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: A new study examining tropical cyclone forecasts in the Atlantic Ocean suggests that artificial intelligence could provide valuable additional guidance for predicting when and where tropical disturbances may develop into tropical storms. Led by Sharan Majumdar, a professor of atmospheric sciences at the University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science, and colleagues, the study compares the European Center for Medium-Range Weather Forecasts (ECMWF)'s traditional Integrated Forecasting System (IFS) with its newer Artificial Intelligence Forecasting System (AIFS).
The researchers analyzed African easterly waves and tropical cyclone development across the Atlantic from 2020 through 2024. African easterly waves are westward-moving atmospheric disturbances that form over sub-Saharan Africa during the boreal summer. Some eventually organize into tropical cyclones, making their evolution an important part of hurricane forecasting.
The study, published in the journal Weather and Forecasting, found that ECMWF's conventional forecasting system improved over the period examined. In 2023, the grid spacing of the IFS ensemble was reduced from 18 kilometers to 9 kilometers, while average probabilities of tropical cyclone formation generally increased over the years. But Majumdar and his collaborators found that ECMWF's new AI system can offer a different and potentially useful perspective.
Among 18 tropical cyclones that developed in 2024, the AIFS ensemble frequently produced higher probabilities of development than the IFS at lead times of roughly 84–120 hours, particularly for stronger tropical waves. At shorter lead times of 36–48 hours, however, the AIFS ensemble generally produced lower probabilities, especially for weaker systems. That difference highlights one of the challenges of forecasting tropical cyclogenesis: The signal that a disturbance will become a tropical cyclone can change substantially as the event approaches.
The research team found that conventional IFS probabilities often increased sharply when forecasts moved from three days to two days before a storm was officially named. The study also found considerable variation from storm to storm, depending in part on the wind-speed threshold used to define development. The AI system also showed advantages in forecasting the location of developing tropical systems.
The average position error of the AIFS ensemble mean was often smaller than those of the AIFS single forecast, the deterministic IFS and the IFS ensemble mean. The findings do not suggest that AI should replace traditional numerical weather prediction, Majumdar said. Instead, AI forecasts can complement the established IFS, providing forecasters with another source of information when assessing the potential for tropical cyclone formation.
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