Sea Surface Temperature and Marine Heat Wave Predictions in the South China Sea: A 3D U-Net Deep Learning Model Integrating Multi-Source Data
文献类型：期刊论文
| 作者 | Xie, Bowen1,2; Qi, Jifeng2,3; Yang, Shuguo1; Sun, Guimin2,3; Feng, Zhongkun1; Yin, Baoshu2,3; Wang, Wenwu4; Salata, Ferdinando |
| 刊名 | ATMOSPHERE
 |
| 出版日期 | 2024 |
| 卷号 | 15期号:1页码:20 |
| 关键词 | sea surface temperature deep learning 3D U-Net model marine heat waves South China Sea multi-source data |
| DOI | 10.3390/atmos15010086 |
| 通讯作者 | Qi, Jifeng(jfqi@qdio.ac.cn) |
| 英文摘要 | Accurate sea surface temperature (SST) prediction is vital for disaster prevention, ocean circulation, and climate change. Traditional SST prediction methods, predominantly reliant on time-intensive numerical models, face challenges in terms of speed and efficiency. In this study, we developed a novel deep learning approach using a 3D U-Net structure with multi-source data to forecast SST in the South China Sea (SCS). SST, sea surface height anomaly (SSHA), and sea surface wind (SSW) were used as input variables. Compared with the convolutional long short-term memory (ConvLSTM) model, the 3D U-Net model achieved more accurate predictions at all lead times (from 1 to 30 days) and performed better in different seasons. Spatially, the 3D U-Net model's SST predictions exhibited low errors (RMSE < 0.5 degrees C) and high correlation (R > 0.9) across most of the SCS. The spatially averaged time series of SST, both predicted by the 3D U-Net and observed in 2021, showed remarkable consistency. A noteworthy application of the 3D U-Net model in this research was the successful detection of marine heat wave (MHW) events in the SCS in 2021. The model accurately captured the occurrence frequency, total duration, average duration, and average cumulative intensity of MHW events, aligning closely with the observed data. Sensitive experiments showed that SSHA and SSW have significant impacts on the prediction of the 3D U-Net model, which can improve the accuracy and play different roles in different forecast periods. The combination of the 3D U-Net model with multi-source sea surface variables, not only rapidly predicted SST in the SCS but also presented a novel method for forecasting MHW events, highlighting its significant potential and advantages. |
| WOS关键词 | TROPICAL PACIFIC ; ATLANTIC SST ; OCEAN ; VARIABILITY ; SCALE ; CIRCULATION ; ANOMALIES ; FORECASTS |
| 资助项目 | Natural Science Foundation of Shandong Province, China |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001151965400001 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/184323]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Qi, Jifeng |
| 作者单位 | 1.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao 266061, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Univ Surrey, Dept Elect & Elect Engn, Guildford GU2 7XH, England |
| 推荐引用方式 GB/T 7714 | Xie, Bowen,Qi, Jifeng,Yang, Shuguo,et al. Sea Surface Temperature and Marine Heat Wave Predictions in the South China Sea: A 3D U-Net Deep Learning Model Integrating Multi-Source Data[J]. ATMOSPHERE,2024,15(1):20. |
| APA | Xie, Bowen.,Qi, Jifeng.,Yang, Shuguo.,Sun, Guimin.,Feng, Zhongkun.,...&Salata, Ferdinando.(2024).Sea Surface Temperature and Marine Heat Wave Predictions in the South China Sea: A 3D U-Net Deep Learning Model Integrating Multi-Source Data.ATMOSPHERE,15(1),20. |
| MLA | Xie, Bowen,et al."Sea Surface Temperature and Marine Heat Wave Predictions in the South China Sea: A 3D U-Net Deep Learning Model Integrating Multi-Source Data".ATMOSPHERE 15.1(2024):20. |
入库方式： OAI收割
来源：海洋研究所
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