SDUST2021GRA: global marine gravity anomaly model recovered from Ka-band and Ku-band satellite altimeter data

Abstract

Abstract. With the launch of altimetry satellites with different observation frequencies and different survey missions, it is necessary to integrate multi-satellites altimeter data to establish a new global marine gravity anomaly model. Based on Ka-band sea surface heights (SSHs) from SARAL/AltiKA and Ku-band SSHs from other satellites (including HY-2A) in geodetic missions and exact repeat missions, the global marine gravity anomaly model of SDUST2021GRA on a 1′ × 1′ grid is derived. Gridded deflections of vertical (DOV) are determined from along-track geoid gradients by the least squares collocation method, in which the noise variances of along-track geoid gradients are obtained by the iteration method for Ka-band geodetic mission and by the SSH crossover discrepancies for other altimetry missions. SDUST2021GRA is recovered from the gridded DOVs by the inverse Vening Meinesz formula, and analyzed by comparing with the recognized marine gravity anomaly models of DTU17 and SIO V30.1. Finally, the accuracy of SDUST2021GRA, DTU17, and SIO V30.1 is assessed by preprocessed shipborne gravity anomalies. In conclusion, the differences between SDUST2021GRA and recognized models are small, indicating the reliability of SDUST2021GRA. The differences are mainly concentrated between −5 and 5 mGal, which accounts for more than 95 % of the total number. Assessed by shipborne gravity, the accuracy of SDUST2021GRA is 2.37 mGal globally, which is higher than that of DTU17 (2.74 mGal) and SIO V30.1 (2.69 mGal). The precision advantage of SDUST2021GRA is mainly concentrated in offshore areas. HY-2A-measured altimeter data have an important role on gravity anomaly recovery in areas with complex coastlines and many islands. SDUST2021GRA is concluded to reach an international advanced level for the altimeter-derived marine gravity model, especially in the offshore area. The SDUST2021GRA model data are freely available at https://doi.org/10.5281/zenodo.6668159 (Zhu et al., 2022).