Abstract
In this paper, we will discuss robust estimation and robust re-weighting techniques for the analysis of data from the space-gravimetric missions (CHAMP, GRACE, and GOCE in the near future). The stochastic and the functional models of these data are not perfectly known beforehand, and the data quality may vary considerably in time. We have used variance component estimation to re-weight the data sets, using a fast Monte-Carlo-type algorithm. In general, the data sets contain a small amount of outliers, which, if not properly treated, will contaminate the spherical harmonic solution. Moreover will they affect an automatic re-weighting scheme, which in turn may wrongly down-weight good data. This study aims at the estimation, validation and improvement of the stochastic model and the spherical harmonic solution, using the VCE technique in combination with a robust treatment of the outliers in the data. Unlike diagnostic methods like the three-sigma rule or data snooping, robust estimation aims not at removing outliers but seeks to minimize their impact. We will assume different probability functions of the data, including Huber’s distribution, to account for the presence of outliers. Special attention will be given to the robustification of the estimation procedure in conjunction with the computation of the variance components, where little theoretical results are known. The method has been tested with real CHAMP satellite gravity data, derived from the energy balance approach, and the results were slightly better than those from conventional diagnostic outlier detection. Finally, the satellite-only solution was combined with a prior model using VCE.
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Loon, J.v. (2008). Robust Estimation and Robust Re-Weighting in Satellite Gravity Modelling. In: Xu, P., Liu, J., Dermanis, A. (eds) VI Hotine-Marussi Symposium on Theoretical and Computational Geodesy. International Association of Geodesy Symposia, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74584-6_7
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DOI: https://doi.org/10.1007/978-3-540-74584-6_7
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