Four-hour thunderstorm nowcasting using deep diffusion models of satellite

2024-04-16

arXiv.org

AI Summary

This work addresses the critical bottlenecks of short-term, nowcasting thunderstorm prediction—namely, limited lead time and narrow spatial coverage—under severe convective weather conditions. We propose the first global-scale, generative 4-hour thunderstorm forecasting method leveraging geostationary satellite (FY-4A) brightness temperature sequences and a deep diffusion model. Innovatively integrating spatiotemporal attention mechanisms with an end-to-end generative architecture, our approach maintains high-resolution outputs (15-minute temporal and 4-km spatial resolution) while covering ~20 million km². Inference requires only 8 minutes per forecast. Compared to existing AI-based models, our method substantially improves forecast lead time, spatial extent, and cross-regional generalizability. It delivers timely, wide-area, and operationally deployable predictions, thereby enhancing disaster emergency response capabilities.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

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 deep diffusion models 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.

Problem

• Improves thunderstorm nowcasting using deep diffusion models.
• Extends forecast lead time to 4 hours with high accuracy.
• Achieves planetary-scale coverage using geostationary satellite data.

Innovation

• Deep diffusion models simulate convective cloud evolution.
• Geostationary satellite data enables planetary-scale coverage.
• AI-based system forecasts convection up to 4 hours.