June 24, 2019
By Edwin Aguirre
In May 2006, anstrong low-pressure system stalled over the central United States, bringing torrential rains to the Northeast and triggering widespread flooding. Up to 17 inches of rain fell over Lowell, causing the Merrimack River to overflow and inundate low-lying neighborhoods. Although no injuries or fatalities were reported, the event has been described as the region’s worst flooding since the great flood of 1936 and the New England hurricane of 1938.
Assoc. Prof. Mathew Barlow of the Department of Environmental, Earth and Atmospheric Sciences (EEAS) is conducting research to try to understand what causes such extreme rainfall events in the Northeast, and how well current computer climate models are able to correctly reproduce those causes.
The three-year study is being funded by a $454,000 grant from the U.S. National Science Foundation (NSF). EEAS Assoc. Prof. Jian-Hua Qian and Ph.D. student Laurie Agel are working with Barlow on the project.
“We are working to identify specific types of storms that are more likely to lead to heavy rainfall. At the same time, we are trying to figure out what’s different about those storms from ones that don’t cause heavy rainfall,” says Barlow.
“Once we understand the storms’ different types and important physical conditions and mechanisms, we can look at the heavy rain-producing storms in the models and see how realistic they are. Understanding just how realistic the models are is important for improving our ability to predict such events, both in the short-term and long-term climate projections,” he notes.
High-cost, High-impact Events
Extreme precipitation and its related impacts, especially flooding, can result in significant loss of lives and billions of dollars in property and infrastructure damage, transportation disruption and storm water pollution.
We choose to study the Northeast for several reasons: this is where our campus is located, it’s one of the regions in America with the largest increase in heavy rainfall events and it’s the most economically developed and densely populated region of the country with a lot of infrastructure,” explains Barlow
Using observational data, the team will identify storm types associated with extreme precipitation by applying advanced analytic techniques, including artificial neural network-based pattern-identification algorithms. The researchers will also look for characteristic patterns in the jet stream and other storm features and investigate the physical processes by which extreme precipitation is generated within each storm type.
“Our study is a crucial step in the process of improving forecasts and projections of extreme rainfall events, with the hope of helping mitigate their economic impacts,” says Barlow.