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首页> 外文期刊>International journal of multiscale computational engineering >Inverse Shallow-Water Flow Modeling Using Model Reduction
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Inverse Shallow-Water Flow Modeling Using Model Reduction

机译:使用模型约简的浅水逆流建模

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The idea presented in this paper is variational data assimilation based on model reduction using proper orthogonal decomposition. An ensemble of forward model simulations is used to determine the approximation of the covariance matrix of the model variability, and only the dominant eigenvectors of this matrix are used to define a model subspace. An approximate linear reduced model is obtained by projecting the original model onto this reduced subspace. Compared to the classical variational method, the adjoint of the tangent linear model is replaced by the adjoint of a linear reduced forward model. Thus, it does not require the implementation of the adjoint of the tangent linear model. The minimization process is carried out in reduced subspace and hence reduces the computational cost. Twin experiments using an operational storm surge prediction model in the Netherlands, the Dutch Continental Shelf Model are performed to estimate the water depth, with the findings that the approach with relatively little computational cost and without the burden of implementation of the adjoint model can be used in variational data assimilation.
机译:本文提出的思想是基于使用适当正交分解的模型约简的变异数据同化。正向模型仿真的集合用于确定模型变异性的协方差矩阵的近似值,并且仅此矩阵的主要特征向量用于定义模型子空间。通过将原始模型投影到该缩小的子空间上,可以获得近似的线性缩小的模型。与经典变分方法相比,切线线性模型的伴随被线性简化正向模型的伴随所代替。因此,它不需要实现切线模型的伴随。最小化过程在减少的子空间中进行,因此减少了计算成本。在荷兰进行了两次使用操作性风暴潮预测模型(荷兰大陆架模型)的实验,以估算水深,结果发现,可以使用计算成本相对较低且无伴随模型实施负担的方法在变异数据同化中

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