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首页> 外文期刊>SIAM Journal on Numerical Analysis >UNIFORM-IN-TIME ERROR ESTIMATES FOR THE POSTPROCESSING GALERKIN METHOD APPLIED TO A DATA ASSIMILATION ALGORITHM
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UNIFORM-IN-TIME ERROR ESTIMATES FOR THE POSTPROCESSING GALERKIN METHOD APPLIED TO A DATA ASSIMILATION ALGORITHM

机译:应用于数据同化算法的后处理Galerkin方法的均匀误差估计

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摘要

We apply the postprocessing Galerkin method to a recently introduced continuous data assimilation (downscaling) algorithm for obtaining a numerical approximation of the solution of the two-dimensional Navier Stokes equations corresponding to given measurements from a coarse spatial mesh. Under suitable conditions on the relaxation (nudging) parameter, the resolution of the coarse spatial mesh, and the resolution of the numerical scheme, we obtain uniform-in-time estimates for the error between the numerical approximation given by the postprocessing Galerkin method and the reference solution corresponding to the measurements. Our results are valid for a large class of interpolant operators, including low Fourier modes and local averages over finite volume elements. Notably, we use here the two-dimensional Navier-Stokes equations as a paradigm, but our results apply equally to other evolution equations, such as the Boussinesq system of Benard convection and other oceanic and atmospheric circulation models.
机译:我们将后处理Galerkin方法应用于最近引入的连续数据同化(缩小)算法,用于获得与来自粗糙空间网格的给定测量相对应的二维Navier Stokes方程的解决方案的数值近似。在松弛(亮亮)参数的合适条件下,粗糙空间网格的分辨率和数值方案的分辨率,我们获得了后处理Galerkin方法和该方法所给出的数值近似之间的误差均匀估计。对应于测量的参考解决方案。我们的结果对于大量的Interpolant运算符有效,包括低傅里叶模式和在有限卷元素上的本地平均值。值得注意的是,我们在这里使用二维Navier-Stokes方程作为范例,但我们的结果同样适用于其他演化方程,例如Benard对流和其他海洋和大气循环模型的Boussinesq系统。

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