In a critical advance for climate resilience, researchers from The Hong Kong University of Science and Technology (HKUST) have developed an AI model that can predict dangerous convective storms-including Black Rainstorms, thunderstorms and extreme heavy rainfall like those that have hit Hong Kong-up to four hours before they strike. This world-first technology, developed in collaboration with national meteorological institutions and powered by satellite data and advanced deep diffusion technology, improves forecast accuracy by over 15% at the 48‑kilometer spatial scale compared with existing systems. This breakthrough strengthens the overall accuracy of the national weather forecasting system and promises to transform early warning systems for vulnerable communities across Asia.
This research aligns with the core objectives of the State Key Laboratory of Climate Resilience for Coastal Cities (SKL-CRCC), which was established with the approval of the Ministry of Science and Technology of China last year. The laboratory operates under the directorship of Prof. Charles NG Wang-Wai, Vice President for Institutional Advancement and CLP Holdings Professorship in Sustainability at HKUST.
The research team includes Prof. SU Hui, the Climate Change and Extreme Weather Direction Lead of the SKL-CRCC, Chair Professor in the Department of Civil and Environmental Engineering, and Global STEM Professor at HKUST; Dr. DAI Kuai, Postdoctoral Fellow in the same department; as well as scholars from the Harbin Institute of Technology (Shenzhen), the China Meteorological Administration's (CMA) Institute of Tropical and Marine Meteorology (ITMM), and the National Satellite Meteorological Center (NSMC). The study was published in the Proceedings of the National Academy of Sciences (PNAS) under the title "Four-hour thunderstorm nowcasting using a deep diffusion model for satellite data."
Extreme weather events have become increasingly frequent in recent years. Hong Kong issued four Black Rainstorm Warnings within just eight days last summer, while regions such as Bali in Indonesia, southern Thailand, and other regions also experienced severe flooding that resulted in significant casualties and economic losses. Conventional weather forecasts rely on numerical weather prediction (NWP) models, which simulate future atmospheric conditions by solving complex fluid-dynamical equations. However, NWP requires intensive computation and is highly sensitive to atmospheric chaos and limitations in observational data. For rapidly evolving, small scale convective systems-including thunderstorms and rainstorms-accurate forecasts are often limited to 20 minutes to two hours in advance. This short window leaves governments, emergency services, and the public with critically limited time to prepare, evacuate, or mitigate damage.
To address these challenges, the HKUST led team developed a new AI computational framework known as the Deep Diffusion Model of Satellite Data (DDMS). The model applies state of the art generative AI techniques: noise is added to satellite data during training, enabling the model to learn the reverse process of generating high-quality data. The team trained the model using infrared brightness temperature data collected by China's FengYun 4A satellite from 2018 to 2021, incorporating professional meteorological domain expertise to accurately capture the evolution of convective cloud structures. Model performance was validated using samples from the spring and summer seasons (May to August) of 2022 and 2023.
The team developed the world's first AI system capable of forecasting thunderstorm development four hours ahead, with accuracy improved by more than 15% at a resolution of 48 kilometers, compared to existing systems.
Other technological breakthroughs include:
High resolution, high frequency forecasts updated at approximately 15 minute intervals, covering a region of about 20 million km2, including China, Korea, Southeast Asia, and surrounding areas.
Stable performance across multiple spatial scales (4 km to 48 km) and throughout different seasons, with particularly strong accuracy in the 2-4 hour forecast window. Within this critical lead time, it excels precisely where conventional models fail most, delivering reliable forecasts with accuracy improvements ranging from 3% to 16% and averaging 8.26%.
Dr. Dai Kuai, the first author of the paper stated, "Conventional weather forecasting models rely mainly on ground based radar, but radar signals are easily affected by terrain and precipitation composition and often detect changes only after convective clouds have already formed. This results in delays in forecast lead time. By leveraging satellite data that monitor cloud evolution from space, the new AI model can detect signs of convective development much earlier, enabling more timely warnings. DDMS represents a major advancement in atmospheric monitoring and severe-weather early warnings, enabling faster and more accurate forecasts and strengthening regional disaster preparedness and response."
Prof. Su remarked, "This research is a collaboration between universities and national level institutions, including the CMA and the NSMC, and provides a valuable new reference model for operational forecasting. The algorithm can be applied to data from different satellites, expanding its coverage and enabling more countries and regions to respond effectively to rising climate risks. The system also has strong commercialization potential: it can support industries such as energy and insurance by providing earlier and more precise risk assessments, helping organizations evaluate the potential impacts of extreme weather in advance and enhance overall resilience. We are moving from simply observing weather to intelligently anticipating it, which is a fundamental shift for safety and sustainability in a warming world."
Co-author of the study include Prof. LI Xutao; Prof. YE Yunming, and PhD student Mr. YU Demin at the School of Computer Science and Technology at Harbin Institute of Technology (Shenzhen); Mr. FANG Junying, Assistant Researcher from the Institute of Tropical and Marine Meteorology; and Dr. WANG Jingsong, Director-General of the CMA NSMC; Mr. XIAN Di and Mr. QIN Danyu from the CMA NSMC.
