[TEXTO PRINCIPAL] Deep diffusion models are pioneering in meteorological forecasting by surpassing traditional techniques in convection nowcasting, offering a four-hour accurate prediction with expansive planetary coverage. Leveraging the power of artificial intelligence (AI) and satellite data, these models provide an invaluable tool for predicting and managing severe weather patterns. The geostationary FengYun-4A satellite data reveals the prowess of the Deep Diffusion Models of Satellite (DDMS) in enhancing the forecasting of convective clouds.

Traditional convection nowcasting relied heavily on numerical weather prediction (NWP), which, although effective on large scales, often grappled with rapidly evolving localized processes. While methods like STEPS and PySTEPS extend predictions based on current observational data, these advection-based techniques fall short in capturing atmospheric variability intricacies and leveraging extensive historical data.

The advent of AI-based methodologies has presented a breakthrough, changing how convection nowcasting is conducted. Deep learning models, including neural networks, have been instrumental in learning spatial and temporal patterns from past meteorological data, producing predictions with increased lead time and scale, though often marred by limited accuracy with heavy rainfall predictions. Overcoming these challenges, DDMS employs advanced diffusion processes to simulate and predict complex spatiotemporal evolution patterns in atmospheric convection.

Hospitalized under the umbrella of AI-powered forecasting, DDMS achieves a thirtyfold spatial efficiency leap, promising transferability across multiple satellite platforms, enriching forecasters’ toolkits globally. The models precisely capture motion trends of cloud development and dissolution patterns, delivering clearer four-hour forecasts, outperforming the state-of-the-art GANs in stability and quality.

Qualitative assessments underline DDMS’s efficiency in predicting severe weather, exemplified by samples from 2022 and 2023. The model demonstrates superiority in identifying and predicting the paths of convective storms—an invaluable advantage over competitors like PredRNN-v2 and NowcastNet.

In conclusion, DDMS marks a significant milestone by enhancing meteorological predictions’ accuracy and robustness, thereby minimizing adverse societal impacts through timely weather warnings. This compelling integration of AI and satellite technology symbolizes the future of meteorological forecasting.