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GOCO06s is the latest satellite-only global gravity field model computed by the GOCO (Gravity Observation Combination) project. It is based on over a billion observations acquired over 15 years from 19 satellites with different complementary observation principles. This combination of different measurement techniques is key in providing consistently high accuracy and best possible spatial resolution of the Earth's gravity field. The motivation for the new release was the availability of reprocessed observation data for the Gravity Recovery and Climate Experiment (GRACE) and Gravity field and steady-state Ocean Circulation Explorer (GOCE), updated background models, and substantial improvements in the processing chains of the individual contributions. Due to the long observation period, the model consists not only of a static gravity field, but comprises additionally modeled temporal variations. These are represented by time-variable spherical harmonic coefficients, using a deterministic model for a regularized trend and annual oscillation. The main focus within the GOCO combination process is on the proper handling of the stochastic behavior of the input data. Appropriate noise modeling for the observations used results in realistic accuracy information for the derived gravity field solution. This accuracy information, represented by the full variance–covariance matrix, is extremely useful for further combination with, for example, terrestrial gravity data and is published together with the solution. The primary model data consisting of potential coefficients representing Earth's static gravity field, together with secular and annual variations, are available on the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/ , last access: 11 June 2020). This data set is identified with the following DOI: 10.5880/ICGEM.2019.002. Supplementary material consisting of the full variance–covariance matrix of the static potential coefficients and estimated co-seismic mass changes is available at https://ifg.tugraz.at/GOCO (last access: 11 June 2020). [ABSTRACT FROM AUTHOR]

In the framework of the COmbination Service for Time-variable Gravity fields (COST-G) gravity field solutions from different analysis centres are combined to provide a consolidated solution of improved quality and robustness to the user. As in many other satellite-related sciences, the correct application of background models plays a crucial role in gravity field determination. Therefore, we publish a set of data of various commonly used forces in orbit and gravity field modelling (Earth's gravity field, tides etc.) evaluated along a one day orbit arc of GRACE, together with auxiliary data to enable easy comparisons. The benchmark data is compiled with the GROOPS software by the Institute of Geodesy (IfG) at Graz University of Technology. It is intended to be used as a reference data set and provides the opportunity to test the implementation of these models at various institutions involved in orbit and gravity field determination from satellite tracking data. In view of the COST-G GRACE and GRACE Follow-On gravity field combinations, we document the outcome of the comparison of the background force models for the Bernese GNSS software from AIUB (Astronomical Institute, University of Bern), the EPOS software of the German Research Centre for Geosciences (GFZ), the GINS software, developed and maintained by the Groupe de Recherche de Géodésie Spatiale (GRGS), the GRACE-SIGMA software of the Leibniz University of Hannover (LUH) and the GRASP software also developed at LUH. We consider differences in the force modelling for GRACE (-FO) which are one order of magnitude smaller than the accelerometer noise of about 10-10 ms-2 to be negligible and formulate this as a benchmark for new analysis centres, which are interested to contribute to the COST-G initiative. [ABSTRACT FROM AUTHOR]

GOCO06s is the latest satellite-only global gravity field model computed by the GOCO (Gravity Observation Combination) project. It is based on over a billion observations acquired over 15 years from 19 satellites with different complementary observation principles. This combination of different measurement techniques is key in providing consistent high-accuracy and best possible spatial resolution of the Earth's gravity field. [ABSTRACT FROM AUTHOR]

In this article, we present a computationally efficient method to incorporate background model uncertainties into the gravity field recovery process. While the geophysical models typically used during the processing of GRACE data, such as the atmosphere and ocean dealiasing product, have been greatly improved over the last years, they are still a limiting factor of the overall solution quality. Our idea is to use information about the uncertainty of these models to find a more appropriate stochastic model for the GRACE observations within the least squares adjustment, thus potentially improving the gravity field estimates. We used the ESA Earth System Model to derive uncertainty estimates for the atmosphere and ocean dealiasing product in the form of an autoregressive model. To assess our approach, we computed time series of monthly GRACE solutions from L1B data in the time span of 2005 to 2010 with and without the derived error model. Intercomparisons between these time series show that noise is reduced on all spatial scales, with up to 25% RMS reduction for Gaussian filter radii from 250 to 300 km, while preserving the monthly signal. We further observe a better agreement between formal and empirical errors, which supports our conclusion that used uncertainty information does improve the stochastic description of the GRACE observables. [ABSTRACT FROM AUTHOR]

ITSG‐Grace2018 is a new series of GRACE‐only gravity field solutions based on reprocessed GRACE observation data (L1B RL03) and the latest atmosphere and ocean dealiasing product (AOD1B RL06). It includes unconstrained monthly and constrained daily solutions, as well as a high‐resolution static gravity field. Compared to the previous ITSG release, we implemented a number of improvements within the processing chain and use updated background models. In an effort to better model all known error sources, we propagate synthetic orientation uncertainties of the star camera assembly to the antenna offset correction for intersatellite ranging observations. This enables the disentanglement of the stationary noise of the K‐Band system and the nonstationary noise of the antenna offset correction. We further incorporated uncertainties of the atmosphere and ocean dealiasing product to reduce temporal aliasing effects. To mitigate errors in the applied ocean tide model, we used constrained GRACE estimates of selected tidal constituents as an additional background model. Variability over quiet ocean areas suggests a 27% to 46% lower noise level compared to the current spherical harmonic solutions of the official processing centers (300 km Gaussian filter applied). To ensure that the low noise floor is not accompanied by signal loss, we examined drainage basin averages, which showed consistent amplitudes with the official GRACE time series. These evaluations lead to the conclusion that ITSG‐Grace2018 is a state‐of‐the‐art GRACE time series which exhibits an excellent signal‐to‐noise ratio. Key Points: Overview of the ITSG‐Grace2018 processing strategyDiscussion and evaluation of selected processing stepsIntercomparison of ITSG‐Grace2018 and official L2 products [ABSTRACT FROM AUTHOR]

We develop a transfer function to determine in situ line‐of‐sight gravity difference (LGD) directly from Gravity Recovery and Climate Experiment (GRACE) range‐acceleration measurements. We first reduce GRACE data to form residual range‐acceleration referenced to dynamic orbit computed with a reference gravity field and nonconservative force data. Thus, the residuals and the corresponding LGD data reflect time‐variable gravity signals. A transfer function is designed based on correlation‐admittance spectral analysis. The correlation spectrum shows that residual range‐acceleration and LGD are near‐perfectly correlated for frequencies >5 cycles‐per‐revolution. The admittance spectrum quantifies that the LGD response to range‐acceleration is systematically larger at lower frequencies, due to the increased contribution of centrifugal acceleration. We find that the correlation and admittance spectra are stationary (i.e., are independent of time, satellite altitude, and gravity strength) and, therefore, can be determined a priori with high fidelity. We determine the spectral transfer function and the equivalent time domain filter. Using both synthetic and actual GRACE data, we demonstrate that in situ LGD can be estimated via the transfer function with an estimation error of 0.15 nm/s2, whereas the actual GRACE data error is around 1.0 nm/s2. We present an application of LGD data to surface water storage changes in large basins such as Amazon, Congo, Parana, and Mississippi by processing 11 years of GRACE data. Runoff routing models are calibrated directly using LGD data. Our technique demonstrates a new way of using GRACE data by forward modeling of various geophysical models and in‐orbit comparison with such GRACE in situ data. Key Points: In situ gravimetric observables can be computed from combination of GPS and KBR data of the GRACE satellite missionWe develop a transfer function for accurate estimation of LOS gravity difference directly from level‐1B residual KBR range‐acceleration dataThe in‐orbit comparison between in situ gravimetric data and models is a simple and powerful approach for hydrological model evaluations [ABSTRACT FROM AUTHOR]

Improvements of a global state-of-the art ocean tide model are identified and quantified by applying three independent approaches, namely (i) empirical ocean tide analysis of multimission altimeter data, (ii) evaluation of GRACE data and gravity field models and (iii) high resolution hydrodynamic modelling. Although these approaches have different capabilities to sense ocean tides they obtain results which are basically consistent one with each other. The analysis of altimeter data clearly identifies significant residual amplitudes over shallow water for all major diurnal and semidiurnal constituents and the non-linear tide M4. GRACE data and the time-series of monthly gravity field models exhibit—on a larger scale—residual ocean tide signals over much the same areas. The analysis of dynamic residuals of hydrodynamic modelling with data assimilation proves the validity of linear dynamics in the deep ocean and shows correlation of dynamic residuals with energy dissipation in these areas. [ABSTRACT FROM AUTHOR]