At a time when artificial intelligence (AI) is revolutionizing weather prediction with its ability to analyze massive datasets and deliver faster, more accurate daily forecasts, researchers from the University of Chicago and New York University and their collaborators have found a critical gap in its predictive power: rare, extreme weather events. They have also made progress toward offering solutions.
Examples of these extreme events include fierce tropical cyclones that are scarcer than even category 5 cyclones such as Hurricane Harvey, which made landfall in Texas in 2017 and became one of the costliest and most destructive hurricanes in U.S. history. Working with researchers at the University of California, Santa Cruz, the team identified a major weakness in current AI systems: they often miss or downplay “gray swan” storms. These events are considered low‑probability and treated as outliers, so pattern‑based algorithms tend to dismiss them. A gray swan is a known risk that goes under‑prepared for, turning an “unlikely” event into a crisis with severe, far‑reaching consequences. As AI systems are rapidly adopted by weather companies and national meteorological agencies, the risk of such failures is growing.