Written by Nathi Magubane for Penn Today.
When Hurricane Katrina reached the Gulf Coast in 2005, emergency responders were blindsided by a storm surge that defied predictions. In Japan six years later, the destructive scale of a tsunami triggered by a massive earthquake outpaced early warnings. The 2020 wildfires that engulfed California overwhelmed air quality models.
In each of these disasters, comprehensive modeling—encompassing tropical cyclones, ocean waves, air quality, and broader climate variables—could have enhanced emergency responses, saved lives, and cut damage repair costs. However, processing such vast amounts of numerical data has traditionally been computationally intensive and expensive, often hindering timely decision-making.
Now, Paris Perdikaris of the University of Pennsylvania and his collaborators at Microsoft Research have developed a machine learning model capable of accurately forecasting a variety of Earth systems, including air quality, ocean waves, and tropical cyclone tracks. Their new model, Aurora, outperforms existing traditional systems at a fraction of the cost, and their findings could help emergency service providers better prepare for extreme weather events. Their findings are published in Nature.
“Earth’s climate is perhaps the most complex system we study—with interactions spanning from quantum scales to planetary dynamics,” says Perdikaris, an associate professor at the School of Engineering and Applied Science. “With Aurora, we addressed a fundamental challenge in Earth system prediction: how to create forecasting tools that are both more accurate and dramatically more computationally efficient.”
For example, the team’s model correctly predicted landfall of 2023’s Typhoon Doksuri—the costliest Pacific typhoon to date—in the northern Philippines four days ahead of the event, while official forecasts erroneously predicted landfall off the coast of northern Taiwan.
Perdikaris explains that the numerical models that have been the backbone of weather prediction for decades involve complex systems of differential equations derived from physics principles. He notes that instead of solving equations, Aurora identifies complex relationships in historical Earth system data and uses these to generate predictions.
“This makes Aurora dramatically faster—generating predictions in seconds rather than hours— while maintaining or even exceeding the accuracy of traditional models,” he says.