Articles | Volume 17, issue 3
https://doi.org/10.5194/essd-17-817-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-817-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
SDUST2023VGGA: a global ocean vertical gradient of gravity anomaly model determined from multidirectional data from mean sea surface
Ruichen Zhou
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
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Shaoshuai Ya
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Heping Sun
State Key Laboratory of Precision Geodesy, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
Xin Liu
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Related authors
No articles found.
Yixiang Liu, Jinyun Guo, Bin Guan, Shaofeng Bian, Heping Sun, and Xin Liu
EGUsphere, https://doi.org/10.5194/egusphere-2025-2585, https://doi.org/10.5194/egusphere-2025-2585, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study refines the coastal gravity anomaly model by constructing a residual terrain model using high-resolution topographic and bathymetric data. In the spatial domain, the RTM (residual terrain model) gravity forward modeling method is applied to effectively compensate for the missing high-frequency information in the XGM2019e-2159 gravity anomaly model. As a result, an RTM-corrected XGM2019e-2159 gravity anomaly model for the study area is obtained.
Xin Liu, Yang Yang, Menghao Song, Xiaofeng Dai, Yurong Ding, Gaoying Yin, and Jinyun Guo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-2, https://doi.org/10.5194/essd-2025-2, 2025
Revised manuscript not accepted
Short summary
Short summary
This study tackles the challenge of measuring sea surface height in the Arctic Ocean, where ice coverage makes accurate modeling difficult. Using advanced satellite data and innovative methods, a new high-resolution mean sea surface model was created. It achieves greater precision than previous models and offers valuable insights into Arctic oceanography. This research provides an important tool for understanding changes in the Arctic environment and their global impacts.
Shuai Zhou, Jinyun Guo, Huiying Zhang, Yongjun Jia, Heping Sun, Xin Liu, and Dechao An
Earth Syst. Sci. Data, 17, 165–179, https://doi.org/10.5194/essd-17-165-2025, https://doi.org/10.5194/essd-17-165-2025, 2025
Short summary
Short summary
Our research focuses on using machine learning to enhance the accuracy and efficiency of bathymetric models. In this paper, a multi-layer perceptron (MLP) neural network is used to integrate multi-source marine geodetic data. And a new bathymetric model of the global ocean, spanning 0–360° E and 80° S–80° N, known as the Shandong University of Science and Technology 2023 Bathymetric Chart of the Oceans (SDUST2023BCO), has been constructed, with a grid size of 1′ × 1′.
Zhen Li, Jinyun Guo, Chengcheng Zhu, Xin Liu, Cheinway Hwang, Sergey Lebedev, Xiaotao Chang, Anatoly Soloviev, and Heping Sun
Earth Syst. Sci. Data, 16, 4119–4135, https://doi.org/10.5194/essd-16-4119-2024, https://doi.org/10.5194/essd-16-4119-2024, 2024
Short summary
Short summary
A new global marine gravity model, SDUST2022GRA, is recovered from radar and laser altimeter data. The accuracy of SDUST2022GRA is 4.43 mGal on a global scale, which is at least 0.22 mGal better than that of other models. The spatial resolution of SDUST2022GRA is approximately 20 km in a certain region, slightly superior to other models. These assessments suggest that SDUST2022GRA is a reliable global marine gravity anomaly model.
Fengshun Zhu, Jinyun Guo, Huiying Zhang, Lingyong Huang, Heping Sun, and Xin Liu
Earth Syst. Sci. Data, 16, 2281–2296, https://doi.org/10.5194/essd-16-2281-2024, https://doi.org/10.5194/essd-16-2281-2024, 2024
Short summary
Short summary
We used multi-satellite altimeter data to construct a high-resolution marine gravity change rate (MGCR) model on 5′×5′ grids, named SDUST2020MGCR. The spatial distribution of SDUST2020MGCR and GRACE MGCR are similar, such as in the eastern seas of Japan (dipole), western seas of the Nicobar Islands (rising), and southern seas of Greenland (falling). The SDUST2020MGCR can provide a detailed view of long-term marine gravity change, which will help to study the seawater mass migration.
Dechao An, Jinyun Guo, Xiaotao Chang, Zhenming Wang, Yongjun Jia, Xin Liu, Valery Bondur, and Heping Sun
Geosci. Model Dev., 17, 2039–2052, https://doi.org/10.5194/gmd-17-2039-2024, https://doi.org/10.5194/gmd-17-2039-2024, 2024
Short summary
Short summary
Seafloor topography, as fundamental geoinformation in marine surveying and mapping, plays a crucial role in numerous scientific studies. In this paper, we focus on constructing a high-precision seafloor topography and bathymetry model for the Philippine Sea (5° N–35° N, 120° E–150° E), based on shipborne bathymetric data and marine gravity anomalies, and evaluate the reliability of the model's accuracy.
Jiajia Yuan, Jinyun Guo, Chengcheng Zhu, Zhen Li, Xin Liu, and Jinyao Gao
Earth Syst. Sci. Data, 15, 155–169, https://doi.org/10.5194/essd-15-155-2023, https://doi.org/10.5194/essd-15-155-2023, 2023
Short summary
Short summary
The mean sea surface (MSS) is a relative steady-state sea level within a finite period with important applications in geodesy, oceanography, and other disciplines. In this study, the Shandong University of Science and Technology 2020 (SDUST2020), a new global MSS model, was established with a 19-year moving average method from multi-satellite altimetry data. Its global coverage is from 80 °S to 84 °N, the grid size is 1'×1', and the reference period is from January 1993 to December 2019.
Chengcheng Zhu, Jinyun Guo, Jiajia Yuan, Zhen Li, Xin Liu, and Jinyao Gao
Earth Syst. Sci. Data, 14, 4589–4606, https://doi.org/10.5194/essd-14-4589-2022, https://doi.org/10.5194/essd-14-4589-2022, 2022
Short summary
Short summary
Accurate marine gravity anomalies play an important role in the fields of submarine topography, Earth structure, and submarine exploitation. With the launch of different altimetry satellites, the density of altimeter data can meet the requirements of inversion of high-resolution and high-precision gravity anomaly models. We construct the global marine gravity anomaly model (SDUST2021GRA) from altimeter data (including HY-2A). The accuracy of the model is high, especially in the offshore area.
Cited articles
Ablain, M., Legeais, J. F., Prandi, P., Marcos, M., Fenoglio-Marc, L., Dieng, H. B., Benveniste, J., and Cazenave, A.: Satellite Altimetry-Based Sea Level at Global and Regional Scales, Surv. Geophys., 38, 7–31, https://doi.org/10.1007/s10712-016-9389-8, 2017. a
Álvarez, O., Giménez, M., Klinger, F. L., Folguera, A., and Braitenberg, C.: The Peru-Chile Margin from Global Gravity Field Derivatives, Springer International Publishing, Cham, 59–79, ISBN 978-3-319-67774-3, https://doi.org/10.1007/978-3-319-67774-3_3, 2018. a, b
Andersen, O. B. and Knudsen, P.: DNSC08 mean sea surface and mean dynamic topography models, J. Geophys. Res.-Oceans, 114, C11001, https://doi.org/10.1029/2008JC005179, 2009. a
Andersen, O. B., Rose, S. K., Abulaitijiang, A., Zhang, S., and Fleury, S.: The DTU21 global mean sea surface and first evaluation, Earth Syst. Sci. Data, 15, 4065–4075, https://doi.org/10.5194/essd-15-4065-2023, 2023. a, b, c
Annan, R. F., Wan, X., Hao, R., and Wang, F.: Global marine gravity gradient tensor inverted from altimetry-derived deflections of the vertical: CUGB2023GRAD, Earth Syst. Sci. Data, 16, 1167–1176, https://doi.org/10.5194/essd-16-1167-2024, 2024. a
Bouman, J., Bosch, W., and Sebera, J.: Assessment of Systematic Errors in the Computation of Gravity Gradients from Satellite Altimeter Data, Mar. Geod., 34, 85–107, https://doi.org/10.1080/01490419.2010.518498, 2011. a
Butler, D. K.: Microgravimetric and gravity gradient techniques for detection of subsurface cavities, Geophysics, 49, 1084–1096, https://doi.org/10.1190/1.1441723, 1984. a
Clift, P. and Vannucchi, P.: Controls on tectonic accretion versus erosion in subduction zones: Implications for the origin and recycling of the continental crust, Rev. Geophys., 42, RG2001, https://doi.org/10.1029/2003RG000127, 2004. a
DiFrancesco, D., Grierson, A., Kaputa, D., and Meyer, T.: Gravity gradiometer systems-Advances and challenges, Geophys. Prospect., 57, 615–623, https://doi.org/10.1111/j.1365-2478.2008.00764.x, 2009. a
Escartín, J., Smith, D. K., Cann, J., Schouten, H., Langmuir, C. H., and Escrig, S.: Central role of detachment faults in accretion of slow-spreading oceanic lithosphere, Nature, 455, 790–794, https://doi.org/10.1038/nature07333, 2008. a
Fu, L.-L. and Cheney, R. E.: Application of satellite altimetry to ocean circulation studies: 1987–1994, Rev. Geophys., 33, 213–223, https://doi.org/10.1029/95RG00187, 1995. a
Fuchs, M. J., Bouman, J., Broerse, T., Visser, P., and Vermeersen, B.: Observing coseismic gravity change from the Japan Tohoku-Oki 2011 earthquake with GOCE gravity gradiometry, J. Geophys. Res.-Solid, 118, 5712–5721, https://doi.org/10.1002/jgrb.50381, 2013. a
Garcia, E. S., Sandwell, D. T., and Smith, W. H.: Retracking CryoSat-2, Envisat and Jason-1 radar altimetry waveforms for improved gravity field recovery, Geophys. J. Int., 196, 1402–1422, https://doi.org/10.1093/gji/ggt469, 2014. a, b, c
GEBCO Bathymetric Compilation Group: The GEBCO_2024 Grid – a continuous terrain model of the global oceans and land, NERC EDS British Oceanographic Data Centre NOC [data set], https://doi.org/10.5285/1c44ce99-0a0d-5f4f-e063-7086abc0ea0f, 2024. a, b
Guo, J., Gao, Y., Hwang, C., and Sun, J.: A multi-subwaveform parametric retracker of the radar satellite altimetric waveform and recovery of gravity anomalies over coastal oceans, Sci. China Earth Sci., 53, 610–616, https://doi.org/10.1007/S11430-009-0171-3, 2010. a
Han, S., Carbotte, S. M., Canales, J. P., Nedimović, M. R., and Carton, H.: Along-Trench Structural Variations of the Subducting Juan de Fuca Plate From Multichannel Seismic Reflection Imaging, J. Geophys. Res.-Solid, 123, 3122–3146, https://doi.org/10.1002/2017JB015059, 2018. a
Hao, R., Wan, X., and Annan, R. F.: Enhanced Short-Wavelength Marine Gravity Anomaly Using Depth Data, IEEE T. Geosci. Remote, 61, 5903109, https://doi.org/10.1109/TGRS.2023.3242967, 2023. a
Hofmann-Wellenhof, B. and Moritz, H.: Physical Geodesy, Springer, Vienna, ISBN 978-3-211-33545-1, 2006. a
Hu, M., Zhang, S., Jin, T., Wen, H., Chu, Y., Jiang, W., and Li, J.: A new generation of global bathymetry model BAT_WHU2020, Acta Geodaet. Cartogr. Sin., 49, 939–954, https://doi.org/10.11947/j.AGCS.2020.20190526, 2020. a
Hwang, C.: A bathymetric model for the South China Sea from satellite altimetry and depth data, Mar. Geod., 22, 37–51, https://doi.org/10.1080/014904199273597, 1999. a
Hwang, C., Kao, E.-C., and Parsons, B.: Global derivation of marine gravity anomalies from Seasat, Geosat, ERS-1 and TOPEX/POSEIDON altimeter data, Geophys. J. Int., 134, 449–459, https://doi.org/10.1111/j.1365-246X.1998.tb07139.x, 1998. a
Hwang, C., Cheng, Y., Han, J., Kao, R., Huang, C., Wei, S., and Wang, H.: Multi-Decadal Monitoring of Lake Level Changes in the Qinghai-Tibet Plateau by the TOPEX/Poseidon-Family Altimeters: Climate Implication, Remote Sens., 8, 446, https://doi.org/10.3390/rs8060446, 2016. a
ICGEM: Global Gravity Field Models, GFZ Helmholtz Centre for Geosciences, https://icgem.gfz-potsdam.de/tom_longtime (last access: 27 February 2025), 2025. a
Ince, E. S., Barthelmes, F., Reißland, S., Elger, K., Förste, C., Flechtner, F., and Schuh, H.: ICGEM – 15 years of successful collection and distribution of global gravitational models, associated services, and future plans, Earth Syst. Sci. Data, 11, 647–674, https://doi.org/10.5194/essd-11-647-2019, 2019. a, b
Jousset, S., Mulet, S., Wilkin, J., Greiner, E., Dibarboure, G., and Picot, N.: New global Mean Dynamic Topography CNES-CLS-22 combining drifters, hydrological profiles and High Frequency radar data, in: Proceedings of the Ocean Surface Topography Science Team (OSTST) 2022 Meeting, 24–28 October 2022, Venice, Italy, https://doi.org/10.24400/527896/a03-2022.3292, 2022. a
Jousset, S., Mulet, S., Greiner, E., Wilkin, J., Vidar, L., Chafik, L., Raj, R., Bonaduce, A., Picot, N., and Dibarboure, G.: New Global Mean Dynamic Topography CNES-CLS-22 Combining Drifters, Hydrological Profiles and High Frequency Radar Data, ESS Open Archive [data set], https://doi.org/10.22541/essoar.170158328.85804859/v1, 2023. a, b, c
Khaki, M., Forootan, E., Sharifi, M., Awange, J., and Kuhn, M.: Improved gravity anomaly fields from retracked multimission satellite radar altimetry observations over the Persian Gulf and the Caspian Sea, Geophys. J. Int., 202, 1522–1534, https://doi.org/10.1093/gji/ggv240, 2015. a
Kim, S.-S. and Wessel, P.: New global seamount census from altimetry-derived gravity data, Geophys. J. Int., 186, 615–631, https://doi.org/10.1111/j.1365-246X.2011.05076.x, 2011. a
Knudsen, P., Andersen, O., and Maximenko, N.: A new ocean mean dynamic topography model, derived from a combination of gravity, altimetry and drifter velocity data, Adv. Space Res., 68, 1090–1102, https://doi.org/10.1016/j.asr.2019.12.001, 2021. a, b
Knudsen, P., Andersen, O. B., Maximenko, N., and Hafner, J.: A New Combined Mean Dynamic Topography Model – DTUUH22MDT, Poster Presentation at ESA Living Planet Symposium 2022, 23–27 May 2022, Bonn, Germany, 2022. a
Li, Z., Guo, J., Zhu, C., Liu, X., Hwang, C., Lebedev, S., Chang, X., Soloviev, A., and Sun, H.: The SDUST2022GRA global marine gravity anomalies recovered from radar and laser altimeter data: contribution of ICESat-2 laser altimetry, Earth Syst. Sci. Data, 16, 4119–4135, https://doi.org/10.5194/essd-16-4119-2024, 2024. a
Marks, K. M., Smith, W., and Sandwell, D.: Significant improvements in marine gravity from ongoing satellite missions, Mar. Geophys. Res., 34, 137–146, https://doi.org/10.1007/s11001-013-9190-8, 2013. a
Melini, D. and Piersanti, A.: Impact of global seismicity on sea level change assessment, J. Geophys. Res.-Solid, 111, B03406, https://doi.org/10.1029/2004JB003476, 2006. a
Michael, P. J. and Cornell, W. C.: Influence of spreading rate and magma supply on crystallization and assimilation beneath mid-ocean ridges: Evidence from chlorine and major element chemistry of mid-ocean ridge basalts, J. Geophys. Res.-Solid, 103, 18325–18356, https://doi.org/10.1029/98JB00791, 1998. a
Moritz, H.: Advanced Physical Geodesy, Sammlung Wichmann: Neue Folge: Buchreihe, Wichmann, ISBN 9780856261954, 1980. a
Mortimer, Z.: Gravity Vertical Gradient Measurements for the Detection of Small Geologic and Anthropogenic Forms; discussion, Geophysics, 42, 1484–1485, https://doi.org/10.1190/1.1440812, 1977. a
Muhammad, S., Zulfiqar, A., and Muhammad, A.: Vertical gravity anomaly gradient effect of innermost zone on geoid-quasigeoid separation and an optimal integration radius in planar approximation, Appl. Geomat., 2, 9–19, https://doi.org/10.1007/s12518-010-0015-z, 2010. a
Novák, P., Tenzer, R., Eshagh, M., and Bagherbandi, M.: Evaluation of gravitational gradients generated by Earth's crustal structures, Comput. Geosci., 51, 22–33, https://doi.org/10.1016/j.cageo.2012.08.006, 2013. a
Oruç, B.: Edge Detection and Depth Estimation Using a Tilt Angle Map from Gravity Gradient Data of the Kozaklı-Central Anatolia Region, Turkey, Pure Appl. Geophys., 168, 1769–1780, https://doi.org/10.1007/s00024-010-0211-0, 2011. a
Panet, I., Pajot-Métivier, G., Greff-Lefftz, M., Métivier, L., Diament, M., and Mandea, M.: Mapping the mass distribution of Earth's mantle using satellite-derived gravity gradients, Nat. Geosci., 7, 131–135, https://doi.org/10.1038/ngeo2063, 2014. a
Pavlis, N. K., Holmes, S. A., Kenyon, S. C., and Factor, J. K.: The development and evaluation of the Earth Gravitational Model 2008 (EGM2008), J. Geophys. Res.-Solid 117, B04406, https://doi.org/10.1029/2011JB008916, 2012. a
Poland, M. P. and Carbone, D.: Insights into shallow magmatic processes at Kīlauea Volcano, Hawai'i, from a multiyear continuous gravity time series, J. Geophys. Res.-Solid, 121, 5477–5492, https://doi.org/10.1002/2016JB013057, 2016. a
Rao, D. G., Krishna, K. S., Neprochnov, Y. P., and Grinko, B. N.: Satellite gravity anomalies and crustal features of the Central Indian Ocean Basin, Curr. Sci., 86, 948–957, 2004. a
Romaides, A. J., Battis, J. C., Sands, R. W., Zorn, A., Jr, D. O. B., and DiFrancesco, D. J.: A comparison of gravimetric techniques for measuring subsurface void signals, J. Phys. D, 34, 433–443, https://doi.org/10.1088/0022-3727/34/3/331, 2001. a
Rummel, R., Yi, W., and Stummer, C.: GOCE gravitational gradiometry, J. Geod., 85, 777–790, https://doi.org/10.1007/s00190-011-0500-0, 2011. a
Sandwell, D. T.: Antarctic marine gravity field from high-density satellite altimetry, Geophys. J. Int., 109, 437–448, https://doi.org/10.1111/j.1365-246X.1992.tb00106.x, 1992. a
Sandwell, D. T. and Smith, W. H. F.: Marine gravity anomaly from Geosat and ERS 1 satellite altimetry, J. Geophys. Res.-Solid, 102, 10039–10054, https://doi.org/10.1029/96JB03223, 1997. a
Sandwell, D. T. and Smith, W. H. F.: Global marine gravity from retracked Geosat and ERS-1 altimetry: Ridge segmentation versus spreading rate, J. Geophys. Res.-Solid, 114, B01411, https://doi.org/10.1029/2008JB006008, 2009. a, b
Sandwell, D. T. and Smith, W. H. F.: Slope correction for ocean radar altimetry, J. Geod., 88, 765–771, https://doi.org/10.1007/s00190-014-0720-1, 2014. a
Sandwell, D. T., Smith, W. H. F., Gille, S., Kappel, E., Jayne, S., Soofi, K., Coakley, B., and Géli, L.: Bathymetry from space: Rationale and requirements for a new, high-resolution altimetric mission, Comptes Rendus. Géoscience, 338, 1049–1062, https://doi.org/10.1016/j.crte.2006.05.014, 2006. a
Sandwell, D. T., Müller, R. D., Smith, W. H. F., Garcia, E., and Francis, R.: New global marine gravity model from CryoSat-2 and Jason-1 reveals buried tectonic structure, Science, 346, 65–67, https://doi.org/10.1126/science.1258213, 2014. a, b
Schwatke, C., Dettmering, D., Bosch, W., and Seitz, F.: DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry, Hydrol. Earth Syst. Sci., 19, 4345–4364, https://doi.org/10.5194/hess-19-4345-2015, 2015. a
Silvestrin, P., Aguirre, M., Massotti, L., Leone, B., Cesare, S., Kern, M., and Haagmans, R.: The Future of the Satellite Gravimetry After the GOCE Mission, in: Geodesy for Planet Earth, Springer, Berlin, Heidelberg, 223–230, ISBN 978-3-642-20338-1, https://doi.org/10.1007/978-3-642-20338-1_27, 2012. a
Skourup, H., Farrell, S. L., Hendricks, S., Ricker, R., Armitage, T. W. K., Ridout, A., Andersen, O. B., Haas, C., and Baker, S.: An Assessment of State-of-the-Art Mean Sea Surface and Geoid Models of the Arctic Ocean: Implications for Sea Ice Freeboard Retrieval, J. Geophys. Res.-Oceans, 122, 8593–8613, https://doi.org/10.1002/2017JC013176, 2017. a
Smith, W. H. F. and Sandwell, D. T.: Global Sea Floor Topography from Satellite Altimetry and Ship Depth Soundings, Science, 277, 1956–1962, https://doi.org/10.1126/science.277.5334.1956, 1997. a, b
Stray, B., Lamb, A., Kaushik, A., Vovrosh, J., Rodgers, A., Winch, J., Hayati, F., Boddice, D., Stabrawa, A., Niggebaum, A., Langlois, M., Lien, Y.-H., Lellouch, S., Roshanmanesh, S., Ridley, K., de Villiers, G., Brown, G., Cross, T., Tuckwell, G., Faramarzi, A., Metje, N., Bongs, K., and Holynski, M.: Quantum sensing for gravity cartography, Nature, 602, 590–594, https://doi.org/10.1038/s41586-021-04315-3, 2022. a
Sulistioadi, Y. B., Tseng, K.-H., Shum, C. K., Hidayat, H., Sumaryono, M., Suhardiman, A., Setiawan, F., and Sunarso, S.: Satellite radar altimetry for monitoring small rivers and lakes in Indonesia, Hydrol. Earth Syst. Sci., 19, 341–359, https://doi.org/10.5194/hess-19-341-2015, 2015. a
van der Meijde, M., Pail, R., Bingham, R., and Floberghagen, R.: GOCE data, models, and applications: A review, Int. J. Appl. Earth Obs. Geoinf., 35, 4–15, https://doi.org/10.1016/j.jag.2013.10.001, 2015. a
Vermeer, M. and Rahmstorf, S.: Global sea level linked to global temperature, P. Natl. Acad. Sci. USA, 106, 21527–21532, https://doi.org/10.1073/pnas.0907765106, 2009. a, b
Vignudelli, S., Birol, F., Benveniste, J., Fu, L.-L., Picot, N., Raynal, M., and Roinard, H.: Satellite Altimetry Measurements of Sea Level in the Coastal Zone, Surv. Geophys., 40, 1319–1349, https://doi.org/10.1007/s10712-019-09569-1, 2019. a
Vigouroux, N., Williams-Jones, G., Chadwick, W., Geist, D., Ruiz, A., and Johnson, D.: 4D gravity changes associated with the 2005 eruption of Sierra Negra volcano, Galápagos, Geophysics, 73, WA29–WA35, https://doi.org/10.1190/1.2987399, 2008. a
Wan, X., Annan, R. F., and Ziggah, Y. Y.: Altimetry-Derived Gravity Gradients Using Spectral Method and Their Performance in Bathymetry Inversion Using Back-Propagation Neural Network, J. Geophys. Res.-Solid, 128, e2022JB025785, https://doi.org/10.1029/2022JB025785, 2023. a
Wang, B., Li, T., Deng, Z., and Fu, M.: Wavelet Transform Based Morphological Matching Area Selection for Underwater Gravity Gradient-Aided Navigation, IEEE T. Vehicul. Technol., 72, 3015–3024, https://doi.org/10.1109/TVT.2022.3218998, 2023. a
Wessel, P., Luis, J. F., Uieda, L., Scharroo, R., Wobbe, F., Smith, W. H. F., and Tian, D.: The Generic Mapping Tools Version 6, Geochem. Geophy. Geosy., 20, 5556–5564, https://doi.org/10.1029/2019GC008515, 2019. a
Yu, Z., Zhao, D., and Li, J.: Structure and dynamics of the Tonga subduction zone: New insight from P-wave anisotropic tomography, Earth Planet. Sc. Lett., 598, 117844, https://doi.org/10.1016/j.epsl.2022.117844, 2022. a
Yuan, J., Guo, J., Zhu, C., Li, Z., Liu, X., and Gao, J.: SDUST2020 MSS: a global mean sea surface model determined from multi-satellite altimetry data, Earth Syst. Sci. Data, 15, 155–169, https://doi.org/10.5194/essd-15-155-2023, 2023. a
Zhou, R., Liu, X., Li, Z., Sun, Y., Yuan, J., Guo, J., and Ardalan, A. A.: On performance of vertical gravity gradient determined from CryoSat-2 altimeter data over Arabian Sea, Geophys. J. Int., 234, 1519–1529, https://doi.org/10.1093/gji/ggad153, 2023. a
Zhou, R., Guo, J., Ya, S., Sun, H., and Liu, X.: SDUST2023VGGA, Zenodo [data set], https://doi.org/10.5281/zenodo.14177000, 2024. a, b
Zhu, C., Guo, J., Yuan, J., Li, Z., Liu, X., and Gao, J.: SDUST2021GRA: global marine gravity anomaly model recovered from Ka-band and Ku-band satellite altimeter data, Earth Syst. Sci. Data, 14, 4589–4606, https://doi.org/10.5194/essd-14-4589-2022, 2022. a
Zingerle, P., Pail, R., Gruber, T., and Oikonomidou, X.: The combined global gravity field model XGM2019e, J. Geod., 94, 66, https://doi.org/10.1007/s00190-020-01398-0, 2020. a, b, c
Short summary
SDUST2023VGGA is a high-resolution (1' × 1') model developed to map the ocean's vertical gradient of gravity anomaly. By using multidirectional mean sea surface data, it reduces the impact of ocean dynamics and provides detailed global gravity anomaly change rates. This model provides critical insights into seafloor structures and ocean mass distribution, contributing to research in marine geophysics and oceanography. The dataset is freely available on Zenodo.
SDUST2023VGGA is a high-resolution (1' × 1') model developed to map the ocean's vertical...
Altmetrics
Final-revised paper
Preprint