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

[摘要] With the launch of altimetry satellites with differentobservation frequencies and different survey missions, it is necessary tointegrate multi-satellites altimeter data to establish a new global marinegravity anomaly model. Based on Ka-band sea surface heights (SSHs) from SARAL/AltiKA and Ku-bandSSHs from other satellites (including HY-2A) in geodetic missions and exactrepeat missions, the global marine gravity anomaly model of SDUST2021GRA ona 1 ′   ×  1 ′ grid is derived. Gridded deflections ofvertical (DOV) are determined from along-track geoid gradients by theleast squares collocation method, in which the noise variances ofalong-track geoid gradients are obtained by the iteration method for Ka-bandgeodetic mission and by the SSH crossover discrepancies for other altimetrymissions. SDUST2021GRA is recovered from the gridded DOVs by the inverseVening Meinesz formula, and analyzed by comparing with the recognized marinegravity anomaly models of DTU17 and SIO V30.1. Finally, the accuracy ofSDUST2021GRA, DTU17, and SIO V30.1 is assessed by preprocessed shipbornegravity anomalies. In conclusion, the differences between SDUST2021GRA andrecognized models are small, indicating the reliability of SDUST2021GRA. Thedifferences are mainly concentrated between −5 and 5 mGal, whichaccounts for more than 95 % of the total number. Assessed by shipbornegravity, the accuracy of SDUST2021GRA is 2.37 mGal globally, which ishigher than that of DTU17 (2.74 mGal) and SIO V30.1 (2.69 mGal). Theprecision advantage of SDUST2021GRA is mainly concentrated in offshoreareas. HY-2A-measured altimeter data have an important role on gravityanomaly recovery in areas with complex coastlines and many islands.SDUST2021GRA is concluded to reach an international advanced level for thealtimeter-derived marine gravity model, especially in the offshore area. TheSDUST2021GRA model data are freely available at https://doi.org/10.5281/zenodo.6668159 (Zhu et al., 2022).

[学科分类] 眼科学