**
As the United States braces for a tumultuous hurricane season and unprecedented summer heat, experts are raising alarm bells regarding the Trump administration’s significant cuts to essential climate and weather data programmes. With forecasts becoming crucial in the face of increasingly extreme weather, the potential decline in the accuracy of federal weather predictions could have serious implications for public safety and disaster preparedness.
The Impact of Budget Cuts
The National Oceanic and Atmospheric Administration (NOAA) recently introduced a new suite of artificial intelligence-driven weather forecast models, claiming that these innovations would enhance “speed, efficiency, and accuracy.” However, the agency has faced criticism for decreasing its data collection efforts, which are vital for training these AI models effectively. Monica Medina, who previously held a senior position at NOAA, pointed out that while AI can indeed process vast quantities of data more rapidly, its effectiveness hinges on the availability of comprehensive training datasets. Under the Trump administration, funding for NOAA has been slashed by 40%, despite a modest budget increase proposed for the National Weather Service.
“We absolutely need AI to help us crunch the data faster and to make sense of more and more data that we can collect,” Medina asserted. “But right now, what we’re doing is cutting back the data collection… we’re going in the wrong direction.”
Data Collection at Risk
Critics argue that staffing cuts have severely impacted NOAA’s National Weather Service, leading to reduced satellite and weather balloon launches—key components of the nation’s meteorological infrastructure. The shrinking climate programmes threaten crucial ocean buoy networks and other observation systems, while research into the climate crisis is also facing funding reductions. Craig McLean, former acting chief scientist at NOAA, emphasised the correlation between cutting climate research and the degradation of weather forecast quality. “Cutting climate research impacts the skill of our weather forecast, and it arrests our advancement of weather forecasts,” he stated.
The urgency of these concerns is magnified by the looming threat of a “super El Niño,” which is expected to escalate temperatures and potentially increase hurricane activity in various regions. NOAA is set to release its outlook for the 2026 Atlantic hurricane season soon, a forecast that many hope will be informed by robust data.
AI Models: A Double-Edged Sword
Historically, weather predictions relied on physics-based models that employed complex mathematics to simulate atmospheric dynamics. In contrast, AI models identify patterns from historical data to forecast future conditions. While these AI-powered systems require less computational power, studies have revealed that they can significantly underperform, particularly in predicting extreme weather events. Because these models rely heavily on past data, they struggle to account for the increasingly severe anomalies that climate change has rendered common.
Sebastian Engelke, a professor at the University of Geneva and co-author of a recent study published in *Science Advances*, noted, “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.” This inherent limitation poses a critical risk, especially as the nation faces growing challenges from climate-induced extremes.
Chris Gloninger, a forensic meteorologist, drew parallels between the shortcomings of AI models and the inadequacies of existing infrastructure designed for a stable climate. He warned that reliance on AI models trained on outdated climate data could lead to dire consequences for forecasting accuracy. “You need accurate data for inputs for our forecast models, but we’re running on less data currently with this current administration,” he cautioned.
Navigating Future Challenges
While NOAA has not fully transitioned to AI forecasting, it is incorporating artificial intelligence into its ensemble models, blending various techniques to generate a range of probable outcomes. Erica Grow Cei, a spokesperson for the National Weather Service, insisted that the new AI model suite complements existing forecasting tools rather than replacing them. Nevertheless, concerns linger about the implications of integrating AI into federal forecasting amidst ongoing data collection cuts.
Neil Jacobs, NOAA’s current administrator, is respected as a leading figure in modelling science. However, as a Trump appointee, he faces pressures to align with the administration’s budget cuts, which some experts argue are jeopardising the agency’s operational integrity. McLean noted that while Jacobs is committed to advancing weather forecasting, “the man has demonstrated his willingness to be obedient to the president who appointed him.”
Why it Matters
The accuracy of weather forecasts is not merely an academic concern; it is a matter of life and death. Reliable forecasting underpins effective disaster response, enhances aviation safety, and guides vital sectors such as agriculture and energy production. As the climate crisis intensifies, the risks associated with less accurate weather predictions could jeopardise public safety and economic stability. The current trajectory, marked by budget cuts and reduced data collection, threatens to undermine critical forecasting capabilities at a time when they are needed most.
