The recent improvements in the Gravity Recovery And Climate Experiment (GRACE) tracking data processing at GeoForschungsZentrum Potsdam (GFZ) and Groupe de Recherche de Géodésie Spatiale (GRGS) Toulouse, the availability of newer surface gravity data sets in the Arctic, Antarctica and NorthAmerica, and the availability of a new mean sea surface height model from altimetry processing at GFZ gave rise to the generation of two new global gravity field models. The first, EIGEN-GL04S1, a satellite-only model complete to degree and order 150 in terms of spherical harmonics, was derived by combination of the latest GFZ Potsdam GRACE-only (EIGEN-GRACE04S) and GRGS Toulouse GRACE/LAGEOS (EIGEN-GL04S) mean field solutions. The second, EIGEN-GL04S1 was combined with surface gravity data from altimetry over the oceans and gravimetry over the continents to derive a new high-resolution global gravity field model called EIGEN-GL04C. This model is complete to degree and order 360 and thus resolves geoid and gravity anomalies at half-wavelengths of 55 km at the equator. A degree-dependent combination method has been applied in order to preserve the high accuracy from the GRACE satellite data in the lower frequency band of the geopotential and to form a smooth transition to the high-frequency information coming from the surface data. Compared to pre-CHAMP global high-resolution models, the accuracy was improved at a spatial resolution of 200 km (half-wavelength) by one order of magnitude to 3 cm in terms of geoid heights. The accuracy of this model (i.e. the commission error) at its full spatial resolution is estimated to be 15 cm. The model shows a reduced artificial meridional striping and an increased correlation of EIGEN-GL04C-derived geostrophic meridional currents with World Ocean Atlas 2001 (WOA01) data. These improvements have led to select EIGEN-GL04C for JASON-1 satellite altimeter data reprocessing.

Abstract. The International Centre for Global Earth Models (ICGEM, http://icgem.gfz-potsdam.de/, last access: 6 May 2019) hosted at the GFZ German Research Centre for Geosciences (GFZ) is one of the five services coordinated by the International Gravity Field Service (IGFS) of the International Association of Geodesy (IAG). The goal of the ICGEM service is to provide the scientific community with a state-of-the-art archive of static and temporal global gravity field models of the Earth, and develop and operate interactive calculation and visualization services of gravity field functionals on user-defined grids or at a list of particular points via its website. ICGEM offers the largest collection of global gravity field models, including those from the 1960s to the 1990s, as well as the most recent ones, which have been developed using data from dedicated satellite gravity missions, CHAMP, GRACE, GOCE, advanced processing methodologies, and additional data sources such as satellite altimetry and terrestrial gravity. The global gravity field models have been collected from different institutions at international level and after a validation process made publicly available in a standardized format with DOI numbers assigned through GFZ Data Services. The development and maintenance of such a unique platform is crucial for the scientific community in geodesy, geophysics, oceanography, and climate research. In this article, we present the development history and future plans of ICGEM and its current products and essential services. We present the ICGEM's data by means of Earth's static, temporal, and topographic gravity field models as well as the gravity field models of other celestial bodies together with examples produced by the ICGEM's calculation and 3-D visualization services and give an insight into how the ICGEM service can additionally contribute to the needs of research and society.

Using three months of GPS satellite‐to‐satellite tracking and accelerometer data of the CHAMP satellite mission, a new long‐wavelength global gravity field model, called EIGEN‐1S, has been prepared in a joint German‐French effort. The solution is derived solely from analysis of satellite orbit perturbations, i.e. independent of oceanic and continental surface gravity data. EIGEN‐1S results in a geoid with an approximation error of about 20 cm in terms of 5 × 5 degree block mean values, which is an improvement of more than a factor of 2 compared to pre‐CHAMP satellite‐only gravity field models. This impressive progress is a result of CHAMP's tailored orbit characteristics and dedicated instrumentation, providing continuous tracking and direct on‐orbit measurements of non‐gravitational satellite accelerations.

[1] We analyze spatiotemporal variations of surface mass anomalies induced by hydrological mass redistributions at the Earth's surface. To this end, we use a suite of global hydrological models as well as products from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. As a novelty we identify dominating periodic patterns that are not restricted to the fundamental annual frequency and its overtones, using a method that combines conventional empirical orthogonal functions (EOF) analysis with a determination of sine waves of arbitrary periods from the principal components. We assess the significance of the derived spectra in view of correlated errors of the GRACE data by means of a Monte Carlo technique. This allows us to create filtered GRACE time series including only the significant terms, which will serve for basin-specific calibration of hydrological models with respect to the dominant periodic water storage variations. The study reveals that besides dominating annual signals, semiannual (found only in a few basins), and also long-periodic waves in the range of 2.1 to 2.5 years contribute to periodic water storage variations. An interpretation and a preliminary explanation of these spectra is included. Comparisons of the spectra obtained from GRACE and global hydrological models exhibit in many river basins a systematic advance of the phases of annual terms of the hydrological models as compared to GRACE in the range of 1 to 6 weeks. This indicates deficiencies of the hydrological models with regard to runoff routing in the river network and/or water retention in lakes and wetlands.

Gravity recovery and climate experiment (GRACE)-derived temporal gravity variations can be resolved within the µgal (10 −8 m/s 2 ) range, if we restrict the spatial resolution to a half-wavelength of about 1,500 km and the temporal resolution to 1 month. For independent validations, a comparison with ground gravity measurements is of fundamental interest. For this purpose, data from selected superconducting gravimeter (SG) stations forming the Global Geodynamics Project (GGP) network are used. For comparison, GRACE and SG data sets are reduced for the same known gravity effects due to Earth and ocean tides, pole tide and atmosphere. In contrast to GRACE, the SG also measures gravity changes due to load-induced height variations, whereas the satellite-derived models do not contain this effect. For a solid spherical harmonic decomposition of the gravity field, this load effect can be modelled using degreedependent load Love numbers, and this effect is added to the P. Schwintzer has deceased. satellite-derived models. After reduction of the known gravity effects from both data sets, the remaining part can mainly be assumed to represent mass changes in terrestrial water storage. Therefore, gravity variations derived from global hydrological models are applied to verify the SG and GRACE results. Conversely, the hydrology models can be checked by gravity variations determined from GRACE and SG observations. Such a comparison shows quite a good agreement between gravity variation derived from SG, GRACE and hydrology models, which lie within their estimated error limits for most of the studied SG locations. It is shown that the SG gravity variations (point measurements) are representative for a large area within the µgal accuracy, if local gravity effects are removed. The individual discrepancies between SG, GRACE and hydrology models may give hints for further investigations of each data series.