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<p>Timely, reliable, and robust radar nowcasting is an essential tool for extreme precipitation predictions and weather-dependent decision-making, yet existing methods still face two limitations: effective utilization of 3D radar data and robust prediction with occlusions or missing observations. We propose EchoCast-3D, a generative AI-based 3D ensemble probabilistic nowcasting model. Based on a Mask Diffusion Transformer backbone and trained using 3D radar echo data, EchoCast-3D delivers spatiotemporally consistent 3D forecasts, and generates reliable and complete predictions even when observations contain missing areas, a situation common in operational practice. In multiple real-world rainstorm case studies, EchoCast-3D precisely predicts the 3D evolution of severe convective systems and precipitation processes. Quantitative verification indicates that compared to existing powerful 2D nowcasting systems, EchoCast-3D achieves remarkable improvements of 34.5% in Continuous Ranked Probability Score, 14.5% in Mean Absolute Error, and 17.6% in Critical Success Index at echo intensity exceeding 40 dBZ. Even with 15% data missing, EchoCast-3D still can deliver stable and reasonable predictions, reaching the current state-of-the-art. Our research demonstrates practical application value in extreme weather preparation, and provides accurate, robust radar nowcasting in operations. We anticipate this work will serve as a foundation for new insights in nowcasting research.</p>

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Generative machine learning for skilful 3D radar nowcasting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月27日

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  • Jiaquan Wan,
  • Tao Yang,
  • Qianhua Yu,
  • Ranyu Liu,
  • Weidong Li,
  • Hao Song,
  • Xing Wang,
  • Junchao Wang,
  • Fengchang Xue,
  • Ziniu Xiao,
  • Chunxiang Shi,
  • Quan J. Wang,
  • Jingyu Wang,
  • Baoxiang Pan

摘要

Timely, reliable, and robust radar nowcasting is an essential tool for extreme precipitation predictions and weather-dependent decision-making, yet existing methods still face two limitations: effective utilization of 3D radar data and robust prediction with occlusions or missing observations. We propose EchoCast-3D, a generative AI-based 3D ensemble probabilistic nowcasting model. Based on a Mask Diffusion Transformer backbone and trained using 3D radar echo data, EchoCast-3D delivers spatiotemporally consistent 3D forecasts, and generates reliable and complete predictions even when observations contain missing areas, a situation common in operational practice. In multiple real-world rainstorm case studies, EchoCast-3D precisely predicts the 3D evolution of severe convective systems and precipitation processes. Quantitative verification indicates that compared to existing powerful 2D nowcasting systems, EchoCast-3D achieves remarkable improvements of 34.5% in Continuous Ranked Probability Score, 14.5% in Mean Absolute Error, and 17.6% in Critical Success Index at echo intensity exceeding 40 dBZ. Even with 15% data missing, EchoCast-3D still can deliver stable and reasonable predictions, reaching the current state-of-the-art. Our research demonstrates practical application value in extreme weather preparation, and provides accurate, robust radar nowcasting in operations. We anticipate this work will serve as a foundation for new insights in nowcasting research.