| Strategies to overcome filter divergences in DRP-4DVar approach |
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| 作者姓名： | LIU Juan-Juan WANG Bin |
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| 作者单位： | 中科院大气物理研究所, |
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| 基金项目： | the National Basic Research Program of China (973 Program) (Grant No. 2010CB951604);the National High Technology Research and Development Program of China (863 Program) (Grant No. 2010AA012304);the China Meteorological Administration for the R&D Special Fund for Public Welfare Industry (Meteorology) (Grant No. GYHY(QX)200906009);the LASG free exploration fund |
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| 摘 要： | This paper discusses an important issue related to filter divergence in the dimension-reduced projection,four-dimensional variational data assimilation(DRP-4-DVar) approach.Idealized experiments with the Lorenz-96 model over a period of 200 days showed that the amplitudes of the root mean square errors(RMSEs) reached the same levels as those of the state variables after approximately 100 days because of the accumulation of sampling errors following the cycle of assimilation.Strategies to reduce sampling errors are critical to ensure the quality of ensemble-based assimilation.Numerical experiments showed that localization and reducing observational errors can alleviate,but cannot completely overcome,the filter divergence in the DRP-4-DVar approach,while the method of perturbing observations and the inflation technique can efficiently eliminate the filter divergence problem.
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| 关 键 词： | filter divergence DRP-4-DVar perturbing observation inflation technique |
Strategies to Overcome Filter Divergence in the DRP-4-DVar Approach |
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| LIU Juan-Juan,WANG Bin.Strategies to overcome filter divergences in DRP-4DVar approach[J].Atmospheric and Oceanic Science Letters,2011,4(3):136-138. |
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| Authors: | LIU Juan-Juan and WANG Bin |
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| Institution: | LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029; Center for Earth System Science, Tsinghua University, Beijing 100080 |
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| Abstract: | This paper discusses an important issue related to filter divergence in the dimension-reduced projection, four-dimensional variational data assimilation (DRP-4-DVar) approach. Idealized experiments with the Lorenz-96 model over a period of 200 days showed that the amplitudes of the root mean square errors (RMSEs) reached the same levels as those of the state variables after approximately 100 days because of the accumulation of sampling errors following the cycle of assimilation. Strategies to reduce sampling errors are critical to ensure the quality of ensemble-based assimilation. Numerical experiments showed that localization and reducing observational errors can alleviate, but cannot completely overcome, the filter divergence in the DRP-4-DVar approach, while the method of perturbing observations and the inflation technique can efficiently eliminate the filter divergence problem. |
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| Keywords: | filter divergence DRP-4DVar perturbing observation inflation technique |
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