Vol.32,  No.1, 
January  2021
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Sun Jian, Cao Zhuo, Li Heng, et al. Application of artificial intelligence technology to numerical weather prediction. J Appl Meteor Sci, 2021, 32(1): 1-11. DOI: 10.11898/1001-7313.20210101.
Citation: Sun Jian, Cao Zhuo, Li Heng, et al. Application of artificial intelligence technology to numerical weather prediction. J Appl Meteor Sci, 2021, 32(1): 1-11. DOI: 10.11898/1001-7313.20210101.
shu

Application of Artificial Intelligence Technology to Numerical Weather Prediction

  • 1.

    National Meteorological Center, Beijing 100081

  • 2.

    Department of Computer Science and Technology, Tsinghua University, Beijing 100084

  • 3.

    National Supercomputing Center in Wuxi, Wuxi 214072

  • 4.

    Department of Earth System Science, Tsinghua University, Beijing 100084

  • 5.

    Ministry of Education Key Laboratory for Earth System Modeling, Beijing 100084

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    Abstract
  • Numerical weather prediction technology plays an increasingly important role in improving accuracy and service level of modern weather forecast. With the development of observation system and higher resolution and complexity of the numerical weather prediction model, the products of numerical weather forecast have been greatly improved in quantity and quality, and can offer rich information at high spatial-temporal frequency. However, such a large amount of prediction data are not fully explored. Artificial intelligence has achieved great success in many fields, such as pattern recognition and natural language processing, which provides an opportunity for further improving numerical weather prediction. It's also employed in initialization, numerical model and production of weather forecast service, involving observation system, data assimilation, model integration, ensemble forecast and high-performance computing methods. Both the accuracy of forecast results and computational efficiency have been improved by using error correction, parameter estimation, local surrogate model and so on. In addition, some end-to-end neural network models also show the potential of pure data-driven weather forecast. These models use spatial-temporal observation data as input and directly output the prediction results in terms of deterministic results or probabilities. Some of them perform well in short-term severe convective weather, precipitation, and long-term climate forecast. Existing works employ various artificial intelligence technology methods, mainly including large-scale calculation of neural network, feature analysis, interpretability, and customized loss function. However, there are still some challenges, the potential of artificial intelligence needs to be further explored. Some issues should be carefully considered, including weak interpretability, uncertainty analysis and the coupling with conventional numerical models, and how to use physical knowledge to guide the design of artificial intelligence model is also worth addressing. To deal with these challenges, some promising suggestions are proposed. Bayesian network and causal network will help to establish more comprehensive and profound feature engineering. Using Bayesian inference to generate distribution characteristics of current meteorological states may be an alternative to efficient and effective uncertainty quantification. The development of some standard workflow and framework will contribute to the coupling of conventional numerical model and artificial intelligence module. Successful artificial intelligence applications in weather forecast require deep cooperation between meteorological experts and computer experts who focus on artificial intelligence and high-performance computing.
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  • Fig  1.   Workflow of numerical weather prediction

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    Fig  2.   Components of artificial intelligence technology

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    Fig  3.   Weather prediction workflow based on artificial intelligence models

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    Table  1   Artificial intelligence applications to numerical weather prediction

    功能 模块 人工智能技术 目标 效果
    初值生成 观测资料处理及质量控制 贝叶斯方案、全卷积网络、极限学习机等 观测偏差纠正[43]、雷达及卫星图像资料预处理[44-45]等 提高观测资料质量，优化高分辨率图像资料分割、资料填补等
    资料同化 随机森林、深度神经网络、支持向量机等 同化算法参数优化[46]、部分替代资料同化方法[47]、聚焦观测区域[48]等 提高同化质量，提高同化速度，更好利用高分辨率资料等
    预报 模式积分 深度神经网络、卷积网络、随机森林等 模式代理[49]、替代物理过程参数化方案[50-53]、参数校正[54-55]等 提高模式计算速度，优化次网格物理过程的表示，提高参数校正效果与速度等
    产品应用 后处理 随机森林、深度神经网络、卷积神经网络等 确定性及集合预报结果后处理[56-58]、替代集合预报[59-60]等 后处理偏差订正、质量更好、效率更高等
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    • Received : 2020-08-24
    • Accepted : 2020-11-01
    • Published : 2021-01-30
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