Regime-dependent skill
Understanding how forecast skill varies with large-scale or local flow conditions.
The Predictability Task Team focuses on forecast skill, uncertainty, and the sources of predictability in impact-relevant sub-seasonal to seasonal forecasts. Its work helps identify when, where, and under what conditions forecast information can support decision-making.
Understanding how forecast skill varies with large-scale or local flow conditions.
Quantifying uncertainty so that it can be communicated and used in decision-making.
Supporting analysis through extended sub-seasonal reforecasts and experiments.
Developing post-processing approaches that account for regime-dependent skill.
S2S forecast skill can arise from multiple interacting drivers, including large-scale modes of climate variability, weather regimes, and local flow characteristics. Understanding these sources is essential for interpreting when forecasts are likely to be informative.
Forecast skill and uncertainty can vary across different regimes. Quantifying these variations can help users understand the confidence that should be placed in forecast information under different conditions.
The team considers both traditional dynamical forecasting systems and newer data-driven methods, including AI and machine-learning approaches for sub-seasonal timescales.
Better understanding of forecast skill and uncertainty can inform calibration and post-processing methods, helping forecasts become more relevant for decision contexts.
The Predictability Task Team provides scientific understanding and methods that can support the application-sector Task Teams. Its work helps connect forecast reliability, uncertainty, and calibration with real-world decision needs.
Understanding when forecasts are more or less skilful helps identify where forecast information can be used responsibly in applications.
Quantifying uncertainty supports clearer communication of forecast confidence and helps users incorporate probabilistic information into decisions.
Predictability research can support agriculture, health, disaster risk reduction, energy, and other application areas by improving how skill, uncertainty, and calibration are handled.