Abstract
With the CHAMP and GRACE satellite gravity missions and the upcoming GOCE mission, millions of gravity-related observations are being released to the geodetic community. In order to provide an optimal gravity model in a statistical sense, it is common practice to combine satellite-only normal equations with prior information derived from terrestrial data or from previous satellite missions in the form of an existing gravity model. The weighting could be derived from formal error estimates, but more often these are adjusted based on heuristics like inspection of the residuals or of subset solutions. In recent years, rigorous approaches based on variance component estimation techniques have been developed and enjoy increasing popularity. At the same time, these techniques aim to provide more realistic error assessments of the combination solutions.
The problem that all these approaches face is that typically systematic inconsistencies exist between the prior information and the information obtained from different observation systems, which may dominate over the random errors. In this contribution, we investigate different methods that can deal with inconsistencies: model augmentation to accommodate for a vector of deterministic inconsistencies, and (partial) downweighting in a variance-component estimation procedure. These methods are tested thoroughly and applied to combine CHAMP data and normal equations with the EGM96 model.
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van Loon, J.P., Kusche, J. (2007). Towards an optimal combination of satellite data and prior information. In: Tregoning, P., Rizos, C. (eds) Dynamic Planet. International Association of Geodesy Symposia, vol 130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49350-1_51
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DOI: https://doi.org/10.1007/978-3-540-49350-1_51
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