R2O: Recent Development of Convective Scale Radar Data Assimilation and Ensemble Forecasting Capability to Improve NWS Operational Hazardous Weather Forecasts

In the current US NWS operational convection allowing forecast systemsHRRR and NAM-CONUS, critical convective scale observations such as radar reflectivity areassimilated indirectly using a separate approach called cloud analysis /diabatic digital filter. The University of Oklahoma (OU) team in collaboration with NCEP/EMC and ESRL/GSD further developed the GSI based EnKF /hybrid DA system to directly assimilate radar reflectivity. Extensive retrospective experiments for the full CONUS domain have found that the newly extended GSI hybrid DA system with the advanced radar DA method outperforms the system using the cloud analysis/DFI. This DA system was tested in real time during both the 2017 and 2018 NOAA HWT. To further improve the system, the static covariance is further extended for convective scale radar data assimilation and included in the GSI EnVar. Recent experiments have found the newly developed static covariance for radar data assimilation further improved the subsequent forecast. With the advancement of the convective scale data assimilation, it lays the foundation to further advance the design of convection allowing ensemble forecast. Experiments were further conducted in HRRR context to address how to optimally perturb initial condition errors in a multi-scale scenario and the impact of using a single dynamic core multi-physics approach vs multi-dynamic core approach to sample model errors.