On Analysis Error Covariances in Variational Data Assimilation
On Analysis Error Covariances in Variational Data Assimilation
复制标题
变分数据同化中的误差协方差分析
DOI：
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发表时间：
2008
影响因子：
3.1
通讯作者：
V. Shutyaev
中科院分区：
文献类型：
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作者：
I. Gejadze;F. L. Dimet;V. Shutyaev
将非线性演化模型的变分数据同化问题表述为求初始条件函数的最优控制问题（分析）。通过输入数据的误差（背景误差和观测误差）推导出分析误差方程。该方程表明，在非线性情况下，分析误差协方差算子可以用包含切线模型约束的辅助数据同化问题的逆Hessian逼近。在求解辅助数据同化问题时，采用拟牛顿BFGS算法构造逆Hessian。开发了一个全非线性集成程序来验证所提出算法的准确性。给出了数值算例。
The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find the initial condition function (analysis). The equation for the analysis error is derived through the errors of the input data (background and observation errors). This equation is used to show that in a nonlinear case the analysis error covariance operator can be approximated by the inverse Hessian of an auxiliary data assimilation problem which involves the tangent linear model constraints. The inverse Hessian is constructed by the quasi-Newton BFGS algorithm when solving the auxiliary data assimilation problem. A fully nonlinear ensemble procedure is developed to verify the accuracy of the proposed algorithm. Numerical examples are presented.