How AI is Making Weather Forecasts More Accurate
Interdisciplinary engineering alumni Kate Duffy, PhD’21, and Thomas Vandal, PhD’18, both worked as NASA scientists before creating the new start-up, Zeus AI, which uses AI and machine learning to analyze data from satellites to improve weather forecasting.
When people aren’t complaining about the weather, they are deploring inaccurate weather forecasts that lead to rain on a planned beach day or a windstorm on a still day.
Thomas Vandal and Kate Duffy worked as NASA scientists after earning doctoral degrees in interdisciplinary engineering from Northeastern. They intend to improve on short-term forecasting with a new startup that uses AI and machine learning to predict weather patterns.
The startup, Zeus AI, draws on the enormous amount of data provided by the latest generation of government satellites—atmospheric winds, water vapors, temperature changes and cloud cover that influence weather across the globe.
“It’s a huge volume of data,” says Duffy, who is the chief product officer for Zeus. “A lot of it isn’t used in current weather prediction models.”
“We’re looking at retrieving more dense and complete information about the state of the atmosphere from geostationary data that can then be incorporated into weather models,” she says.
The latest generation of NASA and NOAA geostationary satellites are known as the GOES-R series satellites. They are “substantially better than the previous generation,” says Vandal, who is the CEO of Zeus.
He says, “this enables way more applications” using machine learning and AI to crunch the data, see patterns and make predictions.
“Traditional weather forecasting systems are insanely expensive to run—they run on the biggest supercomputers in the world, basically,” Vandal says. “And they’re actually not able to take in this high density data.
“This is where machine learning comes in. We’re able to do this much less expensively than the government systems because we can do it on a single machine. We’re able to learn quickly from the data that already exists,” Vandal says.
Ganguly says the two, who recently obtained Small Business Innovation Research phase II funding from NASA to launch their startup, showed early on an extraordinary ability to do interdisciplinary research combining both AI and climate science.
“They have won a best-paper award at a highly-selective data science conference, published in top climate and machine learning journals and conferences, as well as in high impact interdisciplinary journals such as Nature Climate Change and Nature Communications,” Ganguly says.
“Their work has been highlighted as path breaking in comments and review articles in Nature.”
Ganguly says Vandal “is among the top few who understands machine learning and AI for weather and climate analytics,” adding that Duffy understands “critical gaps” in AI in a way that benefits the earth and environmental sciences and engineering.
Read full story at Northeastern Global News