Development of Static Background Error Covariance Capability for the Rapid Refresh Forecast System (RRFS)

The Rapid Refresh Forecast System (RRFS) is a next-generation regional ensemble forecast system developed by NOAA in collaboration with the broader Unified Forecast System (UFS) community. RRFS conducts hourly deterministic forecast covering the North American continent with 3 km horizontal grids. RRFS Data Assimilation System (RDAS) uses hybrid 3D EnVar (Ensemble-Variational) analysis method, in which background error covariance (BEC) is obtained by mixing static BEC and ensemble-based BEC derived from Ensemble Kalman Filter (EnKF).

Although the analysis system of RDAS will be replaced from the Gridpoint Statistical Interpolation (GSI) to the Joint Effort for Data assimilation Integration (JEDI) in the second version of RRFS (RRFSv2), the static BEC modeling capability in JEDI in the context of regional analysis is currently limited. Therefore, the objective of this development is to incorporate the GSI’s recursive filter method into System-Agnostic Background Error Representation (SABER) for RRFSv2. The preliminary results of regional 3DVar analysis using the GSI ‘s recursive filter method in SABER will be introduced in the conference.