A Hybrid Ensemble Transform Particle Filter for Nonlinear and Spatially Extended Dynamical Systems

Data assimilation is the task of combining evolution models and observational data in order to produce reliable predictions. In this paper, we focus on ensemble-based recursive data assimilation problems. Our main contribution is a hybrid filter that allows one to adaptively blend ensemble Kalman and particle filters. While ensemble Kalman filters are robust and applicable to strongly nonlinear systems even with small and moderate ensemble sizes, particle filters are asymptotically consistent in the large ensemble size limit. We demonstrate numerically that our hybrid approach can improve the performance of both Kalman and particle filters at moderate ensemble sizes. We also show how to implement the concept of localization into a hybrid filter, which is key to its applicability for spatially extended systems.