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: 2,117 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,456
573
88
2,117
60
63
HTML: 1,456
PDF: 573
XML: 88
Total: 2,117
BibTeX: 60
EndNote: 63
Views and downloads (calculated since 11 May 2020)
Cumulative views and downloads
(calculated since 11 May 2020)
Total article views: 1,271 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
923
302
46
1,271
20
20
HTML: 923
PDF: 302
XML: 46
Total: 1,271
BibTeX: 20
EndNote: 20
Views and downloads (calculated since 12 May 2021)
Cumulative views and downloads
(calculated since 12 May 2021)
Total article views: 846 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
533
271
42
846
40
43
HTML: 533
PDF: 271
XML: 42
Total: 846
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: 2,117 (including HTML, PDF, and XML)
Thereof 1,922 with geography defined
and 195 with unknown origin.
Total article views: 1,271 (including HTML, PDF, and XML)
Thereof 1,225 with geography defined
and 46 with unknown origin.
Total article views: 846 (including HTML, PDF, and XML)
Thereof 697 with geography defined
and 149 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...