Drifters deployed in close proximity collectively provide a unique observational data set with which to separate mesoscale and submesoscale flows. In this paper we provide a principled approach for doing so by fitting observed velocities to a local Taylor expansion of the velocity flow field. We demonstrate how to estimate mesoscale and submesoscale quantities that evolve slowly over time, as well as their associated statistical uncertainty. We show that in practice the mesoscale component of our model can explain much first and second-moment variability in drifter velocities, especially at low frequencies. This results in much lower and more meaningful measures of submesoscale diffusivity, which would otherwise be contaminated by unresolved mesoscale flow. We quantify these effects theoretically via computing Lagrangian frequency spectra, and demonstrate the usefulness of our methodology through simulations as well as with real observations from the LatMix deployment of drifters. The outcome of this method is a full Lagrangian decomposition of each drifter trajectory into three components that represent the background, mesoscale, and submesoscale flow.
翻译:在近距离部署的漂流器集体提供了一个独特的观测数据集,用以区分中尺度和亚流流。在本文件中,我们提供了一种有原则的方法,将观察到的速率与当地速度流场的泰勒扩展相匹配,从而做到这一点。我们展示了如何估计随着时间的推移缓慢变化的中尺度和亚流规模及其相关的统计不确定性。我们表明,在实践中,我们模型的中尺度部分可以解释漂流器速度,特别是低频率流速的流流体的高度和第二移动变化性。这导致亚流体流体流的亚流体异性测量值低得多,而且更有意义,否则会受到未解决的中尺度流的污染。我们通过计算Lagrangeian频率光谱从理论上量化这些效应,并通过模拟以及从LatMix部署的漂流器的实际观测来证明我们方法的效用。这种方法的结果是将每个漂流体轨迹完全分解成代表背景、中尺度和亚流体流体流的三个组成部分。