This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Artificial intelligence has revolutionized weather forecasting in just a few years, with global AI weather models now able to produce forecasts that rival some of the world's best physics-based prediction systems. This progress has been driven by three factors: massive amounts of weather data, advances in AI models and unprecedented computing power.
While most of the current discussion about improving AI for weather forecasting focuses on models or new hardware, the data is crucial. At the global scale, AI has benefited from decades of climate and weather records covering the entire Earth. These datasets contain millions of examples of how atmospheric conditions evolve over time, allowing AI models to learn patterns in a way that would have been impossible a decade ago.
But when you zoom in from the global scale to the regional scale, forecasting becomes much more challenging for AI. That difference matters for forecasting hurricane intensity. The world has seen many hurricanes rapidly intensify in recent years, strengthening from relatively weak storms into destructive monsters in a matter of hours.
Hurricane Polo did it off Mexico's Pacific coast, rapidly strengthening from a tropical storm on Sept. 21, 2026, to a powerful Category 5 hurricane in 24 hours. Polo quickly became one of the strongest Pacific storms in decades, with winds reaching 180 mph (290 kilometers per hour). When rapid intensification surprises forecasters—as Hurricane Michael did in 2018 when it grew into a destructive Category 5 hurricane right before hitting Tyndall Air Force Base and Mexico Beach, Florida—communities can be left with too little time to evacuate and prepare.
Unlike global weather forecasts, hurricane intensity forecasts are often considered a regional forecasting problem. Regional forecasts are often concerned with extreme events, such as heavy rainfall, squall lines, severe thunderstorms or hurricanes. These extreme events often develop rapidly or move quickly over short periods of time.
Capturing such behavior in AI models requires data in much greater detail than current global datasets can typically provide. When scientists train AI models to predict hurricane intensity, they usually rely on two sources of data. The first is observations, which include measurements of rainfall, near-surface temperature, wind speed and other weather variables collected from weather stations, radars, buoys and satellites.
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