气候变化领域集成服务门户(CCPortal): TemproNet: A transformer-based deep learning model for seawater temperature prediction

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DOI	10.1016/j.oceaneng.2023.116651
	TemproNet: A transformer-based deep learning model for seawater temperature prediction
	Chen, Qiaochuan; Cai, Candong; Chen, Yaoran; Zhou, Xi; Zhang, Dan; Peng, Yan
发表日期	2024
ISSN	0029-8018
EISSN	1873-5258
起始页码	293
卷号	293
英文摘要	Accurate prediction of seawater temperature is crucial for meteorological model understanding and climate change assessment. This study proposes TempreNet, a deep learning model based on a transformer and convolutional neural network, to accurately predict subsurface seawater temperature using satellite observations in the South China Sea. TemproNet uses multivariate sea surface observations such as sea level anomaly (SLA), sea surface temperature (SST), and sea surface wind (SSW) as model inputs, which employs a hierarchical transformer encoder to extract the multi -scale feature, uses a lightweight convolutional decoder to predict seawater temperature. We train and validate the model using the CMEMS temperature dataset and compare its accuracy with Attention-Unet, LightGMB, and ANN. Experimental results show that TemproNet has significantly outperformed other models with RMSE and R2 of 0.52 degrees C and 0.83 in a 32 -layer temperature profile prediction task over 200 m in the South China Sea. In addition, we fully demonstrate the error of our model in space, in time, and at different depths, showing the efficiency and stability of our model. The input sensitivity analysis showed that SST contributed more to predicting shallow water temperature, while SLA significantly impacted the prediction of mid -deep water temperature. The results of this study provide an innovative and reliable solution for seawater temperature prediction and have important implications for meteorological model understanding and climate change assessment.
英文关键词	Transformer; Satellite observation; Deep learning; Seawater temperature
语种	英语
WOS研究方向	Engineering ; Oceanography
WOS类目	Engineering, Marine ; Engineering, Civil ; Engineering, Ocean ; Oceanography
WOS记录号	WOS:001171982100001
来源期刊	OCEAN ENGINEERING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/288366
作者单位	Shanghai University; Shanghai University
推荐引用方式 GB/T 7714	Chen, Qiaochuan,Cai, Candong,Chen, Yaoran,et al. TemproNet: A transformer-based deep learning model for seawater temperature prediction[J],2024,293.
APA	Chen, Qiaochuan,Cai, Candong,Chen, Yaoran,Zhou, Xi,Zhang, Dan,&Peng, Yan.(2024).TemproNet: A transformer-based deep learning model for seawater temperature prediction.OCEAN ENGINEERING,293.
MLA	Chen, Qiaochuan,et al."TemproNet: A transformer-based deep learning model for seawater temperature prediction".OCEAN ENGINEERING 293(2024).