Mixing by ocean eddies on scales of 10-50 km plays a key role in biogeochemical processes, frontal dynamics, and tracer transport in the upper ocean. However, our understanding of these scales is limited because of the resolution of available observations. In this talk, I will describe a suite of stochastic filtering strategies for estimating mixing by ocean eddies from “superresolved” satellite observations obtained by combining low-resolution observations with a stochastic parameterization for the unresolved scales. A novel feature of the methods is the use of computationally inexpensive stochastic models to forecast the unresolved scales. The stochastic model parameters are estimated from data obtained from the observations themselves.
The method enhances the effective resolution of satellite observations by exploiting the effect of spatial aliasing and generates an optimal estimate of small scales using standard Bayesian inference. The technique is tested in quasigeostrophic simulations driven by realistic climatological shear and stratification profiles. Two applications are considered: calculating poleward eddy heat flux from satellite altimetry, and estimating the three-dimensional upper ocean velocity field from superresolved sea-surface temperature imagery. In each case, the superresolved satellite observations result in a considerable improvement in estimates of turbulent fluxes compared with the raw observations.
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