  Convection (thunderstorm) develops rapidly within hours and is highly
destructive, posing a significant challenge for nowcasting and resulting in
substantial losses to nature and society. After the emergence of artificial
intelligence (AI)-based methods, convection nowcasting has experienced rapid
advancements, with its performance surpassing that of physics-based numerical
weather prediction and other conventional approaches. However, the lead time
and coverage of it still leave much to be desired and hardly meet the needs of
disaster emergency response. Here, we propose a deep diffusion model of
satellite (DDMS) to establish an AI-based convection nowcasting system. On one
hand, it employs diffusion processes to effectively simulate complicated
spatiotemporal evolution patterns of convective clouds, significantly improving
the forecast lead time. On the other hand, it utilizes geostationary satellite
brightness temperature data, thereby achieving planetary-scale forecast
coverage. During long-term tests and objective validation based on the
FengYun-4A satellite, our system achieves, for the first time, effective
convection nowcasting up to 4 hours, with broad coverage (about 20,000,000
km2), remarkable accuracy, and high resolution (15 minutes; 4 km). Its
performance reaches a new height in convection nowcasting compared to the
existing models. In terms of application, our system operates efficiently
(forecasting 4 hours of convection in 8 minutes), and is highly transferable
with the potential to collaborate with multiple satellites for global
convection nowcasting. Furthermore, our results highlight the remarkable
capabilities of diffusion models in convective clouds forecasting, as well as
the significant value of geostationary satellite data when empowered by AI
technologies.
