A Global Gravity Field Model to Degree/order 720 Combining GOCE and Complimentary Terrestrial and Satellite Data
Fecher, Thomas; Pail, Roland; Gruber, Thomas
Institute of Astronomical and Physical Geodesy, TU München, GERMANY
Global gravity field models are a key instrument for many scientific disciplines dealing with the description of static and dynamic processes on planet Earth. Oceanographers, for instance, need a geoid solution as a reference surface, and geophysicists use gravity field information for constraining density models of the Earth's interior. To fulfil the various requirements of all users, gravity models should be of high accuracy and high spatial resolution.
ESA's dedicated gravity field mission GOCE measures the long to medium wavelength gravity signal content more accurately than all previous satellite-based observation methods. However, since the omission error of satellite-only models is still in the order of about 30 cm, the GOCE observations have to be combined with complimentary gravity field data to achieve an optimal solution and to meet the user requirements. Data of the satellite gravity mission GRACE contribute with high accuracy to the long wavelength part, while terrestrial and altimetric gravity observations enable a high spatial and spectral resolution of the gravity field model.
One key challenge to use real measured complementary data sources lies in the fact, that terrestrial and altimetric gravity information is not very homogeneous and consistent. Additionally, terrestrial gravity data is restricted or not available for some areas. Therefore, also the combination process is a challenging task, and specific strategies have to be applied to fill these observation gaps. Furthermore, high spectral gravity field determination puts high demands on computer resources as full normal equations systems become very large, and parallel methods become necessary.
A gravity field model based on full normal equation of all observation groups up to d/o 720 has been retrieved at IAPG. Special emphasis is given to the stochastic modelling of all involved satellite and gravity data types, the related optimum relative weighting in the course of the combination, as well as rigorous error propagation, resulting in a full variance-covariance matrix of the gravity field coefficients. The resulting gravity field model is validated by independent external gravity field information, such as GPS/levelling observations.