Learning multi-tracer convolutional models for the reconstruction of high-resolution SSH fields

This paper addresses the reconstruction of high-resolution Sea Surface Height (SSH) from the synergy between along-track altimeter data, OI-interpolated SSH fields and satellite-derived high-resolution Sea Surface Temperature (SST) fields. We aim at better resolving the fine-scale range, typically below 100km, which remains scarcely resolved by operational optimal interpolation schemes. The proposed scheme relies on multi-tracer convolutional models and on their calibration from the observed along-track data. We explore a dictionary-based decomposition of the convolutional models to improve the robustness of the calibration. We report a numerical evaluation using an Observation Simulation System Experiment (OSSE) for a case study region in the western Mediterranean sea. Our numerical experiments demonstrate that we can improve reconstruction performance by about 20%, in terms of mean square error, compared to optimally-interpolated fields. Dictionary-based decompositions also resort to similar potential improvement. We further analyze different parameterizations of the convolution models in relation to physical priors (e.g., SGQ dynamics, isotropical transfer functions).