  Ensemble data assimilation methods such as the Ensemble Kalman Filter (EnKF)
are a key component of probabilistic weather forecasting. They represent the
uncertainty in the initial conditions by an ensemble which incorporates
information coming from the physical model with the latest observations.
High-resolution numerical weather prediction models ran at operational centers
are able to resolve non-linear and non-Gaussian physical phenomena such as
convection. There is therefore a growing need to develop ensemble assimilation
algorithms able to deal with non-Gaussianity while staying computationally
feasible. In the present paper we address some of these needs by proposing a
new hybrid algorithm based on the Ensemble Kalman Particle Filter. It is fully
formulated in ensemble space and uses a deterministic scheme such that it has
the ensemble transform Kalman filter (ETKF) instead of the stochastic EnKF as a
limiting case. A new criterion for choosing the proportion of particle filter
and ETKF update is also proposed. The new algorithm is implemented in the COSMO
framework and numerical experiments in a quasi-operational convective-scale
setup are conducted. The results show the feasibility of the new algorithm in
practice and indicate a strong potential for such local hybrid methods, in
particular for forecasting non-Gaussian variables such as wind and hourly
precipitation.
