Evaluating RRFS Prototypes within an Agile Testing Framework to Help Inform Future Operational Implementation

NOAA is undergoing a major, community-driven effort to unify the NCEP operational model suite under the Unified Forecast System (UFS) umbrella. Under this initiative, a key area of interest is the evolution of legacy operational, convective-allowing systems to a new, unified Finite-Volume Cubed-Sphere (FV3)-based deterministic and ensemble storm-scale system called the Rapid Refresh Forecast System (RRFS). The ongoing transition from the existing NOAA NWP systems to the UFS is a major multi-year undertaking, with the RRFS targeted for initial operational implementation in late 2024. Before operational implementation, the RRFS must undergo rigorous evaluation to ensure performance is at least on-par with existing operational systems—e.g., the North American Mesoscale (NAM) Model, Rapid Refresh (RAP), High-Resolution Rapid Refresh (HRRR), and High-Resolution Ensemble Forecast (HREF). To ensure this is the case, an agile framework is needed for quick yet robust comparisons of verification statistics among the various prototypes of the RRFS as it evolves in the coming months and years.

To assist model developers at NOAA’s Global System Laboratory (GSL) and NCEP’s Environmental Modeling Center (EMC) as well as inform other key stakeholders and entities, significant progress has been made to create an agile framework to compare the performance of new RRFS prototypes against existing operational model datasets using the enhanced Model Evaluation Tools (METplus). This framework is designed to facilitate an accelerated, evidence-based decision-making feedback process and is built within the UFS Short-Range Weather (SRW) Application, allowing for easy comparison of RRFS prototypes with existing operational products using selected case studies and evaluation periods. This presentation will focus on providing an update on the RRFS ensemble performance as it moves towards operational implementation. Results from convective and wintertime periods will be shown, with a focus on ensemble and probabilistic verification.