Implementation and Testing of Radar Data Assimilation Capabilities Within the Joint Effort for Data Assimilation Integration Framework With Ensemble Transformation Kalman Filter Coupled With FV3-LAM Model

Capabilities to directly assimilate radar data are implemented within the local ensemble transform Kalman filter (LETKF) and the gain-form LETKF (LGETKF) algorithms of the Joint Effort for Data assimilation Integration (JEDI) system. The capabilities are evaluated for the analysis and forecast of a severe convection case of 20 May 2019 in the Southern Great Plains using the limited area model version of the FV3 dynamical core (FV3-LAM) from a recent release for Short-Range Weather Application (SRW App). The LETKF and LGETKF implementations are shown to produce analyses and short-range forecasts comparable to those using the ensemble square-root Kalman Filter (EnSRF) within the Gridpoint Statistical Interpolation (GSI) framework used by current NCEP operational models. In addition, LGETKF retaining only 60% variances for model-space vertical localization performs similarly to LGETKF retaining 99% of variance and LETKF using observation error-based vertical localization. JEDI LETKF shows better parallel scalability than LGETKF and GSI EnSRF.