非線性集合四維變分同化方法NLS-4DVar之局地化改進
發(fā)布時間：2019-03-11 20:24
【摘要】：四維變分同化可利用同化窗口內(nèi)所有可能的觀測信息優(yōu)化大氣、海洋模式的初始場,從而極大地提高大氣、海洋模式模擬性能,而作為4DVar標(biāo)準(zhǔn)算法的伴隨方法始終無法避免繁瑣與復(fù)雜的預(yù)報模式伴隨方程的編程、維護以及更新。為避免伴隨模式的使用,集合四維變分方法,4DEnVar方法被逐漸開發(fā),為4DVar的求解提供了一種便捷的途徑。4DEnVar一般通過局地化過程消除樣本不足所造成的虛假相關(guān),而局地化方案的不同也必然會影響到其最終的同化效果。本文將一種集合樣本擴展的局地化方案引入到基于Gaussian-Newton迭代算法的非線性集合四維變分同化方法NLS-4DVar中,從而避免了原算法中為進行局地化過程而額外需要的線性化假設(shè),使得算法收斂更穩(wěn)定。另外,通過將原Gaussian-Newton迭代序列進行變形、避免了矩陣的直接求逆,極大地提高了同化算法的計算效率。利用非線性動力模型Lorenz-96所開展的觀測系統(tǒng)模擬試驗表明:采用新的樣本擴展型局地化方案的NLS-4DVar算法,其同化精度略優(yōu)于NLS-4DVar原始算法,由于避免了矩陣的直接求逆,其計算效率反而有所提高,同化所需時間有所降低,對于大氣與海洋數(shù)據(jù)同化領(lǐng)域的應(yīng)用具有極大的潛力。
[Abstract]:Four-dimensional variational assimilation can optimize the initial field of atmospheric and ocean models by using all possible observations in the assimilation window, thus greatly improving the simulation performance of atmospheric and ocean models. As a standard algorithm of 4DVar, the adjoint method can not avoid the complicated and complicated adjoint equation programming, maintenance and updating. In order to avoid the use of the adjoint model, the set four-dimensional variational method and the 4DEnVar method have been developed gradually, which provides a convenient way to solve 4DVar. 4DEnVar usually eliminates the false correlation caused by the lack of samples through the localization process. And the difference of localization scheme will certainly affect its final assimilation effect. In this paper, a set sample extended localization scheme is introduced into the nonlinear set four dimensional variational assimilation method (NLS-4DVar) based on the Gaussian-Newton iterative algorithm, thus avoiding the linearization assumption that is needed for the localization process in the original algorithm. The convergence of the algorithm is more stable. In addition, by deforming the original Gaussian-Newton iterative sequence, the direct inversion of the matrix is avoided, and the computational efficiency of the assimilation algorithm is greatly improved. The observation system simulation experiments carried out by using the nonlinear dynamic model Lorenz-96 show that the assimilation accuracy of the NLS-4DVar algorithm using the new sample extended localization scheme is slightly better than that of the original NLS-4DVar algorithm, and the direct inversion of the matrix is avoided. On the contrary, the computational efficiency is improved and the time required for assimilation is reduced, which has great potential for the application of atmospheric and ocean data assimilation.
【作者單位】： 山東農(nóng)業(yè)大學(xué);中國科學(xué)院大氣物理研究所國際氣候與環(huán)境科學(xué)中心;
【基金】：國家高技術(shù)研究發(fā)展計劃項目(2013AA122002) 國家自然科學(xué)基金項目(41575100,91437220) 山東省省級水利科研與技術(shù)推廣項目(SDSLKY201503)資助~~
【分類號】：P714
，
本文編號：2438592
[Abstract]:Four-dimensional variational assimilation can optimize the initial field of atmospheric and ocean models by using all possible observations in the assimilation window, thus greatly improving the simulation performance of atmospheric and ocean models. As a standard algorithm of 4DVar, the adjoint method can not avoid the complicated and complicated adjoint equation programming, maintenance and updating. In order to avoid the use of the adjoint model, the set four-dimensional variational method and the 4DEnVar method have been developed gradually, which provides a convenient way to solve 4DVar. 4DEnVar usually eliminates the false correlation caused by the lack of samples through the localization process. And the difference of localization scheme will certainly affect its final assimilation effect. In this paper, a set sample extended localization scheme is introduced into the nonlinear set four dimensional variational assimilation method (NLS-4DVar) based on the Gaussian-Newton iterative algorithm, thus avoiding the linearization assumption that is needed for the localization process in the original algorithm. The convergence of the algorithm is more stable. In addition, by deforming the original Gaussian-Newton iterative sequence, the direct inversion of the matrix is avoided, and the computational efficiency of the assimilation algorithm is greatly improved. The observation system simulation experiments carried out by using the nonlinear dynamic model Lorenz-96 show that the assimilation accuracy of the NLS-4DVar algorithm using the new sample extended localization scheme is slightly better than that of the original NLS-4DVar algorithm, and the direct inversion of the matrix is avoided. On the contrary, the computational efficiency is improved and the time required for assimilation is reduced, which has great potential for the application of atmospheric and ocean data assimilation.
【作者單位】： 山東農(nóng)業(yè)大學(xué);中國科學(xué)院大氣物理研究所國際氣候與環(huán)境科學(xué)中心;
【基金】：國家高技術(shù)研究發(fā)展計劃項目(2013AA122002) 國家自然科學(xué)基金項目(41575100,91437220) 山東省省級水利科研與技術(shù)推廣項目(SDSLKY201503)資助~~
【分類號】：P714
，
本文編號：2438592
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