SDUST2023VGGA: A Global Ocean Vertical Gradient of Gravity Anomaly Model Determined from Multidirectional Data from Mean Sea Surface

Abstract. Satellite altimetry is a vital tool for global ocean observation, providing critical insights into ocean gravity and its gradient. Over the past six 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 multi-directional mean sea surface data. To address computational resource limitations, the global ocean is divided into ten sub-regions. In each sub-region, the DTU21 Mean Sea Surface (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 gravity field model from the original geoid, resulting in a residual geoid (short-wavelength). To ensure the accuracy of the VGGA calculations, a weighted least-squares method is employed using residual geoid data from a 17′ × 17′ area surrounding the computation point. This approach effectively accounts for the actual 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 VGGA and the SIO V32.1 model shows a mean of -0.08 Eötvös (E) and an RMS of 8.50 E, indicating a high degree of consistency across the 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 multi-directional mean sea surface data 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 dataset is freely available at https://doi.org/10.5281/zenodo.14177000 (Zhou et al., 2024).