As the US prepares for hurricane season and a summer of record-breaking heat, experts fear the Trump administration’s cuts to climate and weather data programming could make federal weather forecasts less reliable when they are needed most.
The National Oceanic and Atmospheric Administration (Noaa) launched AI-powered global weather forecast models late last year, which it said would improve “speed, efficiency, and accuracy”. In March, an agency official said those models were being trained with centuries of weather data. However, under Trump, climate and weather data collection has declined, said Monica Medina, who served as Noaa’s principal deputy undersecretary from 2009 to 2012. The administration proposed a modest budget increase for the National Weather Service but a 40% cut to Noaa overall.
“We absolutely need AI to help us crunch the data faster and to make sense of more and more data that we can collect,” said Medina. “But right now, what we’re doing is cutting back the data collection … we’re going in the wrong direction.” Widespread reports show staffing cuts have forced Noaa to scale back satellites and balloon launches, key parts of the data collection system. Shrunken climate programs threaten ocean buoy networks and other observation systems, experts say.
“Weather times time equals climate,” said Craig McLean, Noaa’s former acting chief scientist. “Cutting climate research impacts the skill of our weather forecast, and it arrests our advancement of weather forecasts.” Those impediments come as the US prepares for more extreme weather, with a “super El Niño” expected to spike temperatures and boost hurricane activity. Noaa will issue its outlook for the 2026 Atlantic hurricane season on Thursday.
AI-based models identify patterns in decades of historical data to forecast weather, using less computing power than traditional physics-based models. However, a study published in April in Science Advances found that when predicting extreme weather events, new models still “underperform”. Because their forecasts are based on past weather, they struggle to simulate record-breaking events becoming more common amid the climate crisis, instead predicting weather more similar to historical events. Traditional models don’t have this problem as they assess outcomes based on physical conditions.
