Ensemble data assimilation applied to an adaptive mesh ocean model

Ensemble data assimilation applied to an adaptive mesh ocean model
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应用于自适应网格海洋模型的集合数据同化

DOI:
10.1002/fld.4247
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发表时间:
2016-12
影响因子:
1.8
通讯作者:
Navon I. M.
Navon I. M.
中科院分区:
工程技术4区
文献类型:
--
作者:
Du Juan;Zhu Jiang;Fang Fangxin;Pain C. C.;Navon I. M.

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在本研究中,首次尝试将网格自适应引入集合卡尔曼滤波器(EnKF)方法中。EnKF资料同化系统是为一个非结构自适应网格海洋模式(Fluidity,Imperial College伦敦)建立的。网格自适应包括在大流量梯度区域和观测点周围使用高分辨率网格,以减少观测值的代表性误差。自适应网格的使用必然会给EnKF的实现带来困难。系综在不同的网格上定义。为了克服这些困难,采用超网格技术生成参考网格。然后,将集合从其自身的网格内插到参考网格上。新的EnKF数据同化系统的性能进行了测试,在蒙克环流试验情况。本文的讨论将集中在(a)自适应网格模式内EnKF数据同化系统的开发和(B)网格自适应在海洋数据同化模式中的优势。版权所有© 2016约翰威利父子有限公司.
In this study, a first attempt has been made to introduce mesh adaptivity into the ensemble Kalman fiter (EnKF) method. The EnKF data assimilation system was established for an unstructured adaptive mesh ocean model (Fluidity, Imperial College London). The mesh adaptivity involved using high resolution mesh at the regions of large flow gradients and around the observation points in order to reduce the representativeness errors of the observations. The use of adaptive meshes unavoidably introduces difficulties in the implementation of EnKF. The ensembles are defined at different meshes. To overcome the difficulties, a supermesh technique is employed for generating a reference mesh. The ensembles are then interpolated from their own mesh onto the reference mesh. The performance of the new EnKF data assimilation system has been tested in the Munk gyre flow test case. The discussion of this paper will focus on (a) the development of the EnKF data assimilation system within an adaptive mesh model and (b) the advantages of mesh adaptivity in the ocean data assimilation model. Copyright © 2016 John Wiley & Sons, Ltd.
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