Coastal gravity field refinement by combining airborne and ground-based data

Abstract

Gravity field modelling in coastal region faces challenges due to the degradation of the quality of altimeter data and poor coverage of gravimetric measurements. Airborne gravimetry can provide seamless measurements both onshore and offshore with uniform accuracies, which may alleviate the coastal zone problem. We study the role of airborne data for gravity field recovery in a coastal region, and the possibility to validate coastal gravity field model against recent altimetry data (CryoSat-2, Jason-1, and SARAL/Altika). Moreover, we combine airborne and ground-based gravity data for regional refinement, and quantify and validate the contribution introduced by airborne data. Numerical experiments in the Gippsland Basin over the southeastern coast of Australia show that the effects introduced by airborne gravity data appear as small-scale patterns on the centimetre scale in terms of quasi-geoid heights. Numerical results demonstrate that the combination of airborne data improves the coastal gravity field, and the recent altimetry data can be potentially used to validate the high-frequency signals introduced by airborne data. The validation against recent altimetry data demonstrates that the combination of airborne measurements improves the coastal quasigeoid, by ~5 mm, compared with a model computed from terrestrial and altimetry-derived gravity anomalies alone. These results show that the recently released altimetry data with relatively denser spatial resolutions and higher accuracies than older altimeter data may be beneficial for gravity field model assessment in coastal areas.

Refinement of Mean Dynamic Topography Over Island Areas Using Airborne Gravimetry and Satellite Altimetry Data in the Northwestern South China Sea

“…The GGM-and RTM-components are removed from local gravimetric observations to compute the residuals, based on which the residual quasi-geoid is modeled. The details regarding how we compute the RTM corrections are seen in Wu, Zhou et al, 2017, Wu et al, 2019 The residual gravity field is parameterized with Poisson wavelets, the order of which is fixed at three (Panet et al, 2011). The computational region spans 15.5°N to 18.2°N latitude and 111.0°E to 113.3°E longitude, where Poisson wavelets are placed on a Fibonacci grid located 5 km beneath the topography.…”

An assessment of recently released high-degree global geopotential models based on heterogeneous geodetic and ocean data

“…However, prominent discrepancy between GGM-derived MDT and synthetic/ocean model is observed, particularly in northern SCS, by a magnitude up to 10 cm, where GGM-derived MDTs have smaller dynamic topography. Extreme values existing in GGM-derived MDT along the coast of Hainan, eastern coast of Vietnam, and western coast Brunei and Malaysia, are identi ed as outliers, due to the uncorrected errors in MSS and in quasi-geoid (e.g., Hipkin et al 2004;Wu et al 2017bWu et al , 2019. The remaining errors in MSS are mainly due to the orbit errors and errors in various range corrections (e.g., Andersen et al 2009).…”

“…It is noticeable that GGM notoriously suffers from the coastal problem, where the errors in MDT are magni ed in ocean current computation, since the geostrophic velocities are computed as the gradients of MDT. However, the coastal problem can be mitigated by using the recent altimeter data (e.g., Ophaug et al 2015;Idžanović et al 2017) and airborne gravimetric survey (e.g., Hwang et al 2006;Wu et al 2019).…”

“…6. While, coastal quasi-geoid suffers from the scarcity of gravimetric data (e.g., shipborne/airborne data), degraded quality of altimetry-derived data, and bias/tilt among different data sets (e.g., Huang et al 2017;Wu et al 2019). The EGM2008 commission errors, composed of the low-degree errors estimated using a satellite-only model through error propagation and high-degree ones computed through a surface integral formula using surface gravity data (Pavlis et al 2012), are seen in Appendix A.…”

“…The uncertainty in GGM may cause strong oscillations up to the magnitude of decimeter level, particularly in regions that only ll-in data were used in GGM development, and this remains a major obstacle to oceanographic studies and geophysical investigations (McAdoo et al 2013;Skourup et al 2017;Fecher et al 2017). Moreover, combined GGM that computed by merging altimetry-derived data suffer from the coastal problem (e.g., Huang et al 2017;Wu et al 2019), since the altimeter data contain larger errors close to coast/island than in open seas, due to the severely contaminated waveforms and deteriorated geophysical corrections (Deng and Featherstone 2006;Andersen and Scharroo 2011;Abulaitijiang et al 2015).…”