气候变化领域集成服务门户(CCPortal): Sea Surface Temperature and Marine Heat Wave Predictions in the South China Sea: A 3D U-Net Deep Learning Model Integrating Multi-Source Data

CCPortal

DOI	10.3390/atmos15010086
	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; Qi, Jifeng; Yang, Shuguo; Sun, Guimin; Feng, Zhongkun; Yin, Baoshu; Wang, Wenwu; Salata, Ferdinando
发表日期	2024
EISSN	2073-4433
起始页码	15
结束页码	1
卷号	15 期号:1
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.
英文关键词	sea surface temperature; deep learning; 3D U-Net model; marine heat waves; South China Sea; multi-source data
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001151965400001
来源期刊	ATMOSPHERE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/303092
作者单位	Qingdao University of Science & Technology; Chinese Academy of Sciences; Institute of Oceanology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Surrey
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],2024,15(1).
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).
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).