Abstract

A large number of time-series of monthly gravity fields derived from GRACE data provide users with a wealth of information on mass transport processes in the system Earth. The users are, however, left alone with the decision which time-series to analyze. Following the example of other well-known combination services provided by the geodetic community, the prototype of a combination service has been developed within the frame of the project EGSIEM (2015–2017) to combine the different time-series with the goal to provide a unique and superior product to the user community. Four associated analysis centers (ACs) of EGSIEM, namely AIUB, GFZ, GRGS and IfG, generated monthly gravity fields which were then combined using the different normal equations (NEQs). But the relative weights determined by variance component estimation (VCE) on the NEQ level do not lead to an optimal combined product due to the different processing strategies applied by the individual ACs. We therefore resort to VCE on the solution level to derive relative weights that are representative of the noise levels of the individual solutions. These weights are then applied in the combination on the NEQ level. Prior to combination, empirical scaling factors that are based on pairwise combinations of NEQs are derived to balance the impact of the NEQs on the combined solution. We compare the processing approaches of the different ACs and introduce quality measures derived either from the differences w.r.t. the monthly means of the individual gravity fields or w.r.t. a deterministic signal model. After combination, the gravity fields are validated by comparison to the official GRACE SDS RL05 time-series and the individual contributions of the associated ACs in the spectral and the spatial domain. While the combined gravity fields are comparable in signal strength to the individual time-series, they stand out by their low noise level. In terms of noise, they are in 90% of all months as good or better than the best individual contribution from IfG and significantly less noisy than the official GRACE SDS RL05 time-series.

Highlights

  • Earth gravity fields based on the observations of the Gravity Recovery And Climate Experiment [GRACE, Tapley et al (2004)] satellite mission are an important source of information on temporal mass variations in the systemGraz, Austria 3 Helmholtz Centre Potsdam, GFZ German Research Center for Geosciences, Potsdam, Germany 4 Groupe de Recherche de Géodésie Spatiale, Centre national d’études spatiales, Toulouse, FranceEarth (Wouters et al 2014)

  • As long as no signal biases impede the combination, the fieldwise weights derived by variance component estimation (VCE) on the solution level provide a robust quality indicator for the monthly gravity fields provided by the EGSIEM analysis centers (ACs)

  • Within the EGSIEM project, originally only monthly gravity fields for the two years 2006–2007 were re-processed according to the EGSIEM standards and combined on the normal equations (NEQs) level

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Summary

Introduction

Earth gravity fields based on the observations of the Gravity Recovery And Climate Experiment [GRACE, Tapley et al (2004)] satellite mission are an important source of information on temporal mass variations in the system. One may expect that the combination of monthly gravity fields from different ACs based on different background models and using different parameterizations reduces the analysis noise, all gravity fields are derived from the same observations and no new information is introduced in the combination. The error estimates of the unknown parameters differ considerably between the ACs, and classical VCE converges to nonoptimal results This problem is encountered in the combination of GRACE gravity fields. A direct application of this technique, as it is presented in Appendix A.1, leads to suboptimal results, because the inversion of the NEQs provided by the ACs does not yield realistic covariance matrices of errors in model parameters.

The EGSIEM combination service for monthly gravity fields
Noise assessment
Individual time-series
G Mi G Mref
Empirical scaling to balance the impact of NEQs on the combination
Relative weights based on solution noise
Evaluation of combined monthly solutions
Conclusions and outlook
Relative weighting based on VCE on the NEQ level
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