Lasser, Martin (2022). Noise Modelling for GRACE Follow-On Observables in the Celestial Mechanics Approach. (Thesis). Universität Bern, Bern

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Abstract

A key to understanding the dynamic system Earth in its current state is the continuous observation of its time-variable gravity field. The satellite missions Gravity Recovery And Climate Experiment (GRACE) and its successor GRACE Follow-On take an exceptional position in sensing these time-variable components because of their unique observing concept, which is based on ultra precise measurements of distance changes between a pair of satellites separated by a few hundred kilometres. These observations allow for a modelling of the Earth’s gravity field, typically on a basis of monthly snapshots. One of the key components of any model is the accurate specification of its quality. In temporal gravityfield modelling from GRACE Follow-On data one has to cope with several noise sources contaminating not only the observations but also the observation equations via mis-modellings in the underlying background force models. When employing the Celestial Mechanics Approach (CMA), developed at the Astronomical Institute of the University of Bern (AIUB), for gravity field modelling from satellite data a Least-Squares Adjustment (LSQA) is performed to compute monthly models of the Earth’s gravity field. However, as a consequence of the various contaminations with noise, the jointly estimated formal errors usually do not reflect the error level that could be expected but provides much lower error estimates. One way to deal with such deficiencies in the observations and modelling is to extend the parameter space, i.e., the model, by additional quantities, such as pseudo-stochastic parameters, which are co-estimated in the LSQA. These parameters are meant to absorb any kind of noise while retaining the signal in the gravity field and orbit parameters. In the CMA such pseudo-stochastic parameters are typically set-up as Piece-wise Constant Accelerations (PCAs) in regular intervals of e.g., 15 min. The stochastic behaviour of these parameters is unknown because they reflect an accumulation of a variety of noise sources. In the CMA fictitious artificial zero-observations are appended to the vector of observations together with an empirically determined variance to introduce a stochastic model for the PCAs. In order to also co-estimate a stochastic model for the pseudo-stochastic parameters in the LSQA Variance Component Estimation (VCE) is used in this work as a well established tool to assign variance components to individual groups of observations. In the simplest case the magnitude of the constraints of the pseudo-stochastic parameters can be determined fully automatically. Additionally, VCE is applied as an on-the-fly data reviewing method to account for gross outliers in the observations. Addressing the problem of noise contamination from the point of the GRACE Follow-On satellite mission’s observations, this work presents the incorporation of several noise models into the CMA to not only obtain high-quality time-variable gravity field models but also an accurate description of their stochastic behaviour. The noise models applied stem from pre-launch simulations or the formal covariance propagation of a kinematic point positioning process. Furthermore, the derivation and application of empirical noise models obtained from post-fit residuals between the final GRACE Follow-On orbits, that are co-estimated together with the gravity field, and the observations, expressed in position residuals to the kinematic positions and in the inter-satellite link range-rate residuals, is implemented. Additionally, the current operational processing scheme of GRACE Follow-On data is expounded, including the normal equation handling in the CMA with BLAS and LAPACK routines. All implementations are compared and validated with the operational GRACE Follow-On processing at the AIUB by examining the stochastic behaviour of the respective post-fit residuals and by investigating areas on Earth where a low noise is expected. Finally, the influence and behaviour of the different noise modelling techniques is investigated in a combination of monthly gravity fields computed by various institutions as it is done by the Combination Service for Time-variable Gravity fields (COST-G).

Item Type:	Thesis
Dissertation Type:	Single
Date of Defense:	27 June 2022
Subjects:	500 Science > 520 Astronomy
Institute / Center:	08 Faculty of Science > Institute of Astronomy
Depositing User:	Sarah Stalder
Date Deposited:	23 Feb 2023 14:16
Last Modified:	27 Jun 2023 22:25
URI:	https://boristheses.unibe.ch/id/eprint/4127

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