Data assimilation with state alignment using the EnKF

Sequential assimilation methods allow tracking physical states using dynamic priors and external observations of the studied system. However, when dense image satellite observations are available, such approaches realize a correction of the amplitude of the different state values but do not incorporate the spatial errors of structure positions. In the case of the position of a vortex, for example, when there is misfit between state and observation, the processes can be long to converge and even diverge when high dimensional state spaces are treated with few iterations of the assimilation methods as it is the case in operational algorithms. In this paper, we tackle this issue by decomposing the state of the system to displacement and amplitude varibales. We then propose an alignment method based on object detection methods that uses visual correspondences between the physical state model and the structural information given by a sequence of images observing the phenomena. The ensemble Kalman filter (EnKF) is modified in order to perform sequentially the alignment followed by the amplitude correction. The experimental evaluation of the method using both simulated and real data shows accurate structure detection and a faster convergence rate of the assimilation method.