Combining AI and Earth System Research to Improve Climate Forecasting for Underserved Communities

Maria Molina, an assistant professor of atmospheric and oceanic science, and Chris Metzler, an assistant professor of computer science, are developing novel AI-based approaches to help address systemic inequities and improve the lives of populations that are vulnerable to extreme weather events.

Their work, supported by an $814K award from the National Science Foundation (NSF), will help both experts and non-experts better contextualize uncertainty and inequity in predicting extreme weather and threatening climate conditions.

For example, the researchers say, Black populations are often underserved by existing weather radar systems and therefore may receive less accurate forecasts. Many of these same marginalized communities already struggle to recover from extreme weather events and are expected to experience disproportionate impacts from climate change.

“The growing use of AI models in geoscience has made the largely open challenge of accurately and reliably quantifying uncertainty and inequity more important than ever,” says Molina.

The NSF award is part of a new program that promotes transdisciplinary collaborations among geoscientists, computer scientists, mathematicians and others to develop innovative AI techniques that advance understanding of Earth’s complex systems.

The project by Molina and Metzler was one of only 25 nationwide selected to launch the $20 million program.

The UMIACS researchers plan to develop three algorithmic innovations: a computationally efficient framework that’s capable of producing easily interpretable estimates of uncertainty in geoscience; generalizable metrics that quantify inequities in an Earth system model performance; and a novel AI-ready multimodal geoscience dataset.

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