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<jats:title>Abstract</jats:title> <jats:p>The geoid, according to the classical Gauss–Listing definition, is, among infinite equipotential surfaces of the Earth’s gravity field, the equipotential surface that in a least squares sense best fits the undisturbed mean sea level. This equipotential surface, except for its zero-degree harmonic, can be characterized using the Earth’s global gravity models (GGM). Although, nowadays, satellite altimetry technique provides the absolute geoid height over oceans that can be used to calibrate the unknown zero-degree harmonic of the gravimetric geoid models, this technique cannot be utilized to estimate the geometric parameters of the mean Earth ellipsoid (MEE). The main objective of this study is to perform a joint estimation of <jats:italic>W</jats:italic><jats:sub>0</jats:sub>, which defines the zero datum of vertical coordinates, and the MEE parameters relying on a new approach and on the newest gravity field, mean sea surface and mean dynamic topography models. As our approach utilizes both satellite altimetry observations and a GGM model, we consider different aspects of the input data to evaluate the sensitivity of our estimations to the input data. Unlike previous studies, our results show that it is not sufficient to use only the satellite-component of a quasi-stationary GGM to estimate <jats:italic>W</jats:italic><jats:sub>0</jats:sub>. In addition, our results confirm a high sensitivity of the applied approach to the altimetry-based geoid heights, i.e., mean sea surface and mean dynamic topography models. Moreover, as <jats:italic>W</jats:italic><jats:sub>0</jats:sub> should be considered a quasi-stationary parameter, we quantify the effect of time-dependent Earth’s gravity field changes as well as the time-dependent sea level changes on the estimation of <jats:italic>W</jats:italic><jats:sub>0</jats:sub>. Our computations resulted in the geoid potential <jats:italic>W</jats:italic><jats:sub>0</jats:sub> = 62636848.102 ± 0.004 m<jats:sup>2</jats:sup> s<jats:sup>−2</jats:sup> and the semi-major and minor axes of the MEE, <jats:italic>a </jats:italic>= 6378137.678 ± 0.0003 m and <jats:italic>b </jats:italic>= 6356752.964 ± 0.0005 m, which are 0.678 and 0.650 m larger than those axes of GRS80 reference ellipsoid, respectively. Moreover, a new estimation for the geocentric gravitational constant was obtained as GM = (398600460.55 ± 0.03) × 10<jats:sup>6</jats:sup> m<jats:sup>3</jats:sup> s<jats:sup>−2</jats:sup>.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>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.</jats:p>