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    数值天气预报的未来是人工智能与数学物理模型的融合?

    李双林 张仲石 王惠

    李双林, 张仲石, 王惠, 2022. 数值天气预报的未来是人工智能与数学物理模型的融合?. 地球科学, 47(10): 3919-3921. doi: 10.3799/dqkx.2022.865
    引用本文: 李双林, 张仲石, 王惠, 2022. 数值天气预报的未来是人工智能与数学物理模型的融合?. 地球科学, 47(10): 3919-3921. doi: 10.3799/dqkx.2022.865
    Li Shuanglin, Zhang Zhongshi, Wang Hui, 2022. Will the Future of Numerical Weather Prediction be a Fusion of Artificial Intelligence and Mathematical and Physical Modeling?. Earth Science, 47(10): 3919-3921. doi: 10.3799/dqkx.2022.865
    Citation: Li Shuanglin, Zhang Zhongshi, Wang Hui, 2022. Will the Future of Numerical Weather Prediction be a Fusion of Artificial Intelligence and Mathematical and Physical Modeling?. Earth Science, 47(10): 3919-3921. doi: 10.3799/dqkx.2022.865

    数值天气预报的未来是人工智能与数学物理模型的融合?

    doi: 10.3799/dqkx.2022.865
    详细信息
      作者简介:

      李双林(1966-), 男, 教授, 博士生导师, 长期从事气候动力学(海气相互作用方向)和气候变化的研究.E-mail: shuanglin.li@mail.iap.ac.cn

    Will the Future of Numerical Weather Prediction be a Fusion of Artificial Intelligence and Mathematical and Physical Modeling?

    • 图  1  人工智能(AI)与数学物理模型的深度融合:未来数值天气预报构想

    • [1] Bauer, P., Thorpe, A., Brunet, G., 2015. The Quiet Revolution of Numerical Weather Prediction. Nature, 525(7567): 47-55. https://doi.org/10.1038/nature14956
      [2] Becker, T., Stevens, B., Hohenegger, C., 2017. Imprint of the Convective Parameterization and Sea-Surface Temperature on Large-Scale Convective Self-Aggregation. Journal of Advances in Modeling Earth Systems, 9(2): 1488-1505. https://doi.org/10.1002/2016MS000865
      [3] Bonavita, M., Laloyaux, P., 2020. Machine Learning for Model Error Inference and Correction. Journal of Advances in Modeling Earth Systems, 12(12): e2020MS002232.
      [4] Brajard, J., Carrassi, A., Bocquet, M., et al., 2020. Combining Data Assimilation and Machine Learning to Emulate a Dynamical Model from Sparse and Noisy Observations: A Case Study with the Lorenz 96 Model. Journal of Computational Science, 44: 101171. doi: 10.1016/j.jocs.2020.101171
      [5] Gimeno, L., 2013. Grand Challenges in Atmospheric Science. Frontiers in Earth Science, 1(1): 1-5. https://doi.org/10.3389/feart.2013.00001
      [6] Ham, Y.G., Kim, J.H., Luo, J.J., 2019. Deep Learning for Multi-Year ENSO Forecasts. Nature, 573(7775): 568-572. https://doi.org/10.1038/s41586-019-1559-7
      [7] Rasp, S., Pritchard, M.S., Gentine, P., 2018. Deep Learning to Represent Subgrid Processes in Climate Models. Proceedings of the National Academy of Sciences of the United States of America, 115(39): 9684-9689. https://doi.org/10.1073/pnas.1810286115
      [8] Reichstein, M., Camps-Valls, G., Stevens, B., et al., 2019. Deep Learning and Process Understanding for Data-Driven Earth System Science. Nature, 566(7743): 195-204. https://doi.org/10.1038/s41586-019-0912-1
      [9] Schultz, M.G., Betancourt, C., Gong, B., et al., 2021. Can Deep Learning Beat Numerical Weather Prediction? Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences, 379(2194): 20200097. https://doi.org/10.1098/rsta.2020.0097
      [10] Xia, J.J., Li, H.C., Kang, Y.Y., et al., 2020. Machine Learning-Based Weather Support for the 2022 Winter Olympics. Advances in Atmospheric Sciences, 37(9): 927-932. https://doi.org/10.1007/s00376-020-0043-5
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    出版历程
    • 刊出日期:  2022-10-25

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