Sea Surface Temperature and Marine Heat Wave Predictions in the South China Sea: A 3D U-Net Deep Learning Model Integrating Multi-Source Data

doi:10.3390/atmos15010086

CORC > 海洋研究所 > 中国科学院海洋研究所 > 海洋环流与波动重点实验室

	Sea Surface Temperature and Marine Heat Wave Predictions in the South China Sea: A 3D U-Net Deep Learning Model Integrating Multi-Source Data
	Xie, Bowen 1,2; Qi, Jifeng 2,3; Yang, Shuguo 1; Sun, Guimin 2,3; Feng, Zhongkun 1; Yin, Baoshu 2,3; Wang, Wenwu 4; Salata, Ferdinando
刊名	ATMOSPHERE
	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.
资助项目	Natural Science Foundation of Shandong Province, China
WOS关键词	TROPICAL PACIFIC ; ATLANTIC SST ; OCEAN ; VARIABILITY ; SCALE ; CIRCULATION ; ANOMALIES ; FORECASTS
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
内容类型	期刊论文
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.