Abstract. We describe the status of the assimilation of bending angles from GPS radio occultations in the 3D-Var for DWD's operational global forecast model GME ("Global Model for Europe"). Experiments show that the assimilation of GPSRO data leads to a significant reduction of biases in the analyses of temperature, humidity and wind in the upper troposphere and the stratosphere, as well as a better r. m. s. fit in the comparison to radiosondes. The impact on forecasts is most prominent in the data sparse Southern Hemisphere, but is also quite notable in the Northern Hemisphere extratropics. The positive results found in the impact experiments lead to the implementation of the assimilation of GPS radio occultations from GRACE-A, FORMOSAT-3/COSMIC and GRAS/MetOp-A into the operational suite on 3 August 2010. We also show some initial results from assimilation experiments using radio occultation data from the German research satellite TerraSAR-X.

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We describe the status of the assimilation of bending angles from GPS radio occultations in the 3D-Var for DWD's operational global forecast model GME ("Global Model for Europe"). Experiments show that the assimilation of GPSRO data leads to a significant reduction of biases in the analyses of temperature, humidity and wind in the upper troposphere and the stratosphere, as well as a better r. m. s. fit in the comparison to radiosondes. The impact on forecasts is most prominent in the data sparse Southern Hemisphere, but is also quite notable in the Northern Hemisphere extra-tropics, where we also see a slightly positive impact on surface pressure. The positive results found in the impact experiments lead to the implementation of the assimilation of GPS radio occultations from GRACE-A, FORMOSAT-3/COSMIC and GRAS/MetOp-A into the operational suite on 3 August 2010. We also show some initial results from assimilation experiments using radio occultation data from the German research satellite TerraSAR-X

<p>The German Weather Service (DWD) operationally runs an LETKF (Localized Ensemble Kalman Filter) assimilation scheme for the regional weather forecasts with the ICON-LAM (ICON Limited Area Mode) Numerical Weather Prediction model. We investigate the potential of using an EnVAR (Ensemble Variational data assimilation) using the kilometre-scale Ensemble Data Assimilation (KENDA) ensemble. Quality Control (QC) and Observation Aggregation (OA) are essential parts of a data assimilation system. The former ensures that the assimilated observations are likely to be "acceptable", in the sense of technical, physical and statistical properties. The latter reduces the amount of data and computations under the aspect of efficiency, and helps handling redundant or correlated observations.</p><p>We show results of assimilation experiments for KENDA and EnVAR using a similar selection of conventional observations after QC and OA, while using a fully dynamic B matrix and no variational QC. The difference of the results of the two algorithms does not only depend on the partially differing implementation of QC and OA, but also due to partially different implementations of the observation operators or even the supported observation types. Important differences to the operational global EnVAR code are e.g. the choice of suitable observation types and the interpolation specification of the first guess to the locations of the observations.</p><p>As we use the same code for the EnVAR as in the DWD's global data assimilation scheme, we can potentially assimilate many other observations systems beyond conventional observations. This includes, after some adaptations, a wide range of spaceborne observations. Additionally, it is possible to run a regional EnVAR assimilation and a deterministic forecast with a coarse resolution first guess ensemble. Re-using existing ensembles for the ensemble B matrix might be a computationally efficient way to use a variational algorithm for deterministic forecasts.</p>