Towards the assimilation of dual-polarization radar data

The forecast accuracy of numerical weather prediction models is strongly determined by the precision of the initial conditions, especially for storm and convective-scale weather prediction. Since radars allow to capture the internal structure and important microphysical and dynamical processes in convective systems, they are crucial instrument for improvement of weather forecasts on these scales. Dual-polarization radar, in contrast to a prevalent single-polarization radar, also provides information on the types and sizes of hydrometeor particles. As a result, polarimetric radar data (PRD) proves to be a valuable data source for data assimilation. However, direct assimilation of PRD is not used in current operational non-hydrostatic convection-permitting numerical models. This is associated with several difficulties, such as model error estimation, which require further study.

The current focus of our study is to directly assimilate PRD in an idealized setup. For that purpose, observation system simulation experiments (OSSEs) were performed that simulate the development of a long-lived supercell using the ICON model with two-moment microphysics scheme. In OSSE, the Kilometer-scale Ensemble Data Assimilation (KENDA) system, which comprises the Local Ensemble Transform Kalman Filter (LETKF) was used. The new polarimetric radar forward operator EMVORADO-POL developed at Deutscher Wetterdienst (DWD) was incorporated in the setup. The first steps towards the direct assimilation of differential reflectivity, in addition to non-polarimetric variables, have been implemented and will be presented. Proper thresholds and model equivalents of polarimetric data were examined. Results were compared to reference experiments assimilating non-polarimetric variables such as reflectivity and radial velocity.