随着机器学习技术和数据分析方法的不断发展，机器学习方法在大气和海洋科学领域的应用不断扩展。结合物理模型和机器学习已经成为近年来研究大气和海洋现象的前沿热点。其中，机器学习不仅能够分析和处理大气和海洋领域的海量数据，还能对其中的复杂关系进行分类、建模和分析，有助于深入探索大气与海洋系统内部的多层次和多维结构；同时，基于数据驱动构建的神经网络模型在特定任务中的性能已达到甚至超越当前先进的动力模式，成为促进大气和海洋建模及性能改进的重要工具，这一新方法的应用使得天气预报和气候预测等的预报精度大大提高。
基于此，中国科学院大气物理研究所主办英文期刊Atmospheric and Oceanic Science Letters特别组织了一期“机器学习在大气和海洋科学中的应用”专刊。该专刊由任AOSL编委的南京信息工程大学张荣华教授领衔、南京信息工程大学罗京佳教授和刘青山教授为客座编辑共同召集，专刊邀请并发表了来自中国科学院海洋研究所、复旦大学、中山大学、中国地质大学、中国海洋大学、南京信息工程大学、中国科学院大气所等多位专家学者10篇最新研究成果，以促进和推动机器学习在大气和海洋科学领域更广泛的应用。
（专刊封面）
专刊已于近日在Science Direct平台在线出版（https://www.sciencedirect.com/journal/atmospheric-and-oceanic-science-letters/vol/16/issue/4）。详细文章列表：
1. A machine learning approach to quality-control Argo temperature data
Qi Zhang, Chenyan Qian, Changming Dong
https://doi.org/10.1016/j.aosl.2022.100292
2. Deep learning in extracting tropical cyclone intensity and wind radius information from satellite infrared images—a review
Chong Wang, Xiaofeng Li
https://doi.org/10.1016/j.aosl.2023.100373
3. Intelligent identification of oceanic eddies in remote sensing data via Dual-Pyramid UNet
Nan Zhao, Baoxiang Huang, Xinmin Zhang, Linyao Ge, Ge Chen
https://doi.org/10.1016/j.aosl.2023.100335
4. A deep learning–based U-Net model for ENSO-related precipitation responses to sea surface temperature anomalies over the tropical Pacific
Yuchao Zhu, Rong-Hua Zhang
https://doi.org/10.1016/j.aosl.2023.100351
5. 3D DBSCAN detection and parameter sensitivity of the 2022 Yangtze river summertime heatwave and drought
Zhenchen Liu, Wen Zhou, Yuan Yuan
https://doi.org/10.1016/j.aosl.2022.100324
6. U-Net: A deep-learning method for improving summer precipitation forecasts in China
Qimin Deng, Peirong Lu, Shuyun Zhao, Naiming Yuan
https://doi.org/10.1016/j.aosl.2022.100322
7. Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model
Yi Fang, Yunfei Wu, Fengmin Wu, Yan Yan, Qi Liu, Nian Liu, JiangJiang Xia
https://doi.org/10.1016/j.aosl.2023.100339
8. forecast in the Atlantic Ocean using a double-stage ConvLSTM network
Lin Ouyang, Fenghua Ling, Yue Li, Lei Bai, Jing-Jia Luo
https://doi.org/10.1016/j.aosl.2023.100347
9. A spatiotemporal 3D convolutional neural network model for ENSO predictions: A test case for the 2020/21 La Nina conditions
Lu Zhou, Chuan Gao, Rong-Hua Zhang
https://doi.org/10.1016/j.aosl.2023.100330
10. Prediction of ENSO using multivariable deep learning
Yue Chen, Xiaomeng Huang, Jing-Jia Luo, Yanluan Lin, Jonathon S. Wright, Youyu Lu, Xingrong Chen, Hua Jiang, Pengfei Lin
