Hong Kong scientists launch AI model to better predict extreme weather

By Joyce Zhou HONG KONG, Jan 28 (Reuters) – A team of Hong Kong scientists has developed an artificial intelligence weather-forecasting system to predict thunderstorms and heavy downpours up to four hours ahead, compared with the range of 20 minutes to two hours now. The system will help governments and emergency services respond more effectively to increasingly frequent extremes of weather linked to climate change, the team from Hong Kong University of Science and Technology said on Wednesday. "We hope to use AI and satellite data to improve prediction of extreme weather so we can be better prepared," said Su Hui, chair professor of the university's civil and environmental engineering department, who led the project. The system aimed to predict heavy rainfall, Su told a press conference to describe the work published in the Proceedings of the National Academy of Sciences in December. Its model applies generative AI techniques, injecting noise into training data so that the system learns to reverse the process in the effort to produce more precise forecasts. Developed in collaboration with China’s weather authorities, it refreshes forecasts every 15 minutes and has boosted accuracy by more than 15%, the team said. Such work is crucial because the number of typhoons and episodes of wet weather Hong Kong and much of southern China faced in 2025 far exceeded the seasonal norm, scientists said. The city issued its highest rainstorm warning five times last year and the second highest 16 times, setting new records, its observatory said. Both China's Meteorological Administration and Hong Kong's Observatory are working to incorporate the model into forecasts. The team's new AI framework, called the Deep Diffusion Model based on Satellite Data (DDMS), was trained using infrared brightness temperature data collected between 2018 and 2021 by China’s Fengyun‑4 satellite. Satellites can detect cloud formation earlier than other forecasting systems such as radar, Su added. The data was combined with meteorological expertise to capture the evolution of convective cloud systems and later validated with spring and summer samples from 2022 and 2023. (Reporting by Joyce Zhou; Writing by Farah Master; Editing by Clarence Fernandez)

(The article has been published through a syndicated feed. Except for the headline, the content has been published verbatim. Liability lies with original publisher.)