Weighted ensemble transform Kalman filter for image assimilation

Sebastien Beyou; Anne Cuzol; Sai Subrahmanyam Gorthi; Etienne Mémin

doi:10.3402/tellusa.v65i0.18803

Weighted ensemble transform Kalman filter for image assimilation

Tellus

Volume 65 (2013): Issue 1

By: Sebastien Beyou, Anne Cuzol, Sai Subrahmanyam Gorthi and Etienne Mémin

Open Access

|Dec 2013

Abstract

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.

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Articles in this issue

DOI: https://doi.org/10.3402/tellusa.v65i0.18803 | Journal eISSN: 3035-9554

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Language: English

Page range: 18803 - 18803

Submitted on: May 16, 2012

Accepted on: Nov 6, 2012

Published on: Dec 1, 2013

Published by: Stockholm University Press

In partnership with: Paradigm Publishing Services

Keywords:

image data assimilation,

particle filters,

ensemble filters,

SST satellite images

© 2013 Sebastien Beyou, Anne Cuzol, Sai Subrahmanyam Gorthi, Etienne Mémin, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 65 (2013): Issue 1