在双层准地转模型中利用随机输运噪声模拟不确定性

IF 1.7 Q2 MATHEMATICS, APPLIED
C. Cotter, D. Crisan, Darryl D. Holm, Wei Pan, I. Shevchenko
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引用次数: 47

摘要

Holm(Proc Roy Soc A,2015)引入了地球物理流体动力学的随机变分方法,作为导出未解决尺度的随机参数化的框架。本文将变分随机参数化应用于一个双层准地转模型中,该模型适用于一个平面通道流结构。我们提出了一种新的方法来估计随机强迫(用于参数化),以使用来自高分辨率确定性模拟的数据来近似未解决的分量,并描述了计算随机模型物理一致初始条件的过程。我们还量化了均匀流(与小尺度涡流结合)和非均匀流(具有水平伸长的大尺度射流)中粗网格模拟相对于细网格模拟的不确定性,并分析了随机解的传播如何取决于模型的不同参数。通过将参数化与达到某种统计平衡的真实涡流解析解和低分辨率网格上建模的确定性解进行比较来测试参数化。结果表明,所提出的参数化在很大程度上取决于随机模型的分辨率,并且对于均质流和非均质流都具有良好的集成性能,并且参数化为数据同化奠定了坚实的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling uncertainty using stochastic transport noise in a 2-layer quasi-geostrophic model
The stochastic variational approach for geophysical fluid dynamics was introduced by Holm (Proc Roy Soc A, 2015) as a framework for deriving stochastic parameterisations for unresolved scales. This paper applies the variational stochastic parameterisation in a two-layer quasi-geostrophic model for a \begin{document}$ \beta $\end{document} -plane channel flow configuration. We present a new method for estimating the stochastic forcing (used in the parameterisation) to approximate unresolved components using data from the high resolution deterministic simulation, and describe a procedure for computing physically-consistent initial conditions for the stochastic model. We also quantify uncertainty of coarse grid simulations relative to the fine grid ones in homogeneous (teamed with small-scale vortices) and heterogeneous (featuring horizontally elongated large-scale jets) flows, and analyse how the spread of stochastic solutions depends on different parameters of the model. The parameterisation is tested by comparing it with the true eddy-resolving solution that has reached some statistical equilibrium and the deterministic solution modelled on a low-resolution grid. The results show that the proposed parameterisation significantly depends on the resolution of the stochastic model and gives good ensemble performance for both homogeneous and heterogeneous flows, and the parameterisation lays solid foundations for data assimilation.
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CiteScore
3.30
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0.00%
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