On data-driven induction of the low-frequency variability in a coarse-resolution ocean model
On data-driven induction of the low-frequency variability in a coarse-resolution ocean model
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粗分辨率海洋模型中低频变化的数据驱动归纳
DOI：
10.1016/j.ocemod.2020.101664
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发表时间：
2020
期刊：
影响因子：
3.2
通讯作者：
Berloff, P.
中科院分区：
文献类型：
--
作者：
Ryzhov, E.A.;Kondrashov, D.;Agarwal, N.;McWilliams, J.C.;Berloff, P.
本研究在资料驱动的中尺度海洋涡旋参数化方面取得了进展。为了证明这一概念，并揭示伴随的警告，我们的目的是取代计算昂贵的，标准的高分辨率海洋模型与其廉价的低分辨率模拟增强的参数化。我们考虑了涡分辨和非涡分辨的双环流模式，其特征在于由于非线性中尺度涡效应而具有截然不同的解。该方法的关键是从高分辨率参考解中提取涡场随时间和空间变化的信息，然后利用这些信息改进低分辨率模拟模式，通过交互耦合涡场的连续历史和显式模拟的低分辨率大尺度流场，我们得到了附加的涡动强迫项，它修正了低分辨率模式并显著地扩充了它的解。该涡强迫项代表了涡场的作用，它与大尺度流动耦合，是对增强过程施加的关键动力学约束。尽管增强大大改善了低分辨率环流型，但它没有恢复鲁棒的、内在的、大尺度低频变率（LFV），这是高分辨率解的一个重要特征。这本身就是一个重要的（负面的）结果，对任何数据驱动的涡旋参数化都有重要意义，特别是考虑到我们使用了关于涡旋场时空历史的最完整信息。注意，当我们提供参考（真实）涡动强迫，而不仅仅是涡动场时，LFV被恢复。这表明LFV在很大程度上依赖于时空涡动强迫/大尺度流动相关的细节，而所提出的增强方法并没有完全考虑这些细节。我们通过将增强的低分辨率模型解投影到高分辨率模型的大尺度分量的领先经验正交函数（EOF）上，分离度参比溶液。这种操作使我们能够消除与更高EOF相关的伪效应。我们测试并证实，如果不使用数据驱动的涡流信息，这种滤波单独不能增强低分辨率的解决方案;但结合涡动信息，它产生了理想的结果。此外，作为迈向参数化的自然步骤，我们利用数据驱动的随机逆建模来获得涡流场的廉价仿真器，并显示出增强粗-分辨率模型与所获得的仿真器。我们的研究结果表明，获得LFV特性的涡流参数化，这已经能够再现大规模的流态，应该成为一个标准的参数化要求，但它可以是具有挑战性的，以满足。
This study makes progress towards a data-driven parameterization for mesoscale oceanic eddies. To demonstrate the concept and reveal accompanying caveats, we aimed at replacing a computationally expensive, standard high-resolution ocean model with its inexpensive low-resolution analogue augmented by the parameterization. We considered eddy-resolving and non-eddy-resolving double-gyre ocean circulation models characterized by drastically different solutions due to the nonlinear mesoscale eddy effects. The key step of the proposed approach is to extract from the high-resolution reference solution its eddy field varying in space and time, and then to use this information to improve the low-resolution analogue model.By interactively coupling both the continuously supplied history of the eddy field and the explicitly modeled low-resolution large-scale flow, we obtained the additional eddy forcing term which modified the low-resolution model and significantly augmented its solutions. This eddy forcing term represents the action of the eddy field, its coupling with the large-scale flow and is a key dynamical constraint imposed on the augmentation procedure.Although the augmentation drastically improved the low-resolution circulation patterns, it did not recover the robust, intrinsic, large-scale low-frequency variability (LFV), which is an important feature of the high-resolution solution. This is by itself an important (negative) result that has significant implication for any data-driven eddy parameterization, especially, given the fact that we used the most complete information about the space–time history of the eddy fields. Note, when we supplied the reference (true) eddy forcing, rather than just the eddy field, the LFV was recovered. This suggests that the LFV is crucially dependent on the details of the space–time eddy forcing/large-scale flow correlations, which are not fully respected by the proposed augmentation procedure.In order to overcome the deficiency and recover the LFV, we statistically filtered the augmented low-resolution model solution by projecting it onto the leading Empirical Orthogonal Functions (EOFs) of the large-scale component of the high-resolution reference solution. This operation allowed us to remove spurious effects associated with higher EOFs. We tested and confirmed that without using the data-driven eddy information this filtering alone cannot augment the low-resolution solution; but in conjunction with the eddy information, it produced desirable outcome.Moreover, as a natural step towards parameterization, we took advantage of data-driven stochastic inverse modeling to obtain inexpensive emulators of the eddy field and showed generally promising results of augmenting the coarse-resolution model with the obtained emulators. Our results showed that obtaining the LFV characteristics for the eddy parameterization, which is already capable of reproducing the large-scale flow pattern, should become a standard parameterization requirement, but it can be challenging to meet.
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DOI：
10.1007/978-3-319-58895-7_10
发表时间：
2018
期刊：
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DOI：
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发表时间：
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期刊：
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