Data assimilation and parameter estimation for a multiscale stochastic system withα-stable Lévy noise

This work is about low dimensional reduction for a slow-fast data assimilation system with non-Gaussian stable Levy noise via stochastic averaging. When the observations are only available for slow components, we show that the averaged, low dimensional filter approximates the original filter, by examining the corresponding Zakai stochastic partial differential equations. Furthermore, we demonstrate that the low dimensional slow system approximates the slow dynamics of the original system, by examining parameter estimation and most probable paths.