Weighted Ensemble Transform Kalman Filter for Image Assimilation

This paper proposes an extension of the Weighted Ensemble Kalman filter (WEnKF) proposed by Papadakis et al. (2010) for the assimilation of image observations. The main contribution of this paper consists in a novel formulation of the Weighted filter with the Ensemble Transform Kalman filter (WETKF) incorporating directly as a measurement model a nonlinear image reconstruction criterion. This technique has been compared to the original WEnKF on numerical and real world data of 2D turbulence observed through the transport of a passive scalar. It has been in particular applied for the reconstruction of oceanic surface current vorticity fields from Sea Surface Temperature satellite data. This latter technique enables a consistent recovery of oceanic surface currents, vorticity maps along time in presence of large missing data areas and strong noise.