In a team-based-learning classroom on the University of Michigan’s central campus, the work looked like any coding workshop: small clusters of three and four bent over laptops, water bottles and coffee cups crowding the tables, a workshop leader leaning in over someone’s shoulder to point at a line on the screen.

But the screens told a more specific story.

One showed a Google Colab notebook open to a step labeled “Understand the FVCOM unstructured mesh,” part of a hydrodynamic model used to represent how water moves through the lakes. Another showed a programming environment mid-setup. During the hands-on exercises, scientists in NOAA polos worked through the same problems as the new postdoctoral researchers at the next table, beside another group from the U.S. Army Corps of Engineers.

That mix is the point. The researchers from the University of Michigan and the federal scientists from NOAA’s Great Lakes Environmental Research Laboratory who filled the room earlier this month do not usually collaborate by conference call. Many of them share a building in Ann Arbor, where staff from the U-M Cooperative Institute for Great Lakes Research, or CIGLR, work alongside NOAA colleagues, pulling from the same datasets and asking the same questions about five lakes the region cannot afford to misunderstand. 

The occasion was a workshop on how to clean sensor data, test forecasting models and check machine-learning outputs against what scientists already know about how the lakes behave.

AI tools are entering environmental forecasting, and Great Lakes researchers, from veteran federal scientists to new postdocs, need to know how to use them, and how to question them, before they become routine. Over the course of the day, experts from CIGLR, NOAA and the Michigan Institute for Data and AI in Society (MIDAS) worked through the building blocks: where Great Lakes data comes from—buoys, satellites, atmospheric records—and its limits and uncertainties; how the region’s operational forecast models work; and how to set up a computing environment, then visualize and analyze the data hands-on.

None of it was about any one model. It was about a field deciding, together, when AI belongs in Great Lakes science and when it does not, and about U-M helping lead that conversation.

Read the full story on the Michigan Research website.