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.
Abstract (translated)
从卫星图像准确预测对流云对于减轻气象灾害的影响至关重要，尤其是在地面观测有限的发展中国家和偏远地区。最近在深度学习领域的进展显示出视频预测方面的巨大潜力；然而，现有的模型常常产生模糊的结果，并且在预报物理场时准确性降低。在这里，我们介绍了SATcast，这是一种扩散模型，采用了级联架构并利用多模态输入来对卫星图像中的云层进行临近预报。SATcast结合了深度学习气象模型FuXi预测的物理场以及过去的卫星观测数据作为条件输入，以生成高质量的未来云图。通过全面评估，SATcast在多个指标上优于传统方法，展示了其卓越的准确性和鲁棒性。消融实验强调了其多模态设计和级联架构对于实现可靠预测的重要性。值得注意的是，SATcast能够保持长达24小时的预报技能，突显了它在操作性临近预报中的潜在应用价值。
URL
https://arxiv.org/abs/2502.10957
