Geophysical flows under location uncertainty, Part II Quasi-geostrophy and efficient ensemble spreading
Geophysical flows under location uncertainty, Part II Quasi-geostrophy and efficient ensemble spreading
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DOI：
10.1080/03091929.2017.1312101
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发表时间：
2017-01-01
影响因子：
1.3
通讯作者：
Chapron, B.
中科院分区：
文献类型：
--
作者：
Resseguier, V.;Memin, E.;Chapron, B.
假设速度的一个分量在时间上不相关，推导出位置不确定性下的模型。相应地修改材料导数以包括平流校正、非均匀和各向异性扩散项以及乘性噪声贡献。在本文中，简化的地球物理动力学是根据位置不确定性下的 Boussinesq 模型得出的。调用通常的尺度近似和子网格项的适度影响，获得了分层准地转模型和地表准地转模型的随机公式。基于数值模拟，证明了所提出的随机形式主义的优点。位置不确定性下模型的单一实现可以恢复小尺度结构。一组实现进一步有助于评估模型误差预测，并且比受扰动的确定性模型高一个数量级。如此高的不确定性量化技能是同化集成方法的主要兴趣。 MATLAB (R) 代码示例可在线获取。
Models under location uncertainty are derived assuming that a component of the velocity is uncorrelated in time. The material derivative is accordingly modified to include an advection correction, inhomogeneous and anisotropic diffusion terms and a multiplicative noise contribution. In this paper, simplified geophysical dynamics are derived from a Boussinesq model under location uncertainty. Invoking usual scaling approximations and a moderate influence of the subgrid terms, stochastic formulations are obtained for the stratified Quasi-Geostrophy and the Surface Quasi-Geostrophy models. Based on numerical simulations, benefits of the proposed stochastic formalism are demonstrated. A single realization of models under location uncertainty can restore small-scale structures. An ensemble of realizations further helps to assess model error prediction and outperforms perturbed deterministic models by one order of magnitude. Such a high uncertainty quantification skill is of primary interests for assimilation ensemble methods. MATLAB (R) code examples are available online.