Optimizing the utility of satellite altimetry for diagnosing lateral stirring and mixing in the upper ocean

该项目将评估从卫星测高产品推断出的混合诊断模型误差的重要性，制定改善其影响的策略，并扩大对混合措施之间理论关系的理解。混合诊断对噪声和时空采样分辨率的敏感性将使用高分辨率原始方程海洋模型来确定。本文将建立一种拉格朗日随机参数化方案，并将其与卫星测高计算的观测流相结合。混合诊断参数将在粒子集合上平均，以提供统计上有意义的平均值和置信区间。数据同化方法将被用来动态估计基于连续观测流场的随机参数。最后，研究将利用分析技术、简单的运动流和理想的地球物理湍流模型，发展混合诊断之间以及诊断与大尺度流特征之间的相互关系。该项目旨在优化卫星测高产品的效用，并开发一种广泛记录的算法，该算法可用于任何给定的网格化测高衍生速度时间序列。这项研究的结果将提高对海洋混合的理解，以及我们模拟海洋物质运输的能力，并将其应用于广泛的海洋人类活动。

The project will assess the significance of model error for mixing diagnostics deduced from satellite altimetry products, develop strategies for ameliorating its impact, and expand understanding of the theoretical relationships between measures of mixing. The sensitivity of mixing diagnostics to noise and spatial and temporal sampling resolution will be determined using high resolution primitive equation ocean models. A Lagrangian stochastic parameterization scheme for the unresolved flow will be developed and coupled to the observed flow calculated from satellite altimetry. Mixing diagnostic parameters will be averaged over ensembles of particles to provide statistically meaningful averages and confidence intervals. Data assimilation methods will be exploited to dynamically estimate stochastic parameters based upon successive observations of the flow field. Finally, the research will develop interrelationships among mixing diagnostics and between diagnostics and large-scale features of the flow using analytical techniques, simple kinematic flows, and idealized geophysical turbulence models.The project aims to optimize the utility of satellite altimetry products and develop an extensively documented algorithm that can be utilized with any given gridded altimetry-derived velocity time-series. The results of this research will improve understanding of mixing in the ocean and our ability to model the transport of ocean-borne material with applications to a wide range of marine-based human activities.