用多模态扩散模型实现24小时高精度对流云预报
Skillful Nowcasting of Convective Clouds With a Cascade Diffusion Model
- 采用级联扩散架构融合卫星图像与天气模型输出
- 24小时预报仍保持高精度，显著优于传统方法
- 适合气象灾害预警与观测薄弱地区应用
从卫星图像中准确进行对流云的短临预报对于减轻气象灾害影响至关重要，尤其在地面观测稀疏的发展中国家和偏远地区。深度学习虽在视频预测方面取得进展，但现有模型常生成模糊结果，且在物理场预测上准确性下降。本文提出SATcast，一种基于级联架构和多模态输入的扩散模型，将FuXi深度学习天气模型预测的物理场与历史卫星观测作为条件输入，生成高质量未来云场。全面评估表明，SATcast在多个指标上优于传统方法，展现更优准确性和鲁棒性。消融实验验证了其多模态设计与级联架构对可靠预测的关键作用。值得注意的是，SATcast在长达24小时的预报中仍保持预测能力，展现出其在业务化短临预报中的潜力。
原文摘要 · Abstract (English)
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.
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