Development and Testing of Ensemble-Variational Data Assimilation Capabilities for Radar Data within JEDI coupled with FV3-LAM Model

Abstract. This study presents the first implementation and evaluation of radar reflectivity data assimilation capabilities within the ensemble three-dimensional variational (En3DVar) data assimilation (DA) system of the Joint Effort for Data assimilation Integration (JEDI) framework. Building on our earlier works that assimilated reflectivity in JEDI LETKF and in GSI En3DVar, this study focuses on the JEDI En3DVar algorithm when coupled with the FV3-LAM model using the Thompson microphysics scheme. The radar reflectivity observation operator is refined by modifying the snow and graupel reflectivity formulations to improve consistency with Thompson microphysics. The new operator notably improves reflectivity analyses at the upper levels and reduces root-mean-square innovations for both reflectivity and radial velocity during the DA cycles. A high-impact convective storm event is used to evaluate the new implementation. DA experiments are conducted using both the JEDI and GSI En3DVar systems, employing identical observation operators and similar configurations. The resulting analyses and short-range forecasts from the two systems are comparable, supporting the validity of the new implementation of JEDI En3DVar for reflectivity and radial velocity assimilation. Additional comparisons with real-time High-Resolution Rapid Refresh (HRRR) and experimental Rapid Refresh Forecast System (RRFS) forecasts are made. The JEDI-based experiment captures the storm structure and placement with accuracy similar to or better than the HRRR and RRFS forecasts. Improvements are especially evident in the depiction of convective cores and stratiform rainbands, where reflectivity intensity and coverage are better aligned with radar observations.