Tropical cyclones are one of the biggest natural threats to society, causing substantial economic damage and loss of life annually. Accurate and reliable seasonal predictions of tropical cyclone activity are essential for disaster preparedness, but remain challenging for climate scientists. In a new MAPP-supported study, Manganello et al.
indicates that the potential for high-resolution coupled (atmosphere-ocean) modeling to improve seasonal forecasting of tropical cyclone activity may be greater than previously believed.

Predictions at the seasonal to sub-seasonal scale are important for planning and decision-making in a variety of disciplines, and improving understanding and model skill at this timescale is a key research priority. An as yet underexplored approach to sub-seasonal prediction using data science and graph theory methods that are increasingly common to other fields outside of meteorology and climate science shows potential to improve predictions at this challenging timescale.

NOAA’s Modeling, Analysis, Predictions, and Projections (MAPP) program has competitively chosen nine new one-year projects involving $545,427 in grants and $82,000 in other awards (for a total of $627,427) to evaluate and develop new applications for the North American Multi-Model Ensemble System (NMME), a state-of-the-art multi­-model seasonal prediction system currently in the process of transitioning to National Weather Service operations.