Data assimilation for convective cells tracking in MSG images

This paper is interested with the tracking and the analysis of convective cells on Meteosat Second Generation images. Due to the highly deformable nature of convective clouds, the noisy measurements obtained with image data and the complexity of the physics that governs such phenomena, the conventional tools issued from computer vision to detect and track usual (rigid) objects are not well adapt. In this paper, we face this problem 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 is composed of two curves (represented with implicit surfaces) corresponding to the the whole cloud and the coldest part (heart) of the convective system. Since no precise and manageable dynamical model exists concerning such phenomena, the dynamics is directly measured from images using some motion estimation techniques devoted to fluid motions.