集合變分混合同化背景誤差協(xié)方差流依賴性分析

doi:10.3969/2015jms.0069

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集合變分混合同化背景誤差協(xié)方差流依賴性分析
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                        10.3969/2015jms.0069
                    
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基金項(xiàng)目:國(guó)家自然科學(xué)基金資助項(xiàng)目(41375063)

Flow-dependent characteristics of background error covariance in hybrid variational-ensemble data assimilation

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通過(guò)單點(diǎn)觀測(cè)試驗(yàn)的方法,對(duì)集合變分混合同化背景誤差協(xié)方差的流依賴特征、流依賴性影響因子、產(chǎn)生原因,以及集合預(yù)報(bào)方法對(duì)流依賴性的影響進(jìn)行了研究。結(jié)果表明:由于引入了集合信息,集合變分混合同化的分析增量與天氣系統(tǒng)的分布有關(guān),具有非均勻、各向異性的特征;這種流依賴特征對(duì)混合系數(shù)敏感,當(dāng)集合協(xié)方差所占權(quán)重很小時(shí),分析增量仍呈現(xiàn)出均勻、各向同性特征;混合同化背景誤差協(xié)方差的流依賴特征不僅與集合樣本有關(guān),還與構(gòu)造集合協(xié)方差的ETKF方法有關(guān),只引入與環(huán)流形勢(shì)密切相關(guān)的集合樣本并不能使分析增量表現(xiàn)出顯著的流依賴性,集合樣本和ETKF方法共同作用才能將流依賴信息引入到混合協(xié)方差中,使分析增量出現(xiàn)流依賴特征;不同集合預(yù)報(bào)方法對(duì)混合協(xié)方差的流依賴特征有顯著影響,考慮初值和物理過(guò)程的超級(jí)集合,以及在超級(jí)集合樣本上再進(jìn)行ETKF更新擾動(dòng)后樣本構(gòu)造的混合協(xié)方差流依賴特征更加顯著。

Abstract:

A single point observation experiment was carried out to evaluate the flow-dependent characteristics, flow-dependent influencing factors and occurring reasons of the background error covariance in hybrid variational-ensemble data assimilation, moreover, the effects of the ensemble forecast method on the flow-dependent characteristics were also analyzed. Results show that due to the introduction of ensemble information, the analysis increments of hybrid variational-ensemble data assimilation are related to weather systems distribution and characterized by the inhomogeneity and anisotropy. The flow-dependent characteristics are sensitive to hybrid coefficients, so when the ensemble-derived covariance belongs to small weight, the analysis increments are still homogeneous and isotropic. The flow-dependent characteristics of background error covariance in hybrid variational-ensemble data assimilation is not only related to the ensemble samples, but to the Ensemble Transform Kalman Filter(ETKF) method used to generate ensemble error covariance. So the combined effects of both ensemble samples and ETKF method can introduce flow-dependent information into hybrid background error covariance and then the analysis increments depend on flow characteristics. Different ensemble forecast methods have different effects on flow-dependent characteristics of hybrid background error covariance. It can be concluded that the flow-dependent characteristics are more significant, when the initial value and multi-physics process in the super-ensemble samples are taken into account and the super-ensemble samples are updated by ETKF.

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張明陽(yáng),張立鳳,張斌,張曉慧.集合變分混合同化背景誤差協(xié)方差流依賴性分析.氣象科學(xué),2015,35(6):728-736 ZHANG Mingyang, ZHANG Lifeng, ZHANG Bin, ZHANG Xiaohui. Flow-dependent characteristics of background error covariance in hybrid variational-ensemble data assimilation. Journal of the Meteorological Sciences,2015,35(6):728-736

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收稿日期:2015-05-15
最后修改日期:2015-08-24
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