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Deep learning and process understanding for data-driven Earth system science[J]. Nature, 2019, 566(7743): 195-204.",{"type":18,"tag":26,"props":229,"children":230},{},[231],{"type":24,"value":232},"[2] Kurth, Thorsten, et al. \"Exascale deep learning for climate analytics.\" SC18: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2018.",{"type":18,"tag":26,"props":234,"children":235},{},[236],{"type":24,"value":237},"[3] Ham, Yoo-Geun, Jeong-Hwan Kim, and Jing-Jia Luo. \"Deep learning for multi-year ENSO forecasts.\" Nature 573.7775 (2019): 568-572.",{"type":18,"tag":26,"props":239,"children":240},{},[241],{"type":24,"value":242},"[4] Pathak J, Subramanian S, Harrington P, et al. Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators[J]. arXiv preprint arXiv:2202.11214, 2022.",{"type":18,"tag":26,"props":244,"children":245},{},[246],{"type":24,"value":247},"[5] Bi K, Xie L, Zhang H, et al. Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast[J]. arXiv preprint arXiv:2211.02556, 2022.",{"title":7,"searchDepth":249,"depth":249,"links":250},4,[],"markdown","content:technology-blogs:zh:2103.md","content","technology-blogs/zh/2103.md","technology-blogs/zh/2103","md",1776506120240]