The Role of Convective-Scale Static Background Error Covariance in RRFS Hybrid EnVar for Direct Radar Reflectivity Data Assimilation over the CONUS

Wang and Wang (2021) has shown that the utilization of a convective-scale static background error covariance (BEC) matrix in the hybrid ensemble–variational (EnVar) can improve the convective-scale analysis and prediction for the WRF-ARW. In this study, the convective-scale static BEC for the FV3 limited-area model (FV3-LAM) is further developed. In contrast to Wang and Wang (2021) that includes all cross-variable correlations, this study selects and maintains the most critical cross-variable correlations for convective scales to decrease minimization cost without degrading the analysis and short-term forecast performance. The new BEC matrix developed for the FV3-LAM is employed to directly assimilate radar reflectivity within the GSI-based three-dimensional (3DVar) and EnVar frameworks, emulating the future operational Rapid Refresh Forecast System (RRFS). The role of the static BEC is explored for a case study of severe convective storm systems over the High Plains on 26–27 May 2021.

Experiments using the full static BECs (3DVAR), the full ensemble BECs (ENVAR), and the blended BECs with a static/ensemble covariance weight of 30%/70% (HYBRID) are conducted. Detailed comparisons and evaluations indicate that ENVAR produces less spurious weak reflectivity over the Northern Plains than 3DVAR. Although 3DVAR is much cheaper than ENVAR, it outperforms ENVAR in adding the observed storm in Kansas. Compared to ENVAR, HYBRID improves the analyses and forecasts by maintaining the advantages from both ENVAR and 3DVAR. However, HYBRID only partially suppresses the spurious reflectivity compared to 3DVAR. To further improve the hybridization, the static and ensemble covariances are adaptively blended depending on the ensemble quality (HYBRID_CR). In HYBRID_CR, the spurious weak reflectivity is suppressed, and improved forecast skills are maintained. Developments on additional adaptive weighting methods for convective-scale data assimilation are ongoing and will be presented at the meeting.