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.
