  Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used
for data assimilation in large-scale geophysical applications, as for example
in numerical weather prediction (NWP). There is a growing interest for physical
models with higher and higher resolution, which brings new challenges for data
assimilation techniques because of the presence of non-linear and non-Gaussian
features that are not adequately treated by the EnKF. We propose two new
localized algorithms based on the Ensemble Kalman Particle Filter (EnKPF), a
hybrid method combining the EnKF and the Particle Filter (PF) in a way that
maintains scalability and sample diversity. Localization is a key element of
the success of EnKFs in practice, but it is much more challenging to apply to
PFs. The algorithms that we introduce in the present paper provide a compromise
between the EnKF and the PF while avoiding some of the problems of localization
for pure PFs. Numerical experiments with a simplified model of cumulus
convection based on a modified shallow water equation show that the proposed
algorithms perform better than the local EnKF. In particular, the PF nature of
the method allows to capture non-Gaussian characteristics of the estimated
fields such as the location of wet and dry areas.
