基于GRAPES全球分析系统的Hybrid-3DVAR混合同化研究

Hybrid Ensemble-3DVAR Data Assimilation Scheme for the GRAPES Global Model

  • 摘要: 本文基于我国自主研发的GRAPES全球3DVAR同化系统,利用NCEP全球集合预报产品和time-lagged方法,针对膨胀系数、集合样本数和集合权重系数,开展了每日4次、连续一周的GRAPES全球Hybrid-3DVAR混合同化研究。结果表明:所有试验中,集合样本取60个、集合权重取0.5时,得到的混合同化分析和预报误差最小;在该混合同化系统中,在高层也考虑静态背景误差协方差和集合背景误差协方差的耦合,可避免混合同化方案分析场误差在150 hPa及以上过大,并超过3DVAR分析场误差的情况。

     

    Abstract: Using NCEP ensemble forecast products and continuous experiments, the GRAPES global ensemble-3DVAR, which is a new hybrid assimilation system is studied. Of all the experimental schemes, the GRAPES global ensemble-3DVAR is found to have the smallest analysis and forecast errors when the ensemble sample number is 60 and the ensemble weight is 0.5. However, above 150 hPa, the analysis error exceeded that of the 3DVAR. Therefore, the coupling of the static covariance and the ensemble covariance are also considered for upper layers, which is different from that of the original design. Our experiments revealed that this improvement could resolve the above-mentioned problem of large errors in the upper layer analysis.

     

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