When it comes to predicting extreme weather events, most people think of short-term forecasts such as flash floods or sudden storms. For farmers, water managers, and policymakers, however, the greater challenge often lies in anticipating seasonal extremes, including prolonged droughts or flooding. These events can have devastating economic and environmental impacts, particularly in states like Virginia, where drought ranks as the second costliest natural disaster after tropical cyclones.
Benjamin Cash, research professor in George Mason University’s Department of Atmospheric, Oceanic and Earth Sciences, is leading a $1 million project funded by National Oceanic and Atmospheric Administration (NOAA) to improve seasonal precipitation forecasts in Virginia.
“We are trying to improve predictions of extremes and precipitation on the time scale of the season; something water managers need to know,” Cash said.
Historically, NOAA has employed specialized forecasting models tailored to different hazards and time scales. While this approach enabled advances in some areas, it also limited the ability to efficiently share improvements across forecasting models.
Cash’s research seeks to advance this framework through the Unified Forecast System (UFS), a single, open-source code base accessible to the research community. By consolidating modeling efforts, UFS streamlines maintenance and allows scientific innovations to benefit forecasts across short-range, medium-range, and seasonal scales.
This shift also reflects NOAA’s move toward a community-driven model, inviting researchers nationwide to use and improve the system. “More eyes on the model are more opportunities to improve it,” Cash said.
“Unlike exploratory modeling, this project takes a hypothesis-driven approach, asking targeted questions about how misrepresentations of key climate features, such as the El Niño–Southern Oscillation, affects seasonal predictions,” Cash explained.
Through a series of controlled experiments, including long-term baseline runs and progressively constrained simulations, the team aims to isolate sources of error and identify ways to improve accuracy.
George Mason will also play a key role in the UFS project by expanding education and training opportunities for future climate scientists. The grant includes a hands-on component designed to teach students how to use NOAA’s forecasting models. Faculty members will develop tutorials and conduct practical sessions; refining materials as challenges arise, such as adapting models to different computing environments.
“We want students to be able to use it as a research tool in their classwork or as part of a thesis,” Cash said. “One element of the proposal is hands-on training for students in the climate dynamics program.”
Beyond George Mason, these resources will be shared with other institutions, enabling students nationwide to engage with advanced climate modeling and contribute to improvements in seasonal forecasting. Broadening access and participation to strengthen the next generation of researchers and accelerate innovation in climate prediction supports George Mason’s Grand Challenge Initiative and the goal of building a climate-resilient society.
The work will unfold in phases through 2028, beginning with long-term model simulations, followed by experiments that progressively constrain the model to observed conditions. Success will include not only answering key scientific questions but also uncovering unexpected issues, such as bugs in model code, that lead to broader improvements.
“My hope is that at the end of this project we will have made measurable, tangible contributions toward improving seasonal forecasts for the United States, particularly precipitation extremes,” Cash said.