Improving NWS Subseasonal-to-Seasonal Forecast with Unified Forecast System: Highlights of Modeling and Analysis Results

Subseasonal-to-seasonal (S2S) prediction supports important decisions in sectors ranging from food security and public health to water resource management and disaster risk mitigation. The National Weather Service (NWS) is accelerating S2S modeling as part of the NOAA's Unified Forecast System (UFS), consisting of six components of the Earth system: the FV3 dynamical core for the atmosphere, MOM6 for the ocean, Noah-MP for the land surface, GOCART for aerosols, CICE6 for sea ice and WW3 for ocean surface waves. The UFS is the basis for the next generation coupled Global Forecast System (GFS v17) for medium-range weather forecast up to 16 days and the Global Ensemble Forecast System (GEFS v13) for sub-seasonal forecasts targeting operational implementation in FY24.

The UFS is being built as a community model on software infrastructure components including the National Unified Operational Prediction Capability (NUOPC), the Common Community Physics Package (CCPP), and the Model Evaluation Toolkit (MET-plus) for verification and validation. The community can contribute to the development of the end-to-end S2S prediction system including process-based diagnostic tools and S2S metrics, understanding of sources of predictability, and development of forecast products for the user needs.

Several prototypes of the UFS coupled model have been built and tested. The UFS coupled model prototype outputs are made available on Amazon Web Services (AWS) cloud storage platform. Researchers interested in the S2S model analysis are invited to evaluate the UFS prototype data. Analysis of the data may include process-based evaluations, diagnostic measures that reveal coupled feedback processes, model biases and S2S forecast skill estimations. The presentation will highlight analysis results from the UFS coupled model and ensemble prototype runs including encouraging improvements in representing physical processes and improved forecast skill.