[10-7]

Assimilating 3D radar reflectivity with an Ensemble Kalman Filter on a convection- permitting scale

T. Bick (HErZ, Meteorological Institute, University of Bonn), R. Potthast (Deutscher Wetterdienst), C. Simmer (Meteorological Institute, University of Bonn), K. Stephan (Deutscher Wetterdienst), S. Troemel (HErZ, Meteorological Institute, University of Bonn), K. Wapler (HErZ, Deutscher Wetterdienst)

Due to the atmosphere's chaotic and nonlinear behavior, predicting small-scale convective events is a challenging task and data assimilation is crucial to enhance numerical weather prediction (NWP).

Radar observations represent and ideal database because of their ability to capture the 3D spatial and temporal evolution of convective systems. Applying the EMRADSCOPE radar forward operator developed at the German Weather Service (DWD) and the Karlsruhe Institute of Technology (KIT), we simulate both radar reflectivity and no-reflectivity based on the output of the convection-permitting NWP model COSMO-DE.

For our study we assimilate 3D radar measurements from the DWD C-band radar network within the Local Ensemble Transform Kalman Filter (LETKF) framework implemented at DWD. First results indicate that structure and location of precipitation cells are in good agreement with the observation and that assimilating no-reflectivity helps to suppress spurious convection. Since the assimilation step has a significant influence on the stability and physical consistency of the atmospheric model state, we study the impact of different assimilation update frequencies (ranging from 5 to 60 minutes) on both the quality of the analysis and the forecast skill. The radar reflectivity assimilation experiment is compared to a control ensemble without assimilation of radar observations.