- August 25, 2026
- By Melissa Brachfeld
From violent rainfalls to approaching hurricanes to river flooding, water-related hazards can threaten electrical grids, reservoirs and other critical infrastructure. Energy providers need localized, long-range forecasts to prepare, but current prediction systems aren’t up to the task because of scientific uncertainty and the enormous computing power required.
Now, a University of Maryland-led team of climate and computing experts is using generative artificial intelligence to take on some of the most complex—and computationally demanding—math on Earth.
The U.S. Department of Energy has awarded the researchers $750,000 in Phase I funding to develop an AI-powered framework that can generate faster, more detailed seasonal and multi-year water forecasts. The nine-month award includes approximately $470,000 for UMD, the lead institution.
The project was one of 270 projects selected from more than 5,000 proposals submitted to the department’s highly competitive Genesis Mission program. If funded in Phase II, the team will expand its early prototypes into operational tools with broader real-world applications.
Led by Jinwoong Yoo, an assistant research scientist at UMD’s Earth System Science Interdisciplinary Center, the project brings together experts in atmospheric science, artificial intelligence, high-performance computing and mathematics.
“The key aspect of the DOE Genesis Mission is really about how creatively we can harness the power of machine learning and AI agents to advance the predictability of the Earth system beyond its current limits,” said Yoo, who is also a researcher at NASA’s Goddard Space Flight Center. “To meet that challenge, I've brought together some of the most capable experts in their respective niches from across the country.”
The UMD team includes four faculty members from the University of Maryland Institute for Advanced Computer Studies (UMIACS): Maria Molina, assistant professor of atmospheric and oceanic science; Chris Metzler, assistant professor of computer science; Abhinav Bhatele, professor of computer science; and Haizhao Yang, professor of mathematics. The team also includes ESSIC Associate Research Scientist Mahdi Navari and researchers from Oak Ridge National Laboratory, the University of Florida, the University of Colorado Boulder and Silurian AI.
For Molina, the partnership reflects a growing need to combine advances in AI with decades of expertise in Earth system science. The team’s goal is to integrate cutting-edge AI forecasting with the Department of Energy’s flagship Energy Exascale Earth System Model (E3SM), creating a powerful tool that can produce faster predictions without sacrificing scientific rigor.
“If we can get this framework right, we can give planners a clearer picture of the hazards their communities may face seasons to years in advance by revealing how event likelihood, intensity and local impacts may evolve,” Molina said.
Traditional physics-based Earth system models such as E3SM simulate interactions among the atmosphere, ocean, land and water cycle across the globe. Seasonal-to-multiyear forecasting requires many simulations to capture the range of possible outcomes, creating many tradeoffs. Turning those forecasts into useful information at the community level remains difficult because predictability decreases at longer timescales, while local hazards depend on processes that global models cannot fully represent.
The UMD team’s approach uses AI to accelerate nearly every step of that process.
The framework will first incorporate satellite observations of soil moisture and groundwater using machine learning to improve the model’s starting conditions. Generative AI models trained on decades of E3SM simulations will then emulate the behavior of the original model, producing more than 100 possible forecast scenarios to estimate both likely outcomes and uncertainty more than 100 times faster than traditional methods.
Image-enhancement techniques will sharpen those regional forecasts into street-level flood maps with one-meter resolution. Behind the scenes, autonomous AI software agents will coordinate the complex workflow—from preparing model inputs to running simulations and analyzing results—allowing researchers to focus on evaluating forecast quality and scientific reliability.
To ensure the predictions remain grounded in physical science, Molina will lead the team's validation efforts. Researchers will test the framework against major hurricanes, evaluate whether it captures climate patterns such as El Niño and use explainable AI techniques so users can determine which factors drive the model’s forecasts.
“The real bottleneck has always been computational cost,” Metzler said. “By using AI to emulate these massive Earth system models, we can run simulations in minutes on supercomputers that used to take days. That speed is what makes real-time protection of local grids actually possible.”
Before deploying the system on the Department of Energy’s cutting-edge supercomputers, the team will develop, train and evaluate its initial models using UMIACS’ high-performance computing infrastructure.
Ultimately, the researchers hope their work will help utilities, water managers and emergency planners make better decisions before disasters strike.