Research on parameterized analysis method of 3D temperature field based on remote sensing data

Xing Xiaobo; Xu Yongsheng; Jia Yongjun; Huang Chao

doi:10.3969/j.issn.0253-4193.2020.11.005

Volume 42 Issue 11

Dec. 2020

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Xing Xiaobo，Xu Yongsheng，Jia Yongjun, et al. Research on parameterized analysis method of 3D temperature field based on remote sensing data[J]. Haiyang Xuebao，2020, 42(11)：39–48 doi: 10.3969/j.issn.0253-4193.2020.11.005

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Research on parameterized analysis method of 3D temperature field based on remote sensing data

doi: 10.3969/j.issn.0253-4193.2020.11.005

Xing Xiaobo^{1, 2, 3
,},
Xu Yongsheng^{1, 2, 3
,
,},
Jia Yongjun⁴,
Huang Chao^{1, 2, 3}

1.
Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Laboratory for Ocean Dynamics and Climate, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
4.
National Satellite Ocean Application Service, Beijing 100081, China

Received Date: 2019-11-13
Rev Recd Date: 2020-01-15

Available Online: 2020-12-03

Publish Date: 2020-11-25

Abstract

Abstract

To meet the needs of marine research and marine surveys, a new algorithm for fitting three-dimensional temperature fields was developed based on the Argo profile and sea surface temperature data. The Northwest Pacific region was selected as the experimental sea area to validate the algorithm. The latitude and longitude range of the water area was set as: 30°–40°N, 140°–155°E, and the horizontal resolution was 0.25°. The depth direction was from the sea surface to 2 000 m underwater, and the water area was divided into 29 layers. First the fitting algorithm divided the Argo temperature profile into six layers with five depths. The layers were mixed layer, inter layer, thermocline, transition layer, the first deep layer, and the second deep layer. The first guess and the sea surface temperature obtained by linear regression were used as the initial conditions to reconstruct the three-dimensional temperature field. The RMSE of the reconstructed three-dimensional temperature field was small and well correlated with the original observation profile, indicating that the algorithm is reasonable and effective.
- three-dimensional temperature field,
- remote sensing data,
- parameterization method

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References(23)

References

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