Seasonal Predictability of Weather Type Frequencies Over the Contiguous United States

Skillful seasonal predictions of large-scale weather patterns that are associated with precipitation anomalies offer a potential opportunity for stakeholders across societal sectors. Using data-driven approaches and physical understanding, we investigate the seasonal predictability of large-scale weather type frequencies associated with precipitation for the contiguous United States. A generalized weather type clustering algorithm is applied to seasonal hindcast products: the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Seasonal-to-Multiyear Large Ensemble (SMYLE) experiment from NCAR’s CESM2. Hindcasts are verified by comparing the observed and expected frequencies per season using the power-divergence statistic. Further, to understand contributing processes, we investigate how well the large-scale patterns associated with weather types are represented. Finally, we will discuss pathways towards making these S2S forecasts useful and usable to stakeholders.