Unified Forecast System Research to Operations (UFS-R2O) Project Phase II: Accelerating the Transition of UFS Applications into Operations

The UFS-R2O Project has made significant progress coordinating a large community of researchers, both inside and outside NOAA for integrating new research into the operational UFS applications. The project successfully completed its first phase in June 2023 and entered into the second phase with two additional years of funding provided jointly by NOAA’s National Weather Service (NWS) Office of Science and Technology Integration (OSTI) and the Oceanic and Atmospheric Research (OAR) Weather Program Office (WPO) for sustaining and advancing collaborations between the National Centers forEnvironmental Prediction (NCEP) EnvironmentalModelling Center, 8 NOAA research labs, the National Center for Atmospheric Research (NCAR), the Naval Research Lab (NRL) and 6 universities and cooperative institutes.

Transition of the Hurricane Application, the Hurricane Analysis and Forecast System (HAFS), into operations is a major accomplishment of the UFS-R2O project during its first phase. Additionally, the project led the successful development, testing and evaluation of a six-way coupled UFS Medium Range Weather/Subseasonal to Seasonal (MRW/S2S) Application for upgrading the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS), and an advanced Rapid Refresh Forecast System (RRFS) for Convection Allowing Modeling/Short Range Weather (CAM/SRW) Application. Phase 2 of the UFS-R2O Project will continue advancing these three applications (Hurricane, CAM/SRW, and MRW/S2S) and carve out a new Seasonal Forecast Application for dedicated development of the next generation Seasonal Forecast System (SFS) that will replace the legacy Climate Forecast System (CFSv2) in operations.

This presentation will highlight the accomplishments from the first phase of the UFS-R2O Project, and will report on the current progress and future activities during the second phase of the project. Reduction of coupled model biases, improved representation of key modes of variability, development of a coupled data assimilation (DA) system, and Improved quantification of model uncertainty in ensembles are some of the high priority areas of development needed for advancing the forecast capabilities, especially for extreme events.