Researchers predict power outages caused by Hurricane Matthew
Steven Quiring

Steven Quiring

An interdisciplinary team of researchers is using a unique model to predict how many U.S. residents will lose power because of Hurricane Matthew.

As of Thursday morning, Oct. 6, the team predicted 9.6 million people will lose electricity.

The model was developed by researchers from The Ohio State University, University of Michigan and Texas A&M University.

“Based on current estimates of its track and strength, Hurricane Matthew is forecast to have one of the larger impacts on the power grid that we have seen,” said TDA affiliate Steven Quiring, professor of atmospheric sciences at Ohio State.

Regularly updated power outage forecasts for Hurricane Matthew can be found on the team’s website: http://ioe-guikema.engin.umich.edu/Hurricane_Matthew.html

The model looks at a variety of factors at the census tract level to determine who is likely to lose power. Among those factors is land use, such as whether the area is urban and whether it has a lot of trees.

Of course, the model examines average wind speeds and duration, as well as the intensity of the strongest winds. But it also uses data that hadn’t previously been used for these kinds of predictions, such as soil moisture. “Saturated soils affect the stability of both trees and the power infrastructure,” Quiring said.

The team, which includes Seth Guikema at the University of Michigan and Brent McRoberts at Texas A&M, have been using the model to predict hurricane-caused power outages for about a decade.

They correctly estimated that superstorm Sandy would knock out power for about 10 million people in 2012. “When we know the track and the intensity of the storm, our forecasts are pretty accurate,” Quiring said.

The U.S. Department of Energy and the Department of Homeland Security, among other agencies, use the team’s forecasts to help plan responses to hurricanes.

Share this page
Suggested Articles
Wang to use NASA Early Career Award in Earth Science to accurately quantify Greenland ice mass change

TDAI affiliate Lei Wang, assistant professor of civil, environmental, and geodetic engineering, uses satellite data to understand phenomena that is at once too great and too subtle to accurately study...

Key accomplishments: TDA's 2016 Status Report

Assembling a dynamic community of faculty affiliates is one of Translational Data Analytics’ greatest achievements since its launch in 2014. TDA’s 2016 Status Report illustrates other notable progress toward convening...

Kui Xie awarded TDA seed grant

TDA congratulates Kui Xie, PhD, who received the first-ever TDA@OhioState seed grant for $24,970 for his proposal Using Learning Analytics in Examining Students’ Learning Habits in Online Nursing Classes. Xie...

New research examines interplay between microbes

By Misti Crane Below the surface of systems as large and ancient as an ocean and as small and new as a human baby are communities of viruses and bacteria...

From Theory to Practice: Generating Value from Big Data

The past several years have seen a growing focus on big data, data science, and data analytics that spans a spectrum—from fundamental issues related to the management of large-scale and...