Current data assimilation methods still face problems in strongly nonlinear cases. A
promising solution is a particle filter, which provides a representation of the state
probability density function (pdf) by a discrete set of particles. To allow a particle filter
to work in high-dimensional systems, the proposal density freedom is explored.We used
a proposal density from synchronisation theory, in which one tries to synchronise the
model with the true evolution of a system using one-way coupling, via the observations.
This is done by adding an extra term to the model equations that will control the
growth of instabilities transversal to the synchronisation manifold. In this paper, an
efficient ensemble-based synchronisation scheme is used as a proposal density in the
implicit equal-weights particle filter, a particle filter that avoids filter degeneracy by
construction. Tests using the Lorenz96 model for a 1000-dimensional system show
successful results, where particles efficiently follow the truth, both for observed and
unobserved variables. These first test show that the new method is comparable to
and slightly outperforms a well-tuned Local Ensemble Transform Kalman Filter. This
methodology is a promising solution for high-dimensional nonlinear problems in the
geosciences, such as numerical weather prediction