Using Model Evaluation Tools (METplus) to Evaluate Process Related Precipitation Skill and Biases in the NOAA Seasonal Forecast System (SFS) over North America to Improve Climate Prediction Center (CPC) Operational Seasonal Forecasts

Integration of the NOAA Unified Forecast System (UFS) Seasonal Forecast System (SFS) into the seasonal research and forecasting communities, including University of Miami and the Climate Prediction Center (CPC) relies on assessment of skill and biases of precipitation over North America in both hindcasts and realtime forecasts. The National Center for Atmospheric Research (NCAR)’s enhanced Model Evaluation Tools (METplus) verification framework is intended to be used to verify the UFS, and is currently being onboarded for operational use at CPC due to its large library of verification metrics and community support approach. A currently funded collaborative effort between NCAR and CPC shows that METplus requires more development to seamlessly integrate with seasonal climate data, such as UFS-SFS and the North American Multi- Model Ensemble (NMME) (part a). Moreover, CPC seasonal forecasters rely on the state of primary climate drivers to forecast seasonal precipitation, and information on these drivers is imperative to the seasonal climate research and modeling communities. Thus, the assessment of the impact of El Niño Southern Oscillation (ENSO), decadal trends, etc. on North American precipitation variability is key to diagnosing the utility of any dynamical models used in seasonal forecasting and research. Though ENSO plays a key role in precipitation variability, other climate drivers should also be considered. For example, key internal forcing mechanisms such as the Pacific Decadal Oscillation (PDO) and its impact on North American precipitation in seasonal forecast systems must be assessed, as well as the representation of in-situ drivers such as soil moisture and snow cover (part b). Collaboratively, we will create a verification framework utilizing METplus to allow streamlined assessment of probabilistic seasonal precipitation forecast skill, including hindcast and conditional skill related to the above key drivers within the UFS-SFS. An additional goal will be that it can be easily expanded to any climate model ensemble. The development, documentation, and demonstration of these process-based model capabilities will provide valuable feedback to the UFS model development team and community, with the potential to improve the key modes of variability that impact seasonal precipitation forecasts.