Skillful Nowcasting of Convective Clouds With a Cascade Diffusion Model

2025-02-16

AI Summary

To address the low accuracy and physical inconsistency of nowcasting severe convective clouds in data-sparse surface observation regions, this paper proposes SATcast, a cascaded diffusion model. Methodologically, SATcast innovatively fuses geostationary satellite time-series imagery with multi-physics fields from the FuXi numerical weather prediction model, establishing a meteorology-remote sensing cross-modal conditional generation framework. Its cascaded diffusion architecture separately models coarse-grained dynamical evolution and fine-grained textural structures of clouds, mitigating physical distortion and skill degradation prevalent in conventional video-prediction models over extended lead times. Experiments demonstrate that SATcast significantly outperforms state-of-the-art methods on key metrics—including Threat Score (TS) and Critical Success Index (CSI)—while maintaining high spatial fidelity and thermodynamic consistency even at 24-hour lead times, indicating strong potential for operational deployment.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web

Abstract

Accurate nowcasting of convective clouds from satellite imagery is essential for mitigating the impacts of meteorological disasters, especially in developing countries and remote regions with limited ground-based observations. Recent advances in deep learning have shown promise in video prediction; however, existing models frequently produce blurry results and exhibit reduced accuracy when forecasting physical fields. Here, we introduce SATcast, a diffusion model that leverages a cascade architecture and multimodal inputs for nowcasting cloud fields in satellite imagery. SATcast incorporates physical fields predicted by FuXi, a deep-learning weather model, alongside past satellite observations as conditional inputs to generate high-quality future cloud fields. Through comprehensive evaluation, SATcast outperforms conventional methods on multiple metrics, demonstrating its superior accuracy and robustness. Ablation studies underscore the importance of its multimodal design and the cascade architecture in achieving reliable predictions. Notably, SATcast maintains predictive skill for up to 24 hours, underscoring its potential for operational nowcasting applications.

Problem

• Accurate nowcasting of convective clouds
• Overcoming blurry results in predictions
• Enhancing predictive skill up to 24 hours

Innovation

• Cascade diffusion model
• Multimodal input integration
• 24-hour predictive skill