Advanced methods for the inference of dynamic information applied to remote sensing maps of the ocean

Abstract

The continuous arrival of new platforms for the acquisition of remote‐sensing data of the oceans has led to an increased interest in exploiting this huge source of information in order to derive as much information as possible about the sea state. Nowadays, a common strategy is to directly assimilate the retrieved oceanic variables (Sea Surface Temperature, Salinity or Height) in numerical models of the ocean, so that the complete sea state can be inferred at the resolution of the numerical model. However, remote sensing maps can be directly exploited to derive further dynamic information: classical examples include the Maximum Cross Correlation method, based on the tracking of patterns across a sequence of maps and then inferring surface velocities at a coarse time‐space resolution, and also more modern methodologies such as Surface Quasi‐Geostropy. In this presentation, we will discuss on a different approach, that of the Microcanonical Multifractal Formalism (MMF). MMF is a theory developed to deal with variables in turbulent flows, and is based on a technique known as Singularity Analysis (SA) used to retrieve significant dynamic features (as streamlines) from snapshots of scalar variables. We will illustrate the application of SA on real and simulated data; this application gives direct access to the patterns of ocean circulation at very different resolutions with unprecedented quality. Furthermore, MMF allows to assess the quality of different parametrizations of numerical models and of climatological variables. Finally, we will discuss the connections between our Eulerian singularity patterns and other Lagrangian quantities such as Finite Size Lyapunov Exponents