Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction

Author:

Jiang Jiawei12ORCID,Wang Jun34,Liu Yiping5,Huang Chao1,Jiang Qiufu1ORCID,Feng Liqiang6,Wan Liying7,Zhang Xiangguang12ORCID

Affiliation:

1. Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China

2. University of Chinese Academy of Sciences, Beijing 100049, China

3. College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China

4. Hunan Key Laboratory for Marine Detection Technology, Changsha 410073, China

5. Yantai Center of Coastal Zone Geological Survey, China Geological Survey, Yantai 264000, China

6. Ocean Big Data Center, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China

7. Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing 100081, China

Abstract

In this study, we investigate the feasibility of using historical remote sensing data to predict the future three-dimensional subsurface ocean temperature structure. We also compare the performance differences between predictive models and real-time reconstruction models. Specifically, we propose a multi-scale residual spatiotemporal window ocean (MSWO) model based on a spatiotemporal attention mechanism, to predict changes in the subsurface ocean temperature structure over the next six months using satellite remote sensing data from the past 24 months. Our results indicate that predictions made using historical remote sensing data closely approximate those made using historical in situ data. This finding suggests that satellite remote sensing data can be used to predict future ocean structures without relying on valuable in situ measurements. Compared to future predictive models, real-time three-dimensional structure reconstruction models can learn more accurate inversion features from real-time satellite remote sensing data. This work provides a new perspective for the application of artificial intelligence in oceanography for ocean structure reconstruction.

Funder

Technology Support Talent Program of the Chinese Academy of Sciences

Chinese Academy of Sciences pilot project

Major Science and Technology Infrastructure Maintenance and Reconstruction Project of the Chinese Academy of Sciences

National Key Research and Development Program

National Natural Science Foundation of China

High Level Innovative Talent Project of NUDT

Publisher

MDPI AG

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