Weighted ensemble transform Kalman filter for image assimilation

• Main Authors: Sebastien Beyou, Anne Cuzol, Sai Subrahmanyam Gorthi, Etienne Mémin
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2013-01-01
• Series: Tellus: Series A, Dynamic Meteorology and Oceanography
• Subjects: image data assimilation; particle filters; ensemble filters; SST satellite images
• Online Access: http://www.tellusa.net/index.php/tellusa/article/view/18803/pdf_2

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