Stochastic model validation of satellite gravity data: A test with CHAMP pseudo-observations

Abstract

The energy balance approach is used for a statistical assessment of CHAMP orbits, data and gravity models. It is known that the quality of GPSderived orbits varies and that CHAMP accelerometer errors are difficult to model. The stochastic model of the in-situ potential values from the energy balance is therefore heterogeneous and it is unclear if it can be described accurately using a priori information. We have estimated parameters of this stochastic model in an iterative variance-component estimation procedure, combined with an outlier rejection method. This means we solve simultaneously for a spherical harmonic model, for polynomial coefficients absorbing accelerometer drift, for sub-daily noise variance components, and for a variance parameter that controls the influence of an apriori gravity model (EGM96). We develop here, for the first time, a fast Monte Carlo variant of the Minimum Norm Quadratic Unbiased Estimator (MINQUE) as an alternative for the fast Monte Carlo Maximum Likelihood VCE that we have introduced earlier. In this way, spurious data sets could be indicated and downweighted in the least-squares estimation of the unknown parameters. Using only 299 days of CHAMP kinematic orbit data, the quality of the estimated global gravity model was found comparable to the EIGEN-3P model. Monte Carlo Variance Components Estimation appears to be a valid method to estimate the stochastic model of satellite gravity data and thus improves the least squares solution considerably.