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GRACE/GRACE-FO inter-satellite K-band range-rates are the main observable for the determination of the monthly solutions of the Earth’s gravity field. The range-rates are sensitive not only to the mass distribution on the Earth, and as a consequence, the relative motion of both GRACE and GRACE-FO satellites respectively, but also to the relative orientation of the satellites and consequently on the attitude handling, which relies on the star camera observations, subject to blinding by the sun and moon. In consequence the range-rates observations exhibit a number of difficult to identify error sources and efficient screening is not trivial. In this contribution, we apply novel outlier detection methods such as Isolation Forest and Local Outlier Factor (collectively known under the term machine learning) to flag outliers in an unsupervised fully automated way. We apply both techniques to the post-fit residuals of monthly, common orbit and gravity field determination processings, combined with the geographical position of each observation. The flagged outliers are investigated for local geographical correlations to distinguish between unfitted signal from gravitational sources and artefacts caused by the satellites’ instrumentation. For that we train a Mutual Information Neural Network, learning the mutual information between the post-fit residuals and the geographical location. The outliers flagged as artefacts are removed from the original inter-satellite range-rate data and orbit and gravity field determination processing is repeated to investigate the benefit of the proposed screening procedures.

Satellite Laser Ranging (SLR) data can be processed to estimate geodetic parameters, i.e., the Earth Rotation Parameters (ERPs), station coordinates or low-degree spherical harmonic coefficients of the Earth's gravity field. The satellite orbits are determined in 7-day arcs together with geodetic and instrument parameters. However, the quality of these orbits strongly depends on the accuracy of the background models, e.g., the a priori Earth's gravity field. In this study, SLR analyses are validated, where different a priori gravity field models are used. These include static and time-variable products provided by the Center for Space Research (CSR), the International Laser Ranging Service (ILRS) and the Combination Service for Time-variable Gravity Fields (COST-G). The impact of the background models on the SLR data processing is analyzed by comparing the estimated parameters with internal and external quality metrics., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

In recent years, monitoring of changes in the Earth’s gravity field has been carried out mainly by the Gravity Recovery And Climate Experiment (GRACE) and its successor GRACE Follow-On. However, very little information is available on the temporal evolution of the Earth's gravity field prior to the launch of the GRACE mission and through gaps in GRACE K-band data between 2010 and 2019. Fortunately, GRACE and GRACE Follow-On are not the only missions for the Earth's gravity field recovery. For the mass redistribution characteristics in large scales, we may employ Satellite Laser Ranging (SLR) observations to geodetic satellites.In this study, we derive gravity field changes for Greenland and Antarctica based on SLR data and compare the results to the GRACE, GRACE Follow-On, and Ice sheet Mass Balance Inter-comparison Exercise (IMBIE) data. We propose a set of long-term, continuous solutions, in which the gravity field is expanded up to degree and order 10/10 with a monthly resolution from 1/1995 to 10/2021. We show results from the unconstrained solution, as well as the solution that has been decomposed into normal equations expanded up to degree and order 4, 6, 8, and 10 and stacked, taking advantage of stability and better resolution from lowest and higher-degree expansion. We also propose different types of weighting, degree and order-specific constraining, and different data lengths. The SLR-based solutions are evaluated based on the noise on the oceans and differences to IMBIE and GRACE results for Greenland and Antarctica, which are identified by substantial ice mass loss.

GRACE and GRACE-Follow On time series suffer from a data gap of about one year and there is the need for an intermediate technique that will bridge the gap between the two missions.The combination of high-low satellite-to-satellite tracking (hlSST) of low-Earth orbiting satellites by GNSS in combination with SLR is the most promising candidate and will allow 1) for a continued and uninterrupted time series of mass observations and 2) to compare, cross-validate and link the two time series. SLR is known to provide highest quality time-variable gravity for the very low degrees (2-5) and hlSST is able to provides a higher spatial resolution at a lower precision in the very low degrees. Here, we present a complete series of time-variable gravity field solution based on high-low satellite-to-satellite tracking and satellite laser ranging that covers the time from 2003 till 2022 including the gap period between the end of the GRACE mission in 2017 and the first available solutions of GRACE-Follow On in 2018.&nbsp, The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

The Swarm satellites, designed for observing Earth's magnetic field, also gather GPS data with enough precision to study Earth's time-varying gravity field with a resolution of approximately 1500 km. The models, which span from 2014 to the present and don't require any additional gravimetric data or prior information, are updated monthly. These data fill in the gaps between the GRACE and GRACE-FO missions, as well as any other short interruptions in their series. After more than 9 years of successful operation, the Swarm satellites are still in good health. The planned future mission profile allows for many more years of operation, such that Swarm helps to guarantee continuous observation of Earth’s time variable gravity field.A consortium of international research institutes supported by theEuropean Space Agency and the International Combination Service for Time-variable Gravity Fields (COST-G), publishes the gravity field models every 3 months at the Swarm Data Access server(https://swarm-diss.eo.esa.int)and the International Centre for Global Earth Models(http://icgem.gfz-potsdam.de/series/02_COST-G/Swarm). These models are based on “individual” gravity field models estimated by each institute using different gravity inversion strategies, which are combined using Variance Component Estimation to produce the published models.We showcase the geophysical signals represented in our models across large hydrological basins, compare them with GRACE and GRACE-FO results and the discrepancies over deep oceans areas. Our analysis encompasses the time frame of the GRACE/GRACE-FO gap and highlights the significant importance of the Swarm satellites in bridging the gap caused by the absence of low-low satellite-to-satellite tracking data., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Over the last twenty years the satellite missions GRACE and GRACE Follow-On have provided a unique insight into Earth’s gravity field. A lot of effort has been and is still put into understanding the measurement data to supply the analysis with up-to-date and consistent Level-1B data products. The Astronomical Institute of the University of Bern (AIUB) has published several time series of monthly GRACE gravity fields and is currently operationally computing GRACE Follow-On gravity field models, which are incorporated into the Combination Service for Time-variable Gravity fields (COST-G). In order to bring the time series to a consistent and up-to-date level, we re-process both GRACE and GRACE Follow-On with the newest Level-1B data, incorporate the most recent AOD1B-RL07 de-aliasing models, and possibly update the ocean tides background force model. Furthermore, we revise our processing strategy for consistency and efficiency. In an attempt to come up with reasonable error estimates, we model the stochastic noise of the data with an empirical description of the noise based on the post-fit residuals between the final satellite orbits, that are co-estimated together with the gravity field, and the observations. We compare and validate the new solutions by examining the stochastic behaviour of post-fit residuals, by investigating areas where low noise is expected and by inspecting mass trend estimates. Finally, we address the influence in a combination of monthly gravity fields based on different approaches as it is done by COST-G and make use of the quality control tools used in the frame of COST-G., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)