Data assimilation in multiscale complex systems

This paper develops multi-scale data assimilation/filtering algorithms that are driven by the data, that take advantage of scale interaction to appropriately reduce the dimension of the problem. We incorporate an optimal particle filtering algorithm that generates the best importance sampling density. This particle method consists of control terms in the “prognostic” equations that nudge the particles toward the observations. Finally, we apply the optimal particle filtering algorithm to a lower dimensional chaotic system.