Skillful Nowcasting of Convective Clouds With a Cascade Diffusion Model

TL;DR

SATcast is a novel diffusion-based model that combines multimodal satellite and physical data in a cascade architecture to improve the accuracy and robustness of convective cloud nowcasting up to 24 hours.

Contribution

The paper introduces SATcast, a new cascade diffusion model that integrates physical and satellite data for enhanced convective cloud nowcasting.

Findings

Outperforms traditional methods on multiple metrics

Maintains predictive skill for up to 24 hours

Highlights importance of multimodal and cascade design

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.