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Original Research Papers

A POD-based ensemble four-dimensional variational assimilation method

Authors:

Xiangjun Tian ,

ICCES, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029; LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, CN
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Zhenghui Xie,

LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, CN
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Qin Sun

LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, CN
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Abstract

In this paper, a POD-based ensemble four-dimensional variational data assimilation method (referred to as PODEn4DVar) is proposed on the basis of the proper orthogonal decomposition (POD) and ensemble forecasting techniques. The ensemble forecasts are conducted to obtain the model perturbations (MPs) and their corresponding observation perturbations (OPs). Under the assumption of the linear relationship between the MPs and the OPs, the POD transformation is applied to the OP space rather than the MP space directly, which substantially decreases the computational costs. The optimal MP and its corresponding OPs is thus represented by the transformed MP ensemble and their related OP orthogonal base vectors to fit the 4-D observation innovations in the assimilation window. Further, the implementation of the forecast model ensemble update is successfully implemented by replacing the single 4-D observation innovation with the ensemble of innovation vectors. The feasibility and effectiveness of the PODEn4DVar are demonstrated in an idealized model with simulated observations. It is found that the PODEn4DVar is capable of outperforming both 4DVar and the EnKF under both perfect and imperfect-model scenarios with lower computational costs compared with EnKF.

How to Cite: Tian, X., Xie, Z. and Sun, Q., 2011. A POD-based ensemble four-dimensional variational assimilation method. Tellus A: Dynamic Meteorology and Oceanography, 63(4), pp.805–816. DOI: http://doi.org/10.1111/j.1600-0870.2011.00529.x
63
Citations
  Published on 01 Jan 2011
 Accepted on 18 Apr 2011            Submitted on 9 Nov 2010

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