Purdue Climate Change Research Center

Quantifying Predictability in Nonlinear Multiscale Systems with Applications to Tropical Cyclone Prediction

Funded by the National Science Foundation

This project will utilize a new, fundamental, and unified approach to quantify predictability in complex systems involving a vast-range of interacting spatial-temporal scales. Examples of these types of multiscale phenomena include power outages, earth quakes, tsunami, and tropical cyclones (TCs). TCs in particular, are responsible for billions of dollars of damage annually in the United States alone. The work outlined in this project will fundamentally advance our understanding of ensemble prediction, an important forecasting technique, especially for weather and climate (including TC forecasting). It will drastically improve forecast accuracy from weather to climate scales and tremendously reduce computational complexity and data storage. This work will enable, for the first time, objective and model-independent determination of predictability from observational data, and may ultimately help design better evacuation plans for areas prone to tropical cyclones.


Wen-wen Tung, Department of Earth & Atmospheric Sciences

Jianbo Gao, University of Florida

Contact Information

Purdue University
203 S. Martin Jischke Drive
MANN 105
West Lafayette, IN 47907