Time-variable gravity field determination from GRACE Follow-On data using empirical noise modelling

Lasser, Martin; Meyer, Ulrich; Arnold, Daniel; Jäggi, Adrian (November 2023). Time-variable gravity field determination from GRACE Follow-On data using empirical noise modelling (Unpublished). In: 21st Swiss Geoscience Meeting 2023. Mendrisio, Switzerland. 17--18 November 2023.

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Over the last twenty years the satellite missions GRACE and its successor GRACE Follow-On have provided a unique insight into the time-variable components of the Earth's gravity field on a global scale. A lot of effort has been and is still put into understanding the measurement data but also different analysis concepts. In this contribution we study GRACE Follow-On satellite-to-satellite tracking using the inter-satellite K-band link and kinematic positions of the satellites as observations and pseudo-observations, respectively, to derive orbits and global gravity field models. A key component of any model is the accurate specification of its quality. In the case of gravity field modelling from satellite data typically a least-squares adjustment is performed to obtain model parameters representing the satellite's orbits and a snapshot of the Earth's gravity field. However, associated formal errors usually do not reflect the error level that could be expected, but provide much lower error estimates. We present an approach to come up with reasonable error estimates by empirically modelling the stochastic noise based on the 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 for the kinematic positions and in K-band range-rate residuals. We compare and validate the solutions that use empirical modelling with solutions which do not make use of any kind of empircal noise modelling by examining the stochastic behaviour of the respective post-fit residuals, by investigating areas where a low noise is expected and by inspecting the mass trend estimates in certain areas of global interest.

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