Exploring image data assimilation in the prospect of high-resolution satellite data

Many recent works show the key importance of studying the ocean at fine scales including the meso- and submesoscales. Satellite observations such as ocean color data provide informations on a wide range of scales but do not directly provide information on ocean dynamics. Satellite altimetry provide informations on the ocean dynamic topography (SSH) but so far with a limited resolution in space and even more, in time. However, in the near future, high-resolution SSH data (e.g. SWOT) will give a vision of the dynamic topography at such fine space resolution. This raises some challenging issues for data assimilation in physical oceanography: develop reliable methodology to assimilate high resolution data, make integrated use of various data sets including biogeochemical data, and even more simply, solve the challenge of handling large amont of data and huge state vectors.

In this work, we propose to consider structured information rather than pointwise data. First, we take an image data assimilation approach in studying the feasibility of inverting tracer observations from Sea Surface Temperature and/or Ocean Color datasets, to improve the description of mesoscale dynamics provided by altimetric observations. Finite Size Lyapunov Exponents are used as an image proxy. The inverse problem is formulated in a Bayesian framework and expressed in terms of a cost function measuring the misfits between the two images. Second, we explore the inversion of SWOT-like high resolution SSH data and more especially the various possible proxies of the actual SSH that could be used to control the ocean circulation at various scales. One focus is made on controlling the subsurface ocean from surface only data. A key point lies in the errors and uncertainties that are associated to SWOT data.