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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-75
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-75
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 11 May 2020

Submitted as: development and technical paper | 11 May 2020

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This preprint is currently under review for the journal GMD.

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

Chao Sun1, Li Liu1, Ruizhe Li1, Xinzhu Yu1, Hao Yu1, Biao Zhao1,2,3, Guansuo Wang2, Juanjuan Liu4, Fangli Qiao2,3, and Bin Wang1,4 Chao Sun et al.
  • 1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
  • 2First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China
  • 3Key Lab of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, China
  • 4State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Abstract. Data assimilation (DA) provides better initial states of model runs by combining observational information and models. Ensemble-based DA methods that depend on the ensemble run of a model have been widely used. In response to the development of seamless prediction based on coupled models or even earth system models, coupled DA is now in the mainstream of DA development. In this paper, we focus on the technical challenges in developing a coupled ensemble DA system, which have not been satisfactorily addressed to date. We first propose a new DA framework DAFCC1 (Data Assimilation Framework based on C-Coupler2.0, version 1) for weakly coupled ensemble DA, which enables users to conveniently integrate a DA method into a model as a procedure that can be directly called by the model. DAFCC1 automatically and efficiently handles data exchanges between the model ensemble members and the DA method, and enables the DA method to utilize more processor cores in parallel execution. Based on DAFCC1, we then develop a sample weakly coupled ensemble DA system by combining the ensemble DA system GSI/EnKF and the coupled model FIO-AOW. This sample DA system and our evaluations demonstrate the effectiveness of DAFCC1 in both developing a weakly coupled ensemble DA system and accelerating the DA system.

Chao Sun et al.

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Chao Sun et al.

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Latest update: 31 May 2020
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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 by combining GSI/EnKF and FIO-AOW demonstrates the effectiveness of DAFCC1.
Data assimilation (DA) provides better initial states of model runs by combining observations...
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