Enhancing Forecasts of Tropical Cyclone Extratropical Transition By Statistical Pattern Recognition

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The project builds on previous work which developed a statistical pattern recognition technique called Projection Pursuit (PP) that can be used to predict the extratropical transition (ET) of tropical cyclones based on large-scale characteristics of the surrounding atmosphere that can be gleaned from global analyses, model forecasts, and satellite observations. Normally tropical storms weaken as they leave the warm waters of the subtropics and turn poleward. ET leads to a reintensification of the storm causing it to remain dangerous. The investigators plan to generalize and refine the PP technique, taking it from proof-of-concept to an experimental forecasting tool applicable for real-time forecasting. While the project has a substantial applied aspect, the investigators also aim to understand the underlying physical mechanisms that separate the two categories of storms (ET versus dissipating) embodied by a combination of the storm characteristics and those of the larger synoptic environment, as well as differentiate between various rates of intensification. The ultimate goal is an automated system that can identify likely candidates for ET from model and remote sensing information. The methodology will have application for hurricane and typhoon forecasting and would be applicable in the T-PARC field campaign. Two graduate students and one undergraduate will be supported.