A local ensemble transform Kalman particle filter for convective-scale data assimilation

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 that incorporates information coming from the physical model with the latest observations. High-resolution numerical weather prediction models run at operational centres are able to resolve nonlinear 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 article, 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 updates is also proposed. The new algorithm is implemented in the Consortium for Small-scale Modeling (COSMO) framework and numerical experiments in a quasi-operational convective-scale set-up are conducted. The results show the feasibility of the new algorithm in practice and indicate the strong potential of such local hybrid methods, in particular for forecasting non-Gaussian variables such as wind and hourly precipitation.