Thomas10a

C. Thomas, T. Corpetti, E. Mémin. Data assimilation for convective cells tracking on meteorological image sequences. IEEE Trans. on Geoscience and Remote sensing, 2010.

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Abstract

This paper focuses on the tracking and analysis of convective clouds systems from Meteosat Second Generation images. The highly deformable nature of convective clouds, the complexity of the physical processes involved, but also the partially hidden measurements available from image data make difficult a direct use of conventional image analysis techniques for tasks of detection, tracking and characterization. In this paper, we face these issues using variational data assimilation tools. Such techniques enable to perform the estimation of an unknown state function according to a given dynamical model and to noisy and incomplete measurements. The system state we are setting in this study for the clouds representation is composed of two nested curves corresponding to the exterior frontiers of the clouds and to the interior coldest parts (core) of the convective clouds. Since no reliable simple dynamical model exists for such phenomena at the image grid scale, the dynamics on which we are relying has been directly defined from image based motion measurements and takes into account an uncertainty modeling of the curves dynamics along time. In addition to this assimilation technique, we show in appendix how each cell of the recovered clouds system can be labeled and associated to characteristic parameters (birth or death time, mean temperature, velocity, growth, etc.) of great interest for meteorlogists.

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Etienne Mémin

BibTex Reference

@article{Thomas10a,
   Author = {Thomas, C. and Corpetti, T. and Mémin, E.},
   Title = {Data assimilation for convective cells tracking on meteorological image sequences},
   Journal = {IEEE Trans. on Geoscience and Remote sensing},
   Year = {2010}
}

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