
Data assimilation (DA) provides better initial states of model runs by combining observations...