AI Forecast System Shows Early Skill at Spotting Atlantic Storms Before They Form

A new study from a University of Miami-led research team suggests that artificial intelligence is beginning to earn a place beside the world’s most trusted hurricane forecasting tools, offering useful guidance about Atlantic tropical cyclones several days before they officially come into existence. The research, published in the American Meteorological Society journal Weather and Forecasting, compares the European Centre for Medium-Range Weather Forecasts’ long-established physics-based prediction system with its newer machine-learning counterpart, and finds that the two approaches each bring distinct strengths to one of meteorology’s hardest problems: knowing when a disorganized cluster of thunderstorms will organize into a named tropical storm.

The work was led by Sharan Majumdar, a professor of atmospheric sciences at the University of Miami’s Rosenstiel School of Marine, Atmospheric, and Earth Science, in collaboration with scientists from ECMWF, the NSF National Center for Atmospheric Research, and the University of Bonn. Among the collaborators was Quinton Lawton, a former doctoral student of Majumdar’s who is now an assistant professor in the Department of Earth, Atmosphere and Environment at Northern Illinois University. The team focused their analysis on the Atlantic basin from 2020 through 2024, a five-year window that captures both steady improvements in conventional forecasting and the dramatic arrival of AI-based alternatives.

At the heart of the study is a comparison between two ECMWF systems. The first is the Integrated Forecasting System, or IFS, the center’s traditional numerical weather prediction model, which simulates the atmosphere by solving the physical equations that govern fluid motion, thermodynamics, and moisture on a three-dimensional grid. The second is the Artificial Intelligence Forecasting System, or AIFS, a machine-learning model trained on decades of historical atmospheric data to learn the patterns of atmospheric evolution directly. Rather than stepping forward through time with explicit physics calculations, the AIFS produces forecasts in a fundamentally different way, and ECMWF made it operational in 2025, marking a significant expansion of machine-learning-based forecasting alongside its physics-based numerical models.

The specific targets of the analysis were African easterly waves, the westward-moving atmospheric disturbances that form over sub-Saharan Africa during the boreal summer. These waves are the seedlings of many Atlantic hurricanes. As they roll off the West African coast and cross the tropical Atlantic, only some of them manage to organize into tropical cyclones, and identifying which ones will succeed is a central challenge of hurricane forecasting. The timing and location of this transition, known as tropical cyclogenesis, can determine how much warning coastal populations receive, which makes any improvement in lead time potentially life-saving.

The researchers first established a baseline by examining how the conventional IFS performed across the study period, and the results show genuine progress. In 2023, the grid spacing of the IFS ensemble was reduced from 18 kilometers to 9 kilometers, effectively doubling the model’s resolution and allowing it to represent smaller-scale tropical features with greater fidelity. Over the same years, the average probabilities of tropical cyclone formation produced by the conventional system generally increased, indicating growing confidence and skill in detecting developing systems. These improvements reflect the steady, incremental gains that operational forecasting centers have delivered through better observations, more sophisticated model physics, and finer computational grids.

But the more striking findings emerged when the team turned to the AI system. Among the 18 tropical cyclones that developed in the Atlantic in 2024, the AIFS ensemble frequently produced higher probabilities of development than the IFS at lead times of roughly 84 to 120 hours, which corresponds to about three and a half to five days before formation. This advantage was particularly pronounced for stronger tropical waves, the very systems that forecasters most want to flag early. In other words, when a disturbance showed signs of being a serious candidate for development, the AI model often recognized that potential days sooner than the traditional ensemble did.

The picture reversed at shorter lead times. At 36 to 48 hours before formation, the AIFS ensemble generally produced lower probabilities of development than the IFS, especially for weaker systems. This contrast highlights one of the fundamental challenges of forecasting tropical cyclogenesis: the signal that a disturbance will become a tropical cyclone can change substantially as the event approaches. A wave that looked promising five days out may struggle to consolidate, while a seemingly innocuous disturbance can rapidly organize. The study also found that conventional IFS probabilities often increased sharply when forecasts moved from three days to two days before a storm was officially named, and that there was considerable variation from storm to storm, depending in part on the wind-speed threshold used to define development. Cyclogenesis, it turns out, is not a single crisp event but a process whose detectability depends on both the system and the definition applied to it.

The AI system also showed advantages in forecasting where developing tropical systems would be located. The average position error of the AIFS ensemble mean was often smaller than the errors from the AIFS single forecast, the deterministic IFS, and the IFS ensemble mean. Position accuracy matters enormously in practice, because a disturbance’s location relative to warm ocean waters, wind shear, and dry air strongly influences its chances of survival, and because forecasters use position guidance to decide where to focus monitoring resources, aircraft reconnaissance, and public communications. An ensemble mean that consistently pins down the location of a developing system gives forecasters a more reliable anchor for their assessments.

Importantly, the findings do not suggest that AI should replace traditional numerical weather prediction. Majumdar emphasized that AI forecasts can complement the established IFS, providing forecasters with another source of information when assessing the potential for tropical cyclone formation. This framing reflects a growing consensus in the operational meteorology community: machine-learning models trained on historical reanalyses can capture large-scale patterns with remarkable efficiency and at a fraction of the computational cost of physics-based simulation, but the two approaches have different failure modes, and agreement or disagreement between them is itself informative. A forecaster seeing high development probabilities from both systems at a four-day lead time has a very different situation than one seeing a divergence between the two.

The study was supported by a National Science Foundation grant, AGS-2438140, and by the European Centre for Medium-Range Weather Forecasts, and it arrives at a moment of rapid institutional change. ECMWF’s decision to make the AIFS operational in 2025 signals that machine-learning forecasting has moved from research curiosity to production tool at one of the world’s leading centers. For Atlantic hurricane forecasting, Majumdar pointed out, the potential significance is substantial. Better identification of developing systems several days in advance, he said, could give forecasters more time to monitor disturbances, assess possible tracks and communicate emerging risks. The study results point toward a future in which traditional physics-based forecasting and AI-generated guidance work side by side or are combined, potentially giving forecasters a more complete picture of which Atlantic tropical waves are most likely to become the next named storm. As the 2020s progress and both classes of models continue to improve, the practical question is shifting from whether AI belongs in the hurricane forecaster’s toolkit to how best to blend its strengths with the physics-based systems that have anchored tropical cyclone prediction for decades.

Subject of Research: Artificial intelligence and numerical model forecasting of Atlantic tropical cyclogenesis from African easterly waves

Article Title: AI’s eye on the tropics

Article References: AI’s eye on the tropics. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: tropical cyclones, hurricane forecasting, artificial intelligence, ECMWF, African easterly waves, tropical cyclogenesis, ensemble forecasting, machine learning, Atlantic basin, Weather and Forecasting, numerical weather prediction, University of Miami