CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded
  Modelling
Junchao Gong Lei Bai Peng Ye Wanghan Xu Na Liu Jianhua Dai Xiaokang Yang Wanli Ouyang
Precipitation nowcasting based on radar data plays a crucial role in extreme
weather prediction and has broad implications for disaster management. Despite
progresses have been made based on deep learning, two key challenges of
precipitation nowcasting are not well-solved: (i) the modeling of complex
precipitation system evolutions with different scales, and (ii) accurate
forecasts for extreme precipitation. In this work, we propose CasCast, a
cascaded framework composed of a deterministic and a probabilistic part to
decouple the predictions for mesoscale precipitation distributions and
small-scale patterns. Then, we explore training the cascaded framework at the
high resolution and conducting the probabilistic modeling in a low dimensional
latent space with a frame-wise-guided diffusion transformer for enhancing the
optimization of extreme events while reducing computational costs. Extensive
experiments on three benchmark radar precipitation datasets show that CasCast
achieves competitive performance. Especially, CasCast significantly surpasses
the baseline (up to +91.8%) for regional extreme-precipitation nowcasting.