Description: The Atmosphere and Ocean De-Aliasing Level-1B (AOD1B) Product provides a priori information about temporal variations in the Earth's gravity field caused by global mass variability in atmosphere and ocean.'It is based on analysis and forecast data of the operational high-resolution global numerical weather prediction (NWP) model from the European Centre for Medium-Range Weather Forecasts (ECMWF) such as ERA5 and ocean bottom pressure from an unconstrained simulation with a global ocean general circulation model that is consistently forced with ECMWF atmospheric data.

Description: GRACE/GRACE-FO Level-3 product based on COST-G RL01 Level-2B products (Dahle & Murböck, 2020) representing Ocean Bottom Pressure (OBP) variations provided at 1° latitude-longitude grids as defined over ocean areas. The OBP grids are provided in NetCDF format divided into yearly batches. The files each contain seven different variables: 1) 'barslv': gravity-based barystatic sea-level pressure 2) 'std_barslv': gravity-based barystatic sea-level pressure uncertainties 3) 'resobp': gravity-based residual ocean circulation pressure resobp 4) 'std_resobp': gravity-based residual ocean circulation pressure uncertainties 5) 'leakage': apparent gravity-based bottom pressure due to continental leakage 6) 'model_ocean': background-model ocean circulation pressure 7) 'model_atmosphere': background-model atmospheric surface pressure These Level-3 products are visualized at GFZ's web portal GravIS (http://gravis.gfz-potsdam.de). Link to data products: ftp://isdcftp.gfz-potsdam.de/grace/GravIS/COST-G/Level-3/OBP

Description: GRACE/GRACE-FO Level-3 product based on COST-G RL01 Level-2B products (Dahle & Murböck, 2020) representing Terrestrial Water Storage (TWS) anomalies provided at 1° latitude-longitude grids as defined over all continental regions except Greenland and Antarctica. The TWS anomaly grids are provided in NetCDF format divided into yearly batches. The files each contain four different variables: 1) 'tws': gravity-based TWS 2) 'std_tws': gravity-based TWS uncertainties 3) 'leakage': spatial leakage contained in TWS 4) 'model_atmosphere': background model atmospheric mass These Level-3 products are visualized at GFZ's web portal GravIS (http://gravis.gfz-potsdam.de). Link to data products: ftp://isdcftp.gfz-potsdam.de/grace/GravIS/COST-G/Level-3/TWS

Description: In Irrgang et al. (2020), we have trained a convolutional neural network to perform a so-called downscaling task. This downscaling aims to recover the fine-structure continental water storage distribution on the South American continent from coarse-resolution space-borne gravimetry observations. Here, we share data sets that were used for training the neural network, namely (1) monthly pairs of gridded terrestrial water storage anomalies (TWSA) of the South American continent and (2) surface water storage anomalies (SWSA) in the Amazonas region for the time period 2003-2019. TWSAs were used as target (output) values of the neural network and were derived from the Land Surface Discharge Model (LSDM, Dill, 2008). The corresponding input values were calculated by spatially smoothing the TWSA fields with a 600 km Gaussian filter. After training the neural network over the time period of 2003 to 2018, its performance was tested and compared to LSDM for the subsequent year 2019.

Description: This dataset contains predictions of Earth orientation parameters (EOP) submitted during the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC). The 2nd EOP PCC has been carried out by Centrum Badań Kosmicznych Polskiej Akademii Nauk CBK PAN in Warsaw in cooperation with the GFZ German Research Centre for Geosciences in Potsdam (Germany) and under the auspices of the International Earth Rotation and Reference Systems Service (IERS) within the IERS Working Group on the 2nd EOP PCC. The purpose of the campaign was to re-assess the current capabilities of EOP forecasting and to find most reliable prediction approaches. The operational part of the campaign lasted between September 1, 2021 and December 28, 2022. Throughout the duration of the 2nd EOP PCC, registered campaign participants submitted forecasts for all EOP parameters, including dX, dY, dPsi, dEps (components of celestial pole offsets), polar motion, differences between universal time and coordinated universal time, and its time-derivative length-of-day change. These submissions were made to the EOP PCC Office every Wednesday before the 20:00 UTC deadline. The predictions were then evaluated once the geodetic final EOP observations from the forecasted period became available. Each participant could register more than one method, and each registered method was assigned an individual ID, which was used, e.g., for file naming. The dataset contains text files with predicted parameters as submitted by campaign participants and MATLAB file which is a database with all correct predictions from each participant loaded into a structure.

Description: The Atmosphere and Ocean non-tidal De-aliasing Level-1B (AOD1B) product is widely used in satellite gravimetry to correct for transient effects of atmosphere-ocean mass variability that would otherwise alias into monthly-mean global gravity fields. The most recent release is based on the global ERA5 reanalysis and ECMWF operational data together with simulations from the general ocean circulation model MPIOM consistently forced with fields of the same atmospheric data-set. As background models are inevitably imperfect, residual errors due to aliasing remain. Accounting for the uncertainties of the background model data has, however, proven to be a useful approach to mitigate the impact of residual aliasing. In light of the changes made in the new release of AOD1B, previous uncertainty assessments are deemed too pessimistic and have been revised in the new time-series of true errors: AOe07. One possible way to include the uncertainty information of background models in gravity field estimation or simulation studies is through the computation and application of a variance-covariance matrix that describes the spatio-temporal error characteristics of the background model. The AOe07 variance-covariance-matrix provides this information through (1) a fully populated matrix up to degree and order 40 as well as (2) a diagonal matrix up to degree and order 180.

Description: We provide present-day glacial isostatic adjustment (GIA) gravity changes simulated with the numerical model VILMA. The effects of Earth and ocean pole tide due to rotational deformation (considered in VILMA) were removed. The dataset contains the solutions for 56 GIA model ensemble members including 54 3D models and 2 1D models. The results are provided as Stokes coefficients with a resolution of degree/order 170.