Preface to the Special Issue: AI Applications in Atmospheric and Oceanic Science: Pioneering the Future (Part I)
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00376-024-4006-0.pdf
Reference16 articles.
1. Bi, K. F., L. X. Xie, H. H. Zhang, X. Chen, X. T. Gu, and Q. Tian, 2023: Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619(7970), 533–538, https://doi.org/10.1038/s41586-023-06185-3.
2. Du, S. Y., and R. H. Zhang, 2024: U-net models for representing wind stress anomalies over the tropical pacific and their integrations with an intermediate coupled model for ENSO studies. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-023-3179-2.
3. Ham, Y. G., J. H. Kim, and J. J. Luo, 2019: Deep learning for multi-year ENSO forecasts. Nature, 573(7775), 568–572, https://doi.org/10.1038/s41586-019-1559-7.
4. Huang, G., Y. Wang, Y. G. Ham, B. Mu, W. C. Tao, and C. Y. Xie, 2024: Toward a learnable climate model in the artificial intelligence era. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-024-3305-9.
5. Li, L., Y. J. Dai, Z. W. Wei, W. Shangguan, N. Wei, Y. G. Zhang, Q. L. Li, and X.-X. Li, 2024: Enhancing deep learning soil moisture forecasting models by integrating physics-based models. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-023-3181-8.
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