Geographer Develops Go-To Power Outage Forecasts for Hurricane Matthew
Hurricane Matthew could knock out power for approximately 9-10 million people in the U.S., from Miami to the Carolinas, according to power outage forecasts developed by Ohio State’s Steve Quiring, atmospheric sciences professor in the Department of Geography and researchers at the 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 Quiring.
Quiring — along with Seth Guikema, University of Michigan associate professor of industrial and operations engineering, and Brent McRoberts, postdoctoral researcher in environmental sciences at Texas A&M — has been developing power outage forecasts for a decade. They’re the ones that Homeland Security, the Department of Energy and emergency management agencies call when putting together preparation plans for massive outages. Their model accurately predicted that Superstorm Sandy would knock out power for nearly 10 million people in 2012.
To make the forecasts, the team developed a predictive model that begins with the National Hurricane Center's weather forecast and uses data like population density, tree cover and soil moisture levels to calculate the probability of a power outage in a given area. The power outage model also considers the maximum three-second wind gust at each census tract, as well as how long they can expect winds in a particular area to stay above 45 mph.
“When we know the track and the intensity of the storm, our forecasts are pretty accurate,” Quiring said.
Quiring specializes in computational modeling, data analytics, climatology and hydroclimatology. His current research projects are concentrated in two areas: improving our understanding of the land-atmosphere interactions and applying this information to improve drought and seasonal climate predictability and modeling the impact of weather events on power infrastructure.
“Solving these problems requires the application of a variety of data mining, machine learning and data analytics approaches,” explained Quiring.
As Hurricane Matthew barrels up the Florida coast, the team is sharing detailed models with utility companies along the eastern seaboard to help them deploy repair crews and other resources before, during and after the storm. The forecasts are likely to change quickly as the storm progresses, and will be updated frequently on the team's website.
“We want to make sure that people know where the power might fail, how long it’s going to be out so they can prepare and make decisions accordingly,” said Quiring. “Our job is to run the models, disseminate the information and then they have their own internal decision making processes.”
Quiring's models are part of a larger project that aims to predict the probability of power outages from a wide variety of events, including less severe but more frequent incidents like thunderstorms, heatwaves and blizzards.