地球科学进展

地球科学进展 ›› 2026, Vol. 41 ›› Issue (5): 482 -499. doi: 10.11867/j.issn.1001-8166.2026.037   cstr: 32269.14.adearth.CN62-1091/P.2026.037

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基于深度学习的云特性短临预报研究与应用进展
房宸蔚1(), 史月琴1(), 谭超1, 沈淑婧1, 李扬2   
  1. 1.中国气象局云降水物理与人工影响天气重点开放实验室，中国气象局人工影响天气中心，北京 100081
    2.南京气象科技创新研究院，中国气象科学研究院—江苏省气象局，江苏 南京 210041
  • 收稿日期:2026-04-01 修回日期:2026-05-01 出版日期:2026-05-10
  • 通讯作者: 史月琴 E-mail:fangcw@cma.gov.cn;shiyq@cma.gov.cn
  • 基金资助:
    国家自然科学基金项目(U2342222);中国气象局云降水物理与人工影响天气重点开放实验室创新基金项目(2024CPML-C020);中国气象局人工影响天气中心创新团队项目(WMC2023IT01)

Development and Applications in Deep Learning‑Based Nowcasting for Cloud Characteristics

Chenwei Fang1(), Yueqin Shi1(), Chao Tan1, Shujing Shen1, Yang Li2   

  1. 1.China Meteorological Administration Cloud-Precipitation Physics and Weather Modification Key Laboratory (CPML), China Meteorological Administration Weather Modification Centre, Beijing 100081, China
    2.Nanjing Innovation Institute for Atmospheric Sciences, Chinese Academy of Meteorological Sciences-Jiangsu Provincial Meteorological Bureau, Nanjing 210041, China
  • Received:2026-04-01 Revised:2026-05-01 Online:2026-05-10 Published:2026-07-22
  • Contact: Yueqin Shi E-mail:fangcw@cma.gov.cn;shiyq@cma.gov.cn
  • About author:Fang Chenwei, research areas include deep learning-based cloud nowcasting and weather modification research. E-mail: fangcw@cma.gov.cn
  • Supported by:
    the National Natural Science Foundation of China(U2342222);The Innovation Fund of the Key Laboratory of Cloud and Precipitation Physics and Weather Modification, China Meteorological Administration(2024CPML-C020);The Innovation Team Project of the Weather Modification Center, China Meteorological Administration(WMC2023IT01)
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云宏微观特性的短临变化是未来天气演变的重要表征，相较于数值模式和光流法等实况外推法，深度学习方法更能满足云特性短临预报在时效性、空间分辨率和准确性等方面的要求。相较于针对雷达、降水等变量的深度学习短临预测，虽然深度学习在云属性外推的研究与应用较少，但各类模型架构和数据已通过多种融合方式应用于特定任务目标，亟需对相关成果开展系统性梳理和讨论。鉴于人工影响天气、航空安全、能源预报等多个领域对云特性短临预报需求的持续增长，在评估不同深度学习架构与云相关任务的适配性的基础上，分别总结了以云宏微观特性为目标的国内外最新研究进展。同时，深入探讨了深度学习与数值模式云微物理方案融合的不同技术方法，并归纳了全球范围内基于深度学习的云产品业务建设进展。通过分析云宏微观量短临预测的技术难点，指出未来云宏观量预报应引入云生消物理机制并发展概率性预报，云微观量短临预报可探索基于高质量云微物理量反演产品的时序预测、宏微观量跨尺度迁移学习以及深度学习与云微物理参数化方案耦合等发展路径。

关键词: 云宏微观量, 深度学习, 短临预报, 云微物理方案

The short-term evolution of macrophysical and microphysical cloud properties serves as a crucial indicator of future weather evolution. Compared with numerical models and optical flow-based nowcasting methods, Deep Learning (DL) approaches better meet the requirements of cloud-condition nowcasting in terms of timeliness, spatial resolution, and accuracy. Although DL-based research on cloud properties remains less extensive than that aimed at radar reflectivity and precipitation nowcasting, a growing variety of model architectures and data fusion strategies has emerged to address specific task objectives, necessitating a systematic classification and comprehensive review of existing studies. Driven by the increasing demand for cloud nowcasting across weather modification, aviation safety, and solar power forecasting, this paper first evaluates the suitability of major DL architectures for cloud-related tasks. Recent domestic and international research advances targeting both cloud macrophysical (e.g., cloud top height, cloud cover, and cloud optical depth) and microphysical properties (e.g., liquid water content, ice water content, and droplet number concentration) are reviewed separately, covering model architectures, input data sources, and task-specific methodologies. This is followed by an in-depth examination of different technical approaches for integrating DL models with cloud physics parameterization schemes in numerical models, which offers a complementary pathway to improving the explicit forecast of cloud liquid water content, ice water content, and related variables. Furthermore, DL-based cloud-related operational systems implemented or under development in major meteorological services worldwide are summarized. Based on an analysis of the key technical challenges in short-term nowcasting of cloud macrophysical and microphysical variables, this review identifies promising directions for future development. For cloud macrophysical nowcasting, future efforts should focus on incorporating more complete physical mechanisms governing cloud formation and dissipation, as well as advancing probabilistic forecast frameworks based on generative models. For cloud microphysical nowcasting, which remains at an early stage, potential directions include developing time-series prediction models leveraging high-quality cloud microphysical retrieval products as training data, exploiting macro-to-micro cross-scale transfer learning by pretraining on abundant cloud macrophysical observations, and fine-tuning with scarce microphysical retrievals, and improving NWP-driven microphysical nowcasting through DL-microphysics scheme coupling.

Key words: Cloud macro- and microphysical properties, Deep learning, Nowcasting, Cloud microphysics scheme

中图分类号: 

表1 云特性相关任务使用中主要深度学习架构对比
Table 1 Comparison of major deep learning architectures for cloud‑characteristic related tasks
特性CNNRNNTransformerGNNGANDiffusion
代表模型U-Net、SegNet和ResUNetConvLSTM、TrajGRU和PredRNN系列Earthformer、Pangu-Weather、FourCastNet和Swin Transformer FengWuGraphCast和AIFScGAN、DGMR、NowcastNet和Pix2PixCloudDiff、GenCast和SEEDS
核心机制卷积核滑动提取局部空间特征，编码器—解码器结构门控循环单元建模时序依赖，卷积替代全连接实现时空联合建模自注意力机制计算全局依赖，并行处理序列消息传递处理图节点生成器—判别器对抗训练，条件输入控制生成去噪扩散生成，多次采样量化不确定性
优点空间特征提取能力强捕捉短期演变规律能力强长程依赖建模并行计算效率高，可扩展性强处理非规则站点数据，融合稀疏观测生成高质量细节丰富预报场，缓解预报模糊问题概率生成集合预报、捕捉极端情景、不确定性量化能力强
缺点缺乏时序建模能力误差累积导致预测模糊、对初始条件敏感确定性预报易过度平滑，计算资源需求大图结构构建缺乏普适方案训练不稳定、定量指标弱于判别式模型推理耗时久、训练成本高，难以满足超短临时效性需求
云特性任务适用性云检测、云分割、云分类和云属性反演卫星云图序列短临预测中长时效预报和大尺度云系演变预报站点融合预报、云微物理参数站点外推、稀疏观测插值高分辨率云参数预报、概率集合预报云微物理量集合反演和预报
表2 深度/机器学习云相关量反演/外推业务系统
Table 2 Deep learning/machine learning operational systems for cloud‑related parameter retrieval or extrapolation
机构算法以及所属气象业务系统宏观云属性应用领域微观云属性应用领域深度学习框架
芬兰气象局（Finnish Meteorological Institute，FMI）CloudCast（FMI Nowcasting System）北欧区域总云量短临外推—U-Net
欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts，ECMWF）GNN（Graph Neural Networks）/Transformer云方案（AIFS Single v1模式）全球云宏观参数（总云量及高、中、低云量）中期预测—GNN+Transformer
欧洲气象卫星开发中心（European Organisation for the Exploitation of Meteorological Satellites，EUMETSAT）CTTH-NN（NWCSAF/PPS）全球云顶高度反演—MLP
ROCINN（Sentinel-5P）全球云顶高度、云光学厚度、云反照率反演—MLP
ComboCloud*云掩膜云液/冰水含量廓线、云液/冰水路径柱积分、云滴有效半径以及薄卷云检测标志反演MLP+RF
欧洲地基云遥感网络（Aerosol， Clouds and Trace Gases Research Infrastructure-Cloud Network，ACTRIS-Cloudnet）VOODOO（ACTRIS-Cloudnet处理链）—欧洲观测站点云相态定性识别（液态水/冰晶）CNN
美国国家航空航天局（National Aeronautics and Space Administration，NASA）SatVision-TOA*全球三维云垂直掩膜判断、云顶高度、云底高度、云几何厚度和云光学厚度反演—Transformer
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