Sea Surface Flow Estimation via Ensemble-based Variational Data Assimilation* - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Reports (Research Report) Year : 2017

Sea Surface Flow Estimation via Ensemble-based Variational Data Assimilation*

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 estima-tor 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. The results indicate good performance and efficiency of the proposed estimator.
Fichier principal
Vignette du fichier
EnVar_SST_Cai_20170918.pdf (1.98 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01589637 , version 1 (18-09-2017)

Identifiers

  • HAL Id : hal-01589637 , version 1

Cite

Shengze Cai, Etienne Mémin, Yin Yang, Chao Xu. Sea Surface Flow Estimation via Ensemble-based Variational Data Assimilation*. [Research Report] Inria Rennes - Bretagne Atlantique; IRMAR, University of Rennes 1. 2017. ⟨hal-01589637⟩
414 View
156 Download

Share

Gmail Facebook X LinkedIn More