Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations

Jifeng QI; Guimin SUN; Bowen XIE; Delei LI; Baoshu YIN

doi:10.1007/s00343-023-3063-z

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Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations

Physics | Updated：2024-10-11

- Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations
- Journal of Oceanology and Limnology Vol. 42, Issue 2, Pages: 377-389(2024)
- Affiliations：
  
  1.CAS 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.School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
  4.CAS Engineering Laboratory for Marine Ranching, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
- Author bio：
  
  jfqi@qdio.ac.cn
- Funds：
- DOI：10.1007/s00343-023-3063-z
  CLC：
- Received：06 April 2023，
  
  Published：01 March 2024
- Accepted：
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QI Jifeng,SUN Guimin,XIE Bowen,et al.Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations[J].Journal of Oceanology and Limnology,2024,42(02):377-389.
DOI：

QI Jifeng,SUN Guimin,XIE Bowen,et al.Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations[J].Journal of Oceanology and Limnology,2024,42(02):377-389. DOI： 10.1007/s00343-023-3063-z.

摘要

Abstract

Accurately estimating the ocean subsurface salinity structure (OSSS) is crucial for understanding ocean dynamics and predicting climate variations. We present a convolutional neural network (CNN) model to estimate the OSSS in the Indian Ocean using satellite data and Argo observations. We evaluated the performance of the CNN model in terms of its vertical and spatial distribution

as well as seasonal variation of OSSS estimation. Results demonstrate that the CNN model accurately estimates the most significant salinity features in the Indian Ocean using sea surface data with no significant differences from Argo-derived OSSS. However

the estimation accuracy of the CNN model varies with depth

with the most challenging depth being approximately 70 m

corresponding to the halocline layer. Validations of the CNN model’s accuracy in estimating OSSS in the Indian Ocean are also conducted by comparing Argo observations and CNN model estimations along two selected sections and four selected boxes. The results show that the CNN model effectively captures the seasonal variability of salinity

demonstrating its high performance in salinity estimation using sea surface data. Our analysis reveals that sea surface salinity has the strongest correlation with OSSS in shallow layers

while sea surface height anomaly plays a more significant role in deeper layers. These preliminary results provide valuable insights into the feasibility of estimating OSSS using satellite observations and have implications for studying upper ocean dynamics using machine learning techniques.

关键词

Keywords

references

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