Research on Three-Dimensional Ocean Temperature Field Variation Model Based on Assimilation and Reconstruction of Regression Equation

Qin, F.; Zeng, W.; Zou, C., and Yang, J., 2020. Research on three-dimensional ocean temperature field variation model based on assimilation and reconstruction of regression equation. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coas...

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Published in:	Journal of coastal research Vol. 108; no. sp1; pp. 37 - 41
Main Authors:	Qin, Fang, Zeng, Weijia, Zou, Cunming, Yang, Junshan
Format:	Journal Article
Language:	English
Published:	Fort Lauderdale Coastal Education and Research Foundation 01.06.2020 Allen Press Publishing Allen Press Inc
Subjects:	Accuracy Assimilation Assimilation reconstruction Coastal inlets Coastal research Data analysis Energy flow Environmental changes Environmental monitoring Geology Interpolation Marine ecology Marine ecosystems Marine environment Marine geology Mathematical analysis Methods Monthly multiple linear regression Ocean temperature Oceanography Reconstruction Regression models Satellite observation Sea surface Sea surface temperature Surface temperature Temperature distribution Temperature fields
ISSN:	0749-0208, 1551-5036
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Abstract	Qin, F.; Zeng, W.; Zou, C., and Yang, J., 2020. Research on three-dimensional ocean temperature field variation model based on assimilation and reconstruction of regression equation. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 37–41. Coconut Creek (Florida), ISSN 0749-0208. For a long time, natural marine ecosystems have been subject to strong human intervention and global environmental changes. Therefore, it is a meaningful challenge to objectively evaluate the constituent elements of marine material transport, energy flow, and system functions. Besides, the calculation method of three-dimensional (3D) ocean temperature fields is improved in the paper by establishing regression model assimilation and reconstruction of 3D ocean temperature fields. Moreover, according to Guinethut's method, the model is reconstructed with the help of 3D monthly average temperatures and the salinity field of the Copernicus Marine Environment Monitoring Service. Then, monthly average sea surface data sea level anomaly and sea surface temperature from the satellite observations of the next year are used to derive 3D monthly average temperatures of the next year field. Additionally, the data sets of adjacent or similar years are applied to obtain linear regression relationships between sea surface and underwater features, which can improve the accuracy of reconstruction. The most optimal interpolation method is used to assimilate the measured data of Argo to increase accuracy. Finally, after data analysis and comparison, it is proved that compared with other methods, the observation value of the data model established in research content of the paper is further improved, which provides important reference for marine ecological protection work.
AbstractList	Qin, F.; Zeng, W.; Zou, C., and Yang, J., 2020. Research on three-dimensional ocean temperature field variation model based on assimilation and reconstruction of regression equation. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 37–41. Coconut Creek (Florida), ISSN 0749-0208.For a long time, natural marine ecosystems have been subject to strong human intervention and global environmental changes. Therefore, it is a meaningful challenge to objectively evaluate the constituent elements of marine material transport, energy flow, and system functions. Besides, the calculation method of three-dimensional (3D) ocean temperature fields is improved in the paper by establishing regression model assimilation and reconstruction of 3D ocean temperature fields. Moreover, according to Guinethut's method, the model is reconstructed with the help of 3D monthly average temperatures and the salinity field of the Copernicus Marine Environment Monitoring Service. Then, monthly average sea surface data sea level anomaly and sea surface temperature from the satellite observations of the next year are used to derive 3D monthly average temperatures of the next year field. Additionally, the data sets of adjacent or similar years are applied to obtain linear regression relationships between sea surface and underwater features, which can improve the accuracy of reconstruction. The most optimal interpolation method is used to assimilate the measured data of Argo to increase accuracy. Finally, after data analysis and comparison, it is proved that compared with other methods, the observation value of the data model established in research content of the paper is further improved, which provides important reference for marine ecological protection work. For a long time, natural marine ecosystems have been subject to strong human intervention and global environmental changes. Therefore, it is a meaningful challenge to objectively evaluate the constituent elements of marine material transport, energy flow, and system functions. Besides, the calculation method of three-dimensional (3D) ocean temperature fields is improved in the paper by establishing regression model assimilation and reconstruction of 3D ocean temperature fields. Moreover, according to Guinethut’s method, the model is reconstructed with the help of 3D monthly average temperatures and the salinity field of the Copernicus Marine Environment Monitoring Service. Then, monthly average sea surface data sea level anomaly and sea surface temperature from the satellite observations of the next year are used to derive 3D monthly average temperatures of the next year field. Additionally, the data sets of adjacent or similar years are applied to obtain linear regression relationships between sea surface and underwater features, which can improve the accuracy of reconstruction. The most optimal interpolation method is used to assimilate the measured data of Argo to increase accuracy. Finally, after data analysis and comparison, it is proved that compared with other methods, the observation value of the data model established in research content of the paper is further improved, which provides important reference for marine ecological protection work.
Author	Zeng, Weijia Zou, Cunming Qin, Fang Yang, Junshan
Author_xml	– sequence: 1 givenname: Fang surname: Qin fullname: Qin, Fang organization: Dalian University of Science and Technology, Dalian 116052, China – sequence: 2 givenname: Weijia surname: Zeng fullname: Zeng, Weijia organization: Dalian University of Science and Technology, Dalian 116052, China – sequence: 3 givenname: Cunming surname: Zou fullname: Zou, Cunming organization: Dalian University of Technology, Dalian 116024, China – sequence: 4 givenname: Junshan surname: Yang fullname: Yang, Junshan organization: Dalian University of Science and Technology, Dalian 116052, China
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SubjectTerms	Accuracy Assimilation Assimilation reconstruction Coastal inlets Coastal research Data analysis Energy flow Environmental changes Environmental monitoring Geology Interpolation Marine ecology Marine ecosystems Marine environment Marine geology Mathematical analysis Methods Monthly multiple linear regression Ocean temperature Oceanography Reconstruction Regression models Satellite observation Sea surface Sea surface temperature Surface temperature Temperature distribution Temperature fields
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