Properties of advection algorithms in the context of variational data assimilation
Properties of advection algorithms in the context of variational data assimilation
复制标题
变分数据同化背景下平流算法的特性
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
M. Hecht
中科院分区：
文献类型：
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作者：
T. Vukicevic;M. Steyskal;M. Hecht
研究了数值平流算法特性对变分同化结果的影响。非线性和线性平流算法在二维理想标量平流框架中进行了测试，其中真实解是已知的。数据同化后最优解的精度与正流和伴随平流模式数值近似的精度呈正相关。在使用非线性平流算法的线性化版本的实验中，最优解的精度明显较小。这个性质是优化收敛到成本函数的局部最小值的结果。实验中采用原有的非线性平流算法求解伴随方程，避免了局部极小值的出现。本文的结果建议在正演和伴随计算中应用完全相同的标量平流算法，以便以较低的成本获得与正演模型精度一致的最优解精度。
The influence of numerical advection algorithm properties on variational data assimilation results are investigated. Nonlinear and linear advection algorithms are tested in a 2D idealized scalar advection framework in which the true solution was known. The accuracy of the optimal solutions after the data assimilation was positively correlated with the accuracy of numerical approximations used in both the forward and adjoint advection models. The accuracy of the optimal solutions was significantly smaller in the experiments in which linearized versions of the nonlinear advection algorithm were used. This property was the consequence of the optimization convergence to a local minimum in the cost function. The local minimum was avoided in the experiments in which the adjoint equation was solved by the original nonlinear advection algorithm. The results presented here suggest application of the exact same scalar advection algorithm in forward and adjoint computations in order to obtain, at lower cost, an optimal solution accuracy that is consistent with the forward model accuracy.