Radar reflectivity and radial velocity assimilation in a hybrid ETKF‐3DVAR system for prediction of a heavy convective rainfall

Abstract

AbstractReliable forecasting of heavy convective rainfall events continues to be one of the greatest challenges for numerical weather prediction models. Studies across the globe have indicated that Doppler weather radar (DWR) data assimilation through three‐dimensional variational methods (3D‐Var) has a considerable impact on improving the forecast skill of convective precipitation. However, the time‐averaged background error statistics (BES) employed in 3D‐Var fail to address the day‐to‐day variability of forecast errors in BES. To effectively represent the convective precipitation events through DWR assimilation, it is important to account for the day‐to‐day variability in background error's variance and correlations in the BES. In this study, the effect of assimilating DWR data using flow‐dependent BES within the variational assimilation system is analysed via the hybrid Ensemble Transform Kalman Filter (ETKF)‐3DVAR. For this, the functionality of ETKF in the hybrid ETKF‐3DVAR has been extended to assimilate both DWR radial velocity and reflectivity information. The results suggest that assimilating DWR observations through the hybrid ETKF‐3DVAR improves the forecast skills of convective precipitation as compared to the 3D‐Var. It has also been observed that updating the ensemble states through assimilation of DWR observations in the ETKF system shows an increased wind and moisture convergence.