GOCO06s – a satellite-only global gravity field model

Abstract

Abstract. 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: https://doi.org/10.5880/ICGEM.2019.002 (Kvas et al., 2019b). 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).

Determination and Combination of Monthly Gravity Field Time Series from Kinematic Orbits of GRACE, GRACE-FO and Swarm

“…The time series are derived as follows: (i) monthly SH coefficients are transformed to EWH (Wahr et al 1998), subtracted by the signal of GOCO06s (Kvas et al 2021) to remove the static gravity field part, and smoothed by a 500 km Gaussian filter, (ii) for each month, these SH coefficients are evaluated up to d/o 20 to compute EWH values on a regular 0:5 ı 0:5 ı grid covering the selected regions, (iii) values are averaged over these regions to derive monthly area-weighted mean EWH estimates (i.e., weighted by the cosine of the latitude of the grid cells). In Fig.…”

Choice of observation type affects Bayesian calibration of ice sheet model projections

“…Mass change over the calibration time span of 2007 -2015 is calculated from the observed trends per mascon over the GrIS. Mass change uncertainties are calculated from statistics of the differences between the GSFC high-spatialresolution mascon trend solution (Loomis et al, 2019) and the Gravity Observation Combination release 06 (GOCO-06) spherical harmonic model (Kvas et al, 2021). The mass change uncertainties were assessed separately over the margin and the interior of the GrIS and the resulting uncertainties are 4 cm w.e.…”

The Geoid and Quasigeoid of São Paulo State Using the Updated Gravimetric Data and the 2018 BVRF

“…The Experimental Gravity Field Model (XGM2019e Zingerle et al 2020) was chosen due to the consistency of the model with the GPS determinations on the levelling network, having RMS difference of approximately 30 cm for the state of São Paulo (Gruber et al 2019). This model was combined with satellite data from the GOCO06S model (Kvas et al 2021) and terrestrial gravimetric measurements. The topography of the continent is derived from the EARTH2014 model (Hirt and Rexer 2015) and the ocean from the DTU13 model.…”