Ensemble Transform Kalman Filter (LETKF). In this study, the Efficient Modular VOlume RADar Operator is applied for the assimilation of radar reflectivity data to improve short-term predictions of precipitation. Both deterministic and ensemble forecasts have been carried out. A case-study shows that the assimilation of 3D radar reflectivity data clearly improves precipitation location in the analysis and significantly improves forecasts for lead times up to 4 h, as quantified by the Brier Score and the Continuous Ranked Probability Score. The influence of different update rates on the noise in terms of surface pressure tendencies and on the forecast quality in general is investigated. The results suggest that, while high update rates produce better analyses, forecasts with lead times of above 1 h benefit from less frequent updates. For a period of seven consecutive days, assimilation of radar reflectivity based on the LETKF is compared to that of DWD's current operational radar assimilation scheme based on latent heat nudging (LHN). It is found that the LETKF competes with LHN, although it is still in an experimental phase.

A sequential data assimilation approach (SAM) that incorporates elements of particle filtering with resampling (SIR, Sequential Importance Resampling) is introduced. SAM is applied to the COSMO-DE-EPS, which is an ensemble prediction system for weather forecasting on convection-permitting scales. At the convective scale and beyond, the atmosphere increasingly exhibits non-linear state space evolutions. For an ensemble-based data assimilation system, this requires both an adequate metric that quantifies the distance between the observed atmospheric state and the states simulated by the ensemble members, and a methodology to counteract filter degeneracy, i.e. the collapse of the simulated state space. We, therefore, propose a combination of resampling, which accounts for simulated state space clustering, and nudging. SAM differs from the classical SIR approach mainly in the weighting applied to the ensemble members. By keeping cluster representatives during resampling, the method maintains the potential for non-linear system state development. With three convective case studies, we demonstrate that SAM improves forecast quality compared with the control EPS (EPS without data assimilation) for the first 5-6 h of forecast.

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