Each year, tropical cyclones present a variety of societal and environmental risks to both marine and coastal regions around the world. Accurately quantifying tropical cyclone hazards is therefore important for saving lives, developing more resilient infrastructure, and minimizing economic losses. To better prepare for tropical cyclone hazards, it is vital to seek the accurate prediction of a tropical cyclone’s formation, its track, the storm’s surface winds, the accompanying storm surge, and its capacity for devastating rainfall.

Substantial progress in the understanding and prediction of tropical cyclones has been achieved in recent decades thanks to significant advances in the scientific understanding of tropical cyclones, high-resolution numerical weather prediction models, and computational resources.  Five-day forecasts of track have particularly improved as global models that resolve the large-scale steering flow have gotten better, but there is still room for track error reduction and providing reliable estimates of forecast uncertainty. On the other hand, certain tropical cyclone prediction problems, such as accurately predicting the one- to seven-day prediction of a storm’s intensity, remains far more challenging and therefore has seen slower advancements. This is because tropical cyclones are multi-scale phenomena strongly tied to the large-scale environment surrounding a storm, the storm’s inner-core dynamics, including convective,  turbulent, and microphysical processes, and the storm’s interactions with the ocean and land surface beneath them.

NSAP conducts scientific research on a wide variety of tropical cyclone problems and the scientists here work in the realms of theory, observations, models, and operational forecast applications. Recent and current research by NSAP scientists falls into following focus areas:

• Satellite-based methods for the better understanding and prediction of tropical cyclone phenomena, including rapid intensification and concentric eyewalls,
• Data assimilation and advanced initialization methods in tropical cyclone models, 
• Numerical weather prediction model post-processing techniques to improve forecasts of tropical cyclone track, intensity, and wind structure,
• Statistical and machine learning methods for predicting tropical cyclone intensity change,
• The understanding of tropical cyclone wind impacts on urban and built-up areas for land-falling hurricanes through the use of high-resolution numerical models and large-eddy simulations, and
• Theoretical and modeling studies on tropical cyclone inner-core dynamics.