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

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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.