arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2102.03783physics.flu-dyncs.NAmath.NA
收藏

位置不确定条件下旋转浅水流动的保结构离散方法

Rotating shallow water flow under location uncertainty with a structure-preserving discretization

Rüdiger Brecht, Long Li, Werner Bauer, Etienne Mémin

摘要:

我们介绍了一个物理相关的随机表示的旋转浅水方程。推导主要依赖于一个随机传输原理和分解成一个大规模的组件和噪声项的流体流模型的未解决的流动组件。至于经典的(确定性)系统,这个计划,被称为位置不确定性(LU)下的建模,保存任何实现的全局能量,并提供了可能性,以生成一个合奏的物理相关的随机模拟与模型误差表示和合奏的传播之间的良好的权衡。为了保持数值上的能量守恒功能,我们结合了能量(在空间中)保持离散化的基本确定性模型与近似的随机项是基于标准的有限体积/差分算子。LU推导,建立从非常相同的守恒原理作为通常的地球物理模型,连同提出的数值方案可以直接用于现有的动力核心的全球数值天气预报模式。所提出的框架的能力证明了无粘测试情况下的f-平面和正压不稳定射流的球体。

英文摘要:

We introduce a physically relevant stochastic representation of the rotating shallow water equations. The derivation relies mainly on a stochastic transport principle and on a decomposition of the fluid flow into a large-scale component and a noise term that models the unresolved flow components. As for the classical (deterministic) system, this scheme, referred to as modelling under location uncertainty (LU), conserves the global energy of any realization and provides the possibility to generate an ensemble of physically relevant random simulations with a good trade-off between the model error representation and the ensemble's spread. To maintain numerically the energy conservation feature, we combine an energy (in space) preserving discretization of the underlying deterministic model with approximations of the stochastic terms that are based on standard finite volume/difference operators. The LU derivation, built from the very same conservation principles as the usual geophysical models, together with the numerical scheme proposed can be directly used in existing dynamical cores of global numerical weather prediction models. The capabilities of the proposed framework is demonstrated for an inviscid test case on the f-plane and for a barotropically unstable jet on the sphere.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑