Numerical Weather Prediction of Sea Surface Temperature in South China Sea Using Attention-Based Context Fusion Network

被引：0
|
作者：
He, Hailun [1 ,2 ]
Shi, Benyun [3 ]
Zhu, Yuting [3 ]
Feng, Liu [4 ]
Ge, Conghui [3 ]
Tan, Qi [3 ]
Peng, Yue [3 ]
Liu, Yang [4 ]
Ling, Zheng [5 ]
Li, Shuang [6 ]
机构：
[1] State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou,310012, China
[2] Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai,519082, China
[3] College of Computer and Information Engineering, College of Artificial Intelligence, Nanjing Tech University, Nanjing,211816, China
[4] Department of Computer Science, Hong Kong Baptist University, Hong Kong
[5] Key Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang,524088, China
[6] Institute of Physical Oceanography and Remote Sensing, Ocean College, Zhejiang University, Zhoushan,316021, China
来源：
Remote Sensing | 2024年 / 16卷 / 20期
基金：
中国国家自然科学基金;
关键词：
Numerical weather prediction of sea surface temperature (SST) is crucial for regional operational forecasts. Deep learning offers an alternative approach to traditional numerical general circulation models for numerical weather prediction. In our previous work; we developed a sophisticated deep learning model known as the Attention-based Context Fusion Network (ACFN). This model integrates an attention mechanism with a convolutional neural network framework. In this study; we applied the ACFN model to the South China Sea to evaluate its performance in predicting SST. The results indicate that for a 1-day lead time; the ACFN model achieves a Mean Absolute Error of 0.215 °C and a coefficient of determination ((Formula presented.)) of 0.972. In addition; in situ buoy data were utilized to validate the forecast results. The Mean Absolute Error for forecasts using these data increased to 0.500 °C for a 1-day lead time; with a corresponding (Formula presented.) of 0.590. Comparative analyses show that the ACFN model surpasses traditional models such as ConvLSTM and PredRNN in terms of accuracy and reliability. © 2024 by the authors;
D O I：
10.3390/rs16203793
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