Sparse data-extended fusion method for sea surface temperature prediction on the East China Sea.

Wang, Xiaoliang; Wang, Lei; Zhang, Zhiwei; Chen, Kuo; Jin, Yingying; Yan, Yijun; Liu, Jingjing

Abstract

The accurate temperature background field plays a vital role in the numerical prediction of sea surface temperature (SST). At present, the SST background field is mainly derived from multi-source data fusion, including satellite SST data and in situ data from marine stations, buoys, and voluntary observing ships. The characteristics of satellite SST data are wide coverage but low accuracy, whereas the in situ data have high accuracy but sparse distribution. For obtaining a more accurate temperature background field and realizing the fusion of measured data with satellite data as much as possible, we propose a sparse data-extended fusion method to predict SST in this paper. By using this method, the actual observed sites and buoys data in the East China Sea area are fused with Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Version 5.0 SST data. Furthermore, the temperature field in the study area were predicted by using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) deep learning methods, respectively. Finally, we obtained the results by traditional prediction methods to verify them. The experimental results show that the method we proposed in this paper can obtain more accurate prediction results, and effectively compensate for the uncertainty caused by the parameterization of ocean dynamic process, the discrete method, and the error of initial conditions.

Citation

WANG, X., WANG, L., ZHANG, Z., CHEN, K., JIN, Y., YAN, Y. and LIU, J. 2022. Sparse data-extended fusion method for sea surface temperature prediction on the East China Sea. Applied sciences [online], 12(12); intelligent computing and remote sensing, article 5905. Available from: https://doi.org/10.3390/app12125905

• Journal Article Type: Article
• Acceptance Date: Jun 6, 2022
• Online Publication Date: Jun 10, 2022
• Publication Date: Jun 30, 2022
• Deposit Date: Jun 20, 2022
• Publicly Available Date: Jun 20, 2022
• Journal: Applied Sciences
• Electronic ISSN: 2076-3417
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 12
• Article Number: 5905
• DOI: https://doi.org/10.3390/app12125905
• Keywords: Deep learning; Association relationship; Heterogeneous clustering; Extended fusion
• Public URL: https://rgu-repository.worktribe.com/output/1682197