Multiscale Stuart-Landau Emulators: Application to Wind-Driven Ocean Gyres
Multiscale Stuart-Landau Emulators: Application to Wind-Driven Ocean Gyres
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DOI：
10.3390/fluids3010021
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发表时间：
2018-03-01
期刊：
影响因子：
1.9
通讯作者：
Berloff, Pavel
中科院分区：
文献类型：
--
作者：
Kondrashov, Dmitri;Chekroun, Mickael D.;Berloff, Pavel
由于海洋环流的非线性动力学所引起的多尺度变率仍然是理论认识和实际海洋模拟的一大挑战。本文展示了（Chekroun和Kondrashov， （2017）， Chaos, 27）中引入的数据自适应谐波（DAH）分解和逆随机建模技术如何能够高保真地再现粗粒度涡旋分辨洋流中多尺度变异性的主要统计特性。这种完全数据驱动的方法依赖于提取频率排序的时间相关系数，这些系数描述了海流数据中时空DAH模式（DAHMs）的演变。反过来，这些系数的时间序列可以通过一组低阶随机微分方程（SDEs）有效地建模，这些方程包含一组固定的预测函数和少量的模型系数。这些sde采用随机振荡器的形式，被确定为多层斯图尔特-朗道模型（MSLMs），它们的使用是通过依赖于ruelle - policott共振理论来证明的。由此产生的DAH-MSLM仿真器显示了良好的建模技巧，证明了使用随机振荡器网络模拟地球物理湍流的可行性。从某种意义上说，原始的准周期朗道湍流观，在修正了随机性之后，可以很好地描述湍流。
The multiscale variability of the ocean circulation due to its nonlinear dynamics remains a big challenge for theoretical understanding and practical ocean modeling. This paper demonstrates how the data-adaptive harmonic (DAH) decomposition and inverse stochastic modeling techniques introduced in (Chekroun and Kondrashov, (2017), Chaos, 27), allow for reproducing with high fidelity the main statistical properties of multiscale variability in a coarse-grained eddy-resolving ocean flow. This fully-data-driven approach relies on extraction of frequency-ranked time-dependent coefficients describing the evolution of spatio-temporal DAH modes (DAHMs) in the oceanic flow data. In turn, the time series of these coefficients are efficiently modeled by a family of low-order stochastic differential equations (SDEs) stacked per frequency, involving a fixed set of predictor functions and a small number of model coefficients. These SDEs take the form of stochastic oscillators, identified as multilayer Stuart-Landau models (MSLMs), and their use is justified by relying on the theory of Ruelle-Pollicott resonances. The good modeling skills shown by the resulting DAH-MSLM emulators demonstrates the feasibility of using a network of stochastic oscillators for the modeling of geophysical turbulence. In a certain sense, the original quasiperiodic Landau view of turbulence, with the amendment of the inclusion of stochasticity, may be well suited to describe turbulence.