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- W1528104523 abstract "Values of diffusion coefficients in a numerical weather prediction model must be changed every time when model resolution is increased. In general, the diffusion coefficient should be sufficiently large (the lower limit of the diffusion coefficient) to remove unwanted small-scale noise, but small enough (the upper limit of the diffusion coefficient) to avoid computational instability with a fixed time step. In this study, an objective method is proposed for producing an estimate of diffusion coefficients through a twin data assimilation experiment: the original low-resolution model is used to generate ”observations” and the new high-resolution model is used as an assimilation model, with its diffusion coefficient serving as a control variable. The proposed method is illustrated using a one-dimensional (1-D) shallow water model. A least-square fit of the high-resolution model solution averaged to coarse-resolution model to ”observations” was found to be able to objectively determine an ”optimal” value of the diffusion coefficient for the high-resolution model that not only produces sufficient damping of small-scale noise but also does not create computational instability. It was also noticed that the values of the cost function increase very rapidly when the diffusion coefficient slightly exceeds the upper limit due to the computational instability. Such an abrupt change of the cost function near the upper limit may cause a differential minimization to fail. It was found that a slight decrease (about 20%) of the time step improves significantly the conditioning of the cost function and is able to remove such a difficulty in minimization." @default.
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- W1528104523 date "2009-06-18" @default.
- W1528104523 modified "2023-09-25" @default.
- W1528104523 title "An Objective Method for Estimating Diffusion Coefficient in Numerical Weather Prediction Models" @default.
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