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Communication Dans Un Congrès Année : 2017

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

Résumé

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).
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Dates et versions

hal-01757751 , version 1 (03-04-2018)

Identifiants

Citer

Ronan Fablet, Manuel Lopez Radcenco, Jacques Verron, Baptiste Mourre, Bertrand Chapron, et al.. Learning multi-tracer convolutional models for the reconstruction of high-resolution SSH fields. IGARSS 2017 : IEEE Geoscience And Remote Sensing Symposium, Jul 2017, Fort Wroth, United States. ⟨10.1109/IGARSS.2017.8126981⟩. ⟨hal-01757751⟩
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