Articles | Volume 18, issue 11
https://doi.org/10.5194/essd-18-7963-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-7963-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
MLAWind: a monthly sea surface wind dataset derived from an interpretable machine learning approach integrating in-situ observations and satellite data
Weihao Guo
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China
Global Ocean and Climate Research Center, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China
Global Ocean and Climate Research Center, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China
Xin Wang
CORRESPONDING AUTHOR
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China
Global Ocean and Climate Research Center, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China
Dongxiao Wang
School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
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Chengyan Liu, Zhaomin Wang, Dake Chen, Xianxian Han, Hengling Leng, Xi Liang, Liangjun Yan, Xiang Li, Craig Stevens, Andrew McC. Hogg, Kazuya Kusahara, Kaihe Yamazaki, Kay I. Ohshima, Meng Zhou, Xiao Cheng, Dongxiao Wang, Changming Dong, Jiping Liu, Qinghua Yang, Xichen Li, Ruibo Lei, Minghu Ding, Zhaoru Zhang, Dujuan Kang, Di Qi, Tongya Liu, Jihai Dong, Lu An, Ru Chen, Tong Zhang, Xiaoming Hu, Bo Han, Haibo Bi, Qi Shu, Longjiang Mu, Shiming Xu, Hu Yang, Hailong Liu, Tingfeng Dou, Zhixuan Feng, Lei Zheng, Xueyuan Tang, Guitao Shi, Yongqing Cai, Bingrui Li, Yang Wu, Xia Lin, Wenjin Sun, Yu Liu, Kai Yu, Yu Zhang, Weizeng Shao, Xiaoyu Wang, Shaojun Zheng, Chengyi Yuan, Chunxia Zhou, Jian Liu, Yang Liu, Yue Xia, Xiaoyu Pan, Jiabao Zeng, Kechen Liu, Jiahao Fan, Chen Cheng, and Qi Li
Geosci. Model Dev., 19, 2985–3033, https://doi.org/10.5194/gmd-19-2985-2026, https://doi.org/10.5194/gmd-19-2985-2026, 2026
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We developed a high-resolution computer model to simulate how the ocean, sea ice, and ice shelves interact around Antarctica. This helps us understand their critical role in global climate and sea-level rise. Our model successfully captures essential features like major currents and seasonal ice changes. Despite some remaining biases, it provides a useful tool for predicting future changes in this vital and rapidly evolving region.
Qi'an Chen, Hongzhou Xu, Dongxiao Wang, Bo Hong, Chunlei Liu, Zheyang Zhang, Huichang Jiang, Wei Song, Tong Long, Ling Wang, Sumin Liu, and Rongjie Chen
Ocean Sci., 21, 2631–2642, https://doi.org/10.5194/os-21-2631-2025, https://doi.org/10.5194/os-21-2631-2025, 2025
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NIWs (near-inertial waves) have horizontal spatial scales of 10–100 km and slow group velocities, making them to interact strongly with mesoscale eddies. In the northwestern SCS (South China Sea), large portion of eddy propagate westward and may interact with local NIWs. In this study, four moorings were deployed in this area and they captured a vivid case of interaction between a cyclonic eddy and NIWs. Our paper provides direct evidence of strong interaction between eddy and NIWs in the northwestern SCS.
Chunhua Qiu, Zhenyang Du, Haibo Tang, Zhenhui Yi, Jiawei Qiao, Dongxiao Wang, Xiaoming Zhai, and Wenbo Wang
Earth Syst. Sci. Data, 17, 3189–3202, https://doi.org/10.5194/essd-17-3189-2025, https://doi.org/10.5194/essd-17-3189-2025, 2025
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The high-resolution autonomous underwater vehicle (AUV) dataset for the South China Sea (SCS) provides 13 491 temperature and salinity profiles and covers 463 d of experiments. To our knowledge, the resolution and length of this dataset are enough to detect the asymmetry, vertical tilt, and temporal evolution of mesoscale eddies (MEs) and the corresponding submesoscale processes. The dataset is expected to improve the accuracy of current and biogeochemistry numerical models. More projects conducting AUV experiments will be promoted in the future.
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Short summary
This study develops a monthly near-global sea surface wind dataset (MLAWind) from 1950–2023 through an interpretable machine learning framework integrating in-situ observations and satellite data. Evaluations show that MLAWind achieves comparable performance to the widely-used reanalysis datasets at different time scales. It provides reliable historical wind data during both satellite and non-satellite periods, demonstrating broad application prospects in ocean and climate research.
This study develops a monthly near-global sea surface wind dataset (MLAWind) from 1950–2023...
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