Geophysical turbulence has a wide range of spatiotemporal scales that requires a multiscale prediction model for efficient and fast simulations. Stochastic parameterization is a class of multiscale methods that approximates the large-scale behaviors of the turbulent system without relying on scale separation. In the stochastic parameterization of unresolved subgrid-scale dynamics, there are several modeling parameters to be determined by tuning or fitting to data. We propose a strategy to estimate the modeling parameters in the stochastic parameterization of geostrophic turbulent systems. The main idea of the proposed approach is to generate data in a spatiotemporally local domain and use physical/statistical information to estimate the modeling parameters. In particular, we focus on the estimation of modeling parameters in the stochastic superparameterization, a variant of the stochastic parameterization framework, for an idealized model of synoptic scale turbulence in the atmosphere and oceans. The test regimes considered in this study include strong and moderate turbulence with complicated patterns of waves, jets, and vortices.

中文翻译：

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两层拟营养液流随机超参数化中的参数估计

地球物理湍流具有广泛的时空尺度，因此需要多尺度预测模型才能进行有效而快速的模拟。随机参数化是一类多尺度方法，该方法无需依赖尺度分离即可近似湍流系统的大规模行为。在未解析的子网格规模动力学的随机参数化中，有几个建模参数需要通过调整或拟合数据来确定。我们提出了一种策略来估计地转湍流系统的随机参数化中的建模参数。提出的方法的主要思想是在时空局部域中生成数据，并使用物理/统计信息来估计建模参数。尤其是，对于大气和海洋中天气尺度湍流的理想化模型，我们专注于随机超参数化（随机参数化框架的一种变体）中建模参数的估计。本研究中考虑的测试方案包括强湍流和中度湍流，以及复杂的波浪，射流和涡流模式。