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Characterizing Atlantic Tropical Cyclone Genesis Using Special PREDICT Measurements and Satellite Observations/Products

$375,001FY2009GEONSF

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The proposed PRE-Depression Investigation of Cloud-systems in the Tropics (PREDICT) is a focused observational field campaign to investigate both the structure and evolution of tropospheric wave-like disturbances in the tropics and sub-tropics and the subsynoptic- and mesoscale processes operating within the waves that contribute to the formation of tropical depressions. The formation of tropical cyclones remains one of the great, unsolved problems in meteorology. It inherently requires study of multiple scales of atmospheric motion from cloud particles to synoptic-scale waves. PREDICT is distinguished from previous efforts related to tropical cyclone formation by (a) new dynamical hypotheses to be tested; (b) nearly continuous aircraft observations; (c) the region of study; (d) advanced sensors and facilities. Its intellectual merit is that it will test hypotheses concerning tropical cyclone genesis using this unique data set. The contribution by the team of investigators associated with this specific part of PREDICT will be to provide timely and unique satellite-based products that will help investigators define environmental parameters associated with tropical cyclone genesis. Broader Impacts: While there is evidence that tropical cyclone forecasts have improved in recent years, deterministic genesis prediction is still a very difficult problem. As prediction is required on longer time scales for emergency management and short-term mitigation, prediction of genesis becomes more important. Mechanisms of genesis are crucial to understand in order that processes leading to simulated genesis in weather and climate models can be adequately evaluated. PREDICT will improve our ability to anticipate genesis, by arming forecasters with conceptual tools regarding the process.

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