As hurricane season approaches and the nation braces for intensifying heat, a critical vulnerability is emerging in America's forecasting infrastructure: the government is deploying artificial intelligence weather models that need massive amounts of training data, even as it slashes the funding that collects that data.
The National Oceanic and Atmospheric Administration launched an advanced suite of AI-powered weather models late last year, designed to speed up forecasts and improve accuracy. The agency trained these systems on centuries of historical weather data. But Trump administration budget proposals would cut NOAA's overall funding by 40 percent, even as the National Weather Service receives a modest increase.
The disconnect is stark. AI weather models require robust data to function reliably. Without it, they fail precisely when forecasters need them most.
"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 Monica Medina, who held senior positions at NOAA under both the Obama and Biden administrations. "But right now, what we're doing is cutting back the data collection. We're going in the wrong direction."
Budget cuts have forced NOAA's National Weather Service to scale back satellite launches and weather balloon operations, two pillars of the nation's atmospheric observation system. Climate research programs are being slashed, threatening ocean buoy networks and other monitoring infrastructure. Funding for researchers who analyze data and identify new sources is being reduced.
A National Weather Service spokesperson disputed the characterization, saying the agency collects "a wealth of weather data each day, from satellites in space, to a network of weather balloons, to buoys in the ocean, and land-based sensors." But multiple reports confirm that staffing reductions have curtailed operations across these systems.
The stakes are highest for extreme weather prediction. A new generation of AI models identifies patterns in historical data to forecast conditions, which makes them faster and computationally efficient. But researchers have found they struggle with the unprecedented events becoming routine under climate change.
According to an April study in Science Advances, AI-powered models "underperform" when predicting extreme weather because they are anchored to historical patterns. When conditions break records, the models tend to predict something closer to what happened before, not what actually occurs. Physics-based models don't have this limitation because they simulate the rules of atmospheric dynamics regardless of whether the situation is familiar.
"They don't really care if there's a different situation than we've seen before, because they can understand based on a rules-based analysis what will happen tomorrow," said Sebastian Engelke, a University of Geneva professor who co-authored the study.
The problem manifested in a recent February 2026 blizzard in the northeastern United States, where conventional models outperformed AI-based ones. Meteorologist Chris Gloninger compared the situation to other infrastructure failures under climate stress: roads designed for stable temperatures buckling under extreme heat, stormwater systems overwhelmed by record rainfall.
"The AI weather models were trained on a climate that no longer exists," Gloninger said.
NOAA has not abandoned traditional forecasting models entirely. The agency says its new AI systems are additions to its ensemble approach, which blends multiple modeling techniques. The new AI suite was built on data from NOAA's flagship physics-based Global Forecast System.
But Gloninger warned that adding AI components while reducing data collection creates compounding risk. "You need accurate data for inputs for our forecast models, but we're running on less data currently with this current administration," he said.
Craig McLean, NOAA's former acting chief scientist, outlined the cascade effect. "Weather times time equals climate. Cutting climate research impacts the skill of our weather forecast, and it arrests our advancement of weather forecasts."
NOAA administrator Neil Jacobs is respected as a skilled modeler who would not rush untested systems into operation, according to John Sokich, a former National Weather Service congressional affairs director. But Jacobs faces a different constraint: he is a Trump appointee whose career depends on defending the administration's budget decisions.
McLean noted that Jacobs defended NOAA's budget cuts at a House hearing in April. "I don't think Dr Jacobs would be in a rush to replace capacity with AI that's not ready yet," McLean said. "But at the same time, the man has demonstrated his willingness to be obedient to the president who appointed him and who is destroying the National Oceanic and Atmospheric Administration."
Weather forecasts power early warning systems for disasters, enable safe aviation and maritime operations, and guide economic decisions across energy and agriculture. Medina emphasized the stakes. "Weather forecasts are vital to our economy, to our health, and to public safety."
Author James Rodriguez: "Deploying AI forecasting tools while gutting the data pipelines that feed them is bureaucratic malpractice, especially heading into a season when Americans will depend on accurate warnings."
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