SDUST2023VGGA: a global ocean vertical gradient of gravity anomaly model determined from multidirectional data from mean sea surface
Αποθηκεύτηκε σε:
| Εκδόθηκε σε: | Earth System Science Data vol. 17, no. 3 (2025), p. 817 |
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| Κύριος συγγραφέας: | |
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Copernicus GmbH
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| Διαθέσιμο Online: | Citation/Abstract Full Text Full Text - PDF |
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| 100 | 1 | |a Zhou, Ruichen |u College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China; State Key Laboratory of Precision Geodesy, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China | |
| 245 | 1 | |a SDUST2023VGGA: a global ocean vertical gradient of gravity anomaly model determined from multidirectional data from mean sea surface | |
| 260 | |b Copernicus GmbH |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Satellite altimetry is a vital tool for global ocean observation, providing critical insights into ocean gravity and its gradients. Over the past 6 years, satellite data from various space agencies have nearly tripled, facilitating the development of high-precision ocean gravity anomaly and ocean vertical gradient of gravity anomaly (VGGA) models. This study constructs a global ocean VGGA model named SDUST2023VGGA using multidirectional mean sea surface (MSS). To address computational limitations, the global ocean is divided into 72 sub-regions. In each sub-region, the DTU21 MSS model and the CNES-CLS22 mean dynamic topography (MDT) model are used to derive the geoid. To mitigate the influence of long-wavelength signals on the calculations, the study subtracts the long-wavelength geoid derived from the XGM2019e_2190 gravity field model from the (full-wavelength) geoid, resulting in a residual (short-wavelength) geoid. To ensure the accuracy of the VGGA calculations, a weighted least-squares method is employed using residual geoid data from a <inline-formula>17′×17′</inline-formula> area surrounding the computation point. This approach effectively accounts for the real ocean environment, thereby enhancing the precision of the calculation results. After combining the VGGA models for all sub-regions, the model's reliability is validated against the SIO V32.1 VGGA (named curv) model. The comparison between the SDUST2023VGGA and the SIO V32.1 model shows a residual mean is <inline-formula>-0.08</inline-formula> Eötvös (E) and the RMS is 8.50 E, demonstrating high consistency on a global scale. Analysis of the differences reveals that the advanced data processing and modeling strategies employed in the DTU21 MSS model enable SDUST2023VGGA to maintain stable performance across varying ocean depths, unaffected by ocean dynamics. The effective use of multidirectional MSS allows for the detailed capture of ocean gravity field information embedded in the MSS model. Analysis across diverse ocean regions demonstrates that the SDUST2023VGGA model successfully reveals the internal structure and mass distribution of the seafloor. The SDUST2023VGGA model is freely available at 10.5281/zenodo.14177000 <xref ref-type="bibr" rid="bib1.bibx68" id="paren.1" />. | |
| 653 | |a Ocean floor | ||
| 653 | |a Satellite data | ||
| 653 | |a Marine environment | ||
| 653 | |a Accuracy | ||
| 653 | |a Data processing | ||
| 653 | |a Datasets | ||
| 653 | |a Gravity anomalies | ||
| 653 | |a Dynamic topography | ||
| 653 | |a Geoid | ||
| 653 | |a Topography | ||
| 653 | |a Oceanography | ||
| 653 | |a Altimetry | ||
| 653 | |a Sea level | ||
| 653 | |a Wavelength | ||
| 653 | |a Least squares method | ||
| 653 | |a Gravitational fields | ||
| 653 | |a Data analysis | ||
| 653 | |a Sea surface | ||
| 653 | |a Measurement techniques | ||
| 653 | |a Gravity field | ||
| 653 | |a Ocean depths | ||
| 653 | |a Oceans | ||
| 653 | |a Satellite altimetry | ||
| 653 | |a Ocean dynamics | ||
| 653 | |a Mass distribution | ||
| 653 | |a Geophysics | ||
| 653 | |a Ocean circulation | ||
| 653 | |a Earthquakes | ||
| 653 | |a Geoids | ||
| 653 | |a Geodetics | ||
| 653 | |a Climate science | ||
| 653 | |a Climate change | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Guo, Jinyun |u College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China | |
| 700 | 1 | |a Shaoshuai Ya |u College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China | |
| 700 | 1 | |a Sun, Heping |u State Key Laboratory of Precision Geodesy, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China | |
| 700 | 1 | |a Liu, Xin |u College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China | |
| 773 | 0 | |t Earth System Science Data |g vol. 17, no. 3 (2025), p. 817 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3173153596/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3173153596/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3173153596/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |