Temperature profile inversion in the South China Sea under the constraint of depth-fixed temperature

Li Qianqian; Wang Ziwen; Zhu Jinlong; Juan Zhihao; Li Qi; Luo Yu

doi:10.12284/hyxb2023097

Volume 45 Issue 7

Jul. 2023

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Article Navigation > Haiyang Xuebao > 2023 > 45(7): 126-136

Li Qianqian，Wang Ziwen，Zhu Jinlong, et al. Temperature profile inversion in the South China Sea under the constraint of depth-fixed temperature[J]. Haiyang Xuebao，2023, 45(7)：126–136 doi: 10.12284/hyxb2023097

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Temperature profile inversion in the South China Sea under the constraint of depth-fixed temperature

doi: 10.12284/hyxb2023097

Li Qianqian^{1, 2
,},
Wang Ziwen¹,
Zhu Jinlong¹,
Juan Zhihao¹,
Li Qi¹,
Luo Yu^{1
,
,}

1.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

Received Date: 2022-10-20
Rev Recd Date: 2023-01-07

Available Online: 2023-07-31

Publish Date: 2023-07-01

Abstract

Abstract

In order to quickly obtain a large-scale, quasi-real-time internal structure of the ocean, sea surface remote sensing data are widely used to construct the vertical structure of the temperature profiles, but satellite remote sensing can only obtain relatively accurate ocean surface or near-surface data. In order to improve the accuracy of temperature profile inversion, this paper takes the depth-fixed temperature as the constraint, and the nonlinear mapping between the temperature profiles and the sea surface remote sensing data such as sea surface temperature (SST) and sea level anomaly (SLA) is generated through the radial basis function (RBF) neural network, and discuss the theoretical basis for constrained depth selection. The inversion results of the temperature profiles in the South China Sea show that the first empirical orthogonal function (EOF) coefficient can characterize the vertical displacement of the thermocline. And there is a strong correlation between the temperature at the depth corresponding to the extreme point of the first EOF and the first EOF coefficient. Therefore, when the temperature at this depth is added as a constraint, the inversion accuracy of the thermocline is about 0.35℃ higher than that of only using sea surface remote sensing data, and the mean root mean square error of temperature profile inversion is about 0.33℃.
- temperature profile,
- radial basis function neural network,
- empirical orthogonal function,
- sea surface temperature,
- sea level anomaly,
- depth-fixed temperature

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References(20)

References

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