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Conference Papers Year : 2018

Sea Surface Flow Estimation via Ensemble-based Variational Data Assimilation

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Abstract

In this paper, we propose a data assimilation method for consistently estimating the velocity fields from a whole image sequence depicting the evolution of sea surface temperature transported by oceanic surface flow. The estimator is conducted through an ensemble-based variational data assimilation , which is designed by combining the advantages of two approaches: the ensemble Kalman filter and the variational data assimilation. This idea allows us to obtain the optimal initial condition as well as the full system trajectory. In order to extract the velocity fields from fluid images, a surface quasi-geostrophic model representing the generic evolution of the temperature field of the flow, and the optical flow constraint equation derived from the image intensity constancy assumption, are involved in the assimilation context. Numerical experimental evaluation is presented on a synthetic fluid image sequence.
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Dates and versions

hal-01971389 , version 1 (07-01-2019)

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Shengze Cai, Etienne Mémin, Yin Yang, Chao Xu. Sea Surface Flow Estimation via Ensemble-based Variational Data Assimilation. ACC 2018 - Annual American Control Conference, Jun 2018, Milwaukee, WI, United States. pp.3496-3501, ⟨10.23919/ACC.2018.8430804⟩. ⟨hal-01971389⟩
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