Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0
Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0
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基于C-Coupler2.0开发通用、灵活、高效的弱耦合集合数据同化框架
DOI:
10.5194/gmd-14-2635-2021
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发表时间:
2021-05
影响因子:
5.1
通讯作者:
Wang Bin
中科院分区:
文献类型:
--
作者:
Sun Chao;Liu Li;Li Ruizhe;Yu Xinzhu;Yu Hao;Zhao Biao;Wang Guansuo;Liu Juanjuan;Qiao Fangli;Wang Bin
Abstract. Data assimilation (DA) provides 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, especially how to conveniently
achieve efficient interaction between the ensemble of the coupled model and
the DA methods. 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 ensemble. DAFCC1
automatically and efficiently handles data exchanges between the model
ensemble members and the DA method without global communications and does
not require users to develop extra code for implementing the data exchange
functionality. Based on DAFCC1, we then develop an example weakly coupled
ensemble DA system by combining an ensemble DA system and a regional
atmosphere–ocean–wave coupled model. This example DA system and our
evaluations demonstrate the correctness of DAFCC1 in developing a weakly
coupled ensemble DA system and the effectiveness in accelerating an offline
DA system that uses disk files as the interfaces for the data exchange
functionality.
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DOI:
10.5772/30330
发表时间:
2012-01
期刊:
--
影响因子:
--
作者:
Y. Fujii;M. Kamachi;T. Nakaegawa;T. Yasuda;G. Yamanaka;T. Toyoda;K. Ando;S. Matsumoto
通讯作者:
Y. Fujii;M. Kamachi;T. Nakaegawa;T. Yasuda;G. Yamanaka;T. Toyoda;K. Ando;S. Matsumoto
DOI:
10.1002/2016jc012262
发表时间:
2017-03
期刊:
Journal of Geophysical Research - Oceans
影响因子:
--
作者:
Zhao Biao;Qiao Fangli;Cavaleri Luigi;Wang Guansuo;Bertotti Luciana;Liu Li
通讯作者:
Liu Li
DOI:
10.1016/j.envsoft.2015.02.003
发表时间:
2015-06
期刊:
Environ. Model. Softw.
影响因子:
--
作者:
P. Browne;Simon Wilson
通讯作者:
P. Browne;Simon Wilson
影响因子:
3.2
作者:
Lea, D. J.;Mirouze, I.;Thurlow, M.
通讯作者:
Thurlow, M.
DOI:
10.1109/sc.2016.4
发表时间:
2016-11
期刊:
SC16: International Conference for High Performance Computing, Networking, Storage and Analysis
影响因子:
--
作者:
F. Qiao;Wei Zhao;Xunqiang Yin;Xiaomeng Huang;Xin Liu;Qi Shu;Guansuo Wang;Zhenya Song;Xinfang Li
通讯作者:
F. Qiao;Wei Zhao;Xunqiang Yin;Xiaomeng Huang;Xin Liu;Qi Shu;Guansuo Wang;Zhenya Song;Xinfang Li