  Complex systems are often described with competing models. Such divergence of
interpretation on the system may stem from model fidelity, mathematical
simplicity, and more generally, our limited knowledge of the underlying
processes. Meanwhile, available but limited observations of system state could
further complicates one's prediction choices. Over the years, data assimilation
techniques, such as the Kalman filter, have become essential tools for improved
system estimation by incorporating both models forecast and measurement; but
its potential to mitigate the impacts of aforementioned model-form uncertainty
has yet to be developed. Based on an earlier study of Multi-model Kalman
filter, we propose a novel framework to assimilate multiple models with
observation data for nonlinear systems, using extended Kalman filter, ensemble
Kalman filter and particle filter, respectively. Through numerical examples of
subsurface flow, we demonstrate that the new assimilation framework provides an
effective and improved forecast of system behaviour.
