On Old Dominion University Week: When it’s hot out, some places are hotter than others.
Maryam Golbazi, research assistant professor of climate science, examines why.
Faculty Bio:
Maryam Golbazi is a Research Assistant Professor at Old Dominion University working with the Joint Institute on Advanced Computing for Environmental Studies (JI-ACES). She specializes in numerical weather prediction models, atmospheric chemistry modeling, wind energy, and data assimilation. Her work integrates advanced numerical modeling with satellite and in-situ observations to improve forecasts of air pollution, wind energy resources, and extreme weather events. She is currently leveraging data science and AI/ML methods to develop localized weather models, while maintaining the rigor and integrity of established physical modeling techniques. With prior research experience at the National Center for Atmospheric Research and many collaborative projects, Dr. Golbazi’s research bridges science and application to address pressing environmental and energy challenges. She aims to leverage fundamental science and state of the art data-driven techniques to produce actionable insights that help protect communities, inform policy, and guide sustainable infrastructure planning.
Transcript:
On a summer afternoon in Hampton Roads, Virginia, the heat doesn’t feel the same everywhere. In some neighborhoods, the air lingers thick, heavy, slow to cool even after sunset. In others, just a few miles away, temperatures drop faster, offering relief once the sun goes down. These differences aren’t random. They’re shaped by concrete, roads, buildings, and the environment.
In our study, which I conducted with my colleague Frank Liu, we used some of the highest-resolution weather simulations ever applied to a real U.S. city to understand how extreme heat behaves at the neighborhood scale. Instead of looking at cities from satellites, we zoomed in, down to city blocks, using advanced atmospheric models.
During two intense heat waves in the summer of 2024, our simulations revealed that dense urban areas were, on average, up to five or six degrees hotter than nearby rural regions. And at night urban neighborhoods stayed warm far longer.
But temperature was only part of the story.
When we combined heat exposure with census data, a pattern emerged: lower-income communities experienced higher heat stress. And that translated directly into energy demand.
That matters, because cooling isn’t free. For families already struggling with energy costs, extreme heat becomes both a health risk and a financial burden.
As heat waves become longer and more intense, understanding where heat concentrates, and who pays the price, may be just as important as predicting the temperature itself. Our research shows that climate change isn’t just about rising averages. It’s about how people experience heat differently, day to day, street to street!
Read More:
[Springer Nature] - High-resolution modeling of extreme heat events with socioeconomic consideration: a real-case WRF–LES approach
