Four-hour thunderstorm nowcasting using a deep diffusion model of satellite data

📄 arXiv: 2404.10512v5 📥 PDF

作者: Kuai Dai, Xutao Li, Junying Fang, Yunming Ye, Demin Yu, Hui Su, Di Xian, Danyu Qin, Jingsong Wang

分类: cs.LG

发布日期: 2024-04-16 (更新: 2025-12-18)

💡 一句话要点

提出深度扩散模型以实现四小时雷暴短期预报

🎯 匹配领域: 支柱八：物理动画 (Physics-based Animation)

关键词: 雷暴预报 深度学习 扩散模型 卫星数据 气象预测 人工智能 时空演变 灾害响应

📋 核心要点

  1. 现有的雷暴短期预报方法在预报时效和覆盖范围上仍存在不足，难以满足灾害应急响应的需求。
  2. 本文提出了一种深度扩散模型，利用扩散过程模拟对流云的复杂演变，结合卫星亮温数据和气象专家的领域知识。
  3. 经过验证，该系统实现了四小时的有效雷暴短期预报，覆盖约2000万平方公里，准确性和分辨率均显著提升。

📝 摘要（中文）

雷暴在短时间内迅速发展，具有高度破坏性，给短期预报带来了重大挑战。随着人工智能方法的出现，雷暴短期预报取得了快速进展，但现有方法在预报时效和覆盖范围上仍存在不足。本文提出了一种基于深度扩散模型的卫星数据雷暴短期预报系统，能够有效模拟对流云的复杂时空演变模式，实现更长时效的准确预报。经过长期测试，该系统首次实现了四小时的有效雷暴短期预报，覆盖范围广、准确性高，展示了扩散模型在对流云预报中的显著能力。

🔬 方法详解

问题定义：本文旨在解决现有雷暴短期预报方法在预报时效和覆盖范围上的不足，尤其是在灾害应急响应中面临的挑战。

核心思路：提出的深度扩散模型通过模拟对流云的复杂时空演变，结合卫星数据和领域知识，提升了预报的准确性和时效性。

技术框架：系统整体架构包括数据输入模块（卫星亮温数据）、扩散模型模块（模拟对流云演变）、输出模块（生成预报结果），并通过反馈机制不断优化模型。

关键创新：最重要的创新在于利用扩散过程有效模拟复杂的对流云演变，与传统物理模型相比，能够在更长的时间范围内提供更准确的预报。

关键设计：模型设计中采用了特定的损失函数以优化预报精度，并在网络结构上进行了调整，以适应高分辨率数据的处理需求。具体参数设置和网络架构细节在论文中进行了详细描述。

📊 实验亮点

实验结果显示，该系统首次实现了四小时的有效雷暴短期预报，覆盖范围达到约2000万平方公里，分辨率为15分钟和4公里，较现有模型的性能显著提升，展示了扩散模型在气象预报中的巨大潜力。

🎯 应用场景

该研究的潜在应用领域包括气象预报、灾害应急响应和基础设施保护等。通过与多颗卫星的协作，该系统有望实现全球范围内的雷暴短期预报，提升社会对自然灾害的应对能力。

📄 摘要（原文）

Convection (thunderstorm) develops rapidly within hours and is highly destructive, posing a significant challenge for nowcasting and resulting in substantial losses to infrastructure and society. After the emergence of artificial intelligence (AI)-based methods, convection nowcasting has experienced rapid advancements, with its performance surpassing that of physics-based numerical weather prediction and other conventional approaches. However, the lead time and coverage of it still leave much to be desired and hardly meet the needs of disaster emergency response. Here, we propose a deep diffusion model for satellite data (DDMS) to establish an AI-based convection nowcasting system. Specifically, DDMS employs diffusion processes to effectively simulate complicated spatiotemporal evolution patterns of convective clouds, achieving more accurate forecasts of convective growth and dissipation over longer lead times. Additionally, it combines geostationary satellite brightness temperature data and domain knowledge from meteorological experts, thereby achieving planetary-scale forecast coverage. During long-term tests and objective validation based on the FengYun-4A satellite, our system achieves, for the first time, effective convection nowcasting up to 4 hours, with broad coverage (about 20,000,000 km2), remarkable accuracy, and high resolution (15 minutes; 4 km). Its performance reaches a new height in convection nowcasting compared to the existing models. In terms of application, our system is highly transferable with the potential to collaborate with multiple satellites for global convection nowcasting. Furthermore, our results highlight the remarkable capabilities of diffusion models in convective clouds forecasting, as well as the significant value of geostationary satellite data when empowered by AI technologies.