Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea

Xiaoliang Wang, Lei Wang (Lead / Corresponding author), Zhiwei Zhang, Kuo Chen, Yingying Jin, Yijun Yan, Jingjing Liu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
24 Downloads (Pure)

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.

Original languageEnglish
Article number5905
Number of pages11
JournalApplied Sciences (Switzerland)
Volume12
Issue number12
DOIs
Publication statusPublished - 10 Jun 2022

Keywords

  • association relationship
  • deep learning
  • extended fusion
  • heterogeneous clustering

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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