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Nov. 5, 2012

After years of devastating storms along the U.S. coast, the National Science Foundation has awarded a team of researchers a three-year, $550,000 grant to develop more predictive computer models of the ocean, including the impact of storm surge during a hurricane.

The award is shared by a team of investigators from CSU, the University of Notre Dame and the University of Texas at Austin as part of a new NSF program, called Computational and Data-Enabled Science and Engineering in Mathematical and Statistical Sciences.

Even before Superstorm Sandy, a series of events during the past seven years has driven a demand in improving scientists’ ability to predict hurricane impact. Namely, Hurricane Katrina (2005), in devastating fashion, demonstrated the perils of underestimating the vulnerability of coastal communities to storm surge. Following on the heels of Katrina were hurricanes Rita (2005), Gustav (2008) and Ike (2008), which all caused tremendous damage to communities along the northern Gulf of Mexico.

“These events have spurred a serious and sustained effort to improve the ability to predict coastal ocean conditions,” said Don Estep, professor in the CSU Departments of Statistics and Mathematics and CSU principal investigator. “However, the prediction of coastal conditions beyond what can be observed – for example, predicting future maximum storm surge from current and near past coastal observation data in real-time - is an exceedingly challenging mathematical, statistical and computational problem.”

Advanced computer models that capture the complex processes of the coastal ocean can be used to predict the impact of storm surge as hurricanes approach landfall.

Two significant problems face predictions of complex phenomena such as storm surge, Estep said. First, such predictions require calibration of input data for the models to observable quantities through solution of an "inverse" problem that is extremely difficult and computationally expensive to solve. Second, the predictions are affected by many sources of error and uncertainty.

The project, titled “Data-Driven Inverse Sensitivity Analysis for Predictive Coastal Ocean Modeling,” aims to tackle both problems through an interdisciplinary approach. Notre Dame and UT Austin team members are providing expertise in physical and computational modeling of storm surge while the CSU team of Estep, who is a University Interdisciplinary Research Scholar, and Troy Butler in the Department of Statistics are providing new mathematical and statistical theory to undertake solution of the calibration inverse problem and quantifying uncertainty in model predictions.

The computational methodology and tools developed under this project are applicable to other problems in coastal engineering, marine science, material science and other engineering disciplines. Technology transfer of the mathematical and numerical methodologies developed under this project will occur with the coastal ocean modeling community, and with agencies such as the U.S. Army Corps of Engineers, NOAA, the Department of Homeland Security, state and local agencies, industry and other universities in the United States and abroad.

The NSF program accepts proposals that confront and embrace the host of mathematical and statistical challenges presented to the scientific and engineering communities by the ever-expanding role of computational modeling and simulation on the one hand, and the explosion in production of digital and observational data on the other. The goal of the program is to promote the creation and development of the next generation of mathematical and statistical theories and tools that will be essential for addressing such issues. This program is part of the wider Computational and Data-enabled Science and Engineering enterprise at NSF.

Contact: Emily Wilmsen

E-mail: Emily.Wilmsen@colostate.edu

Phone: (970) 491-2336

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