Description: Earth angular momentum forecasts are naturally accompanied by forecast errors that typically grow with increasing forecast length. In contrast to this behavior, we have detected large quasi‐periodic deviations between atmospheric angular momentum wind term forecasts and their subsequently available analysis. The respective errors are not random and have some hard to define yet clearly visible characteristics which may help to separate them from the true forecast information. These kinds of problems, which should be automated but involve some adaptation and decision‐making in the process, are most suitable for machine learning methods. Consequently, we propose and apply a neural network to the task of removing the detected artificial forecast errors. We found that a cascading forward neural network model performed best in this problem. A total error reduction with respect to the unaltered forecasts amounts to about 30% integrated over a 6‐days forecast period. Integrated over the initial 3‐days forecast period, in which the largest artificial errors are present, the improvements amount to about 50%. After the application of the neural network, the remaining error distribution shows the expected growth with forecast length. However, a 24‐hourly modulation and an initial baseline error of 2 × 10−8 became evident that were hidden before under the larger forecast error.

Description: Plain Language Summary: Variations in Earth rotation can be described by changes in Earth angular momentum. Angular momentum functions are calculated from mass redistributions, for example, given by atmospheric models. Typically, atmospheric model forecasts are naturally accompanied by forecast errors that grow with increasing forecast length. In contrast to this behavior, atmospheric angular momentum wind term forecasts show large quasi‐periodic deviations when compared to their subsequently available model analysis data. The detected errors are not random and have some hard to define yet clearly visible characteristics. A postprocessing step using machine learning methods was established to remove the detected artificial forecast errors. A cascading forward neural network approach was able to reduce the forecast error by about 50% for the first forecast days and about 30% for a 6‐day forecast horizon. Moreover, the remaining error distribution shows the expected growth with forecast length. This postprocessing step improves atmospheric angular momentum forecasts without touching the numerical weather prediction model itself. Improved angular momentum forecasts should help to further decrease Earth rotation predictions errors.

Description: Key Points: Motion terms of atmospheric angular momentum forecasts contain systematic errors. Machine learning is used to learn and reduce these errors. Remaining stochastic errors show modulations with a 24‐hr period.

Description: http://esmdata.gfz-potsdam.de:8080/repository

Description: The ability of any satellite gravity mission concept to monitor mass transport processes in the Earth system is typically tested well ahead of its implementation by means of various simulation studies. Those studies often extend from the simulation of realistic orbits and instrumental data all the way down to the retrieval of global gravity field solution time-series. Basic requirement for all these simulations are realistic representations of the spatio-temporal mass variability in the different sub-systems of the Earth, as a source model for the orbit computations. For such simulations, a suitable source model is required to represent (i) high-frequency (i.e., subdaily to weekly) mass variability in the atmosphere and oceans, in order to realistically include the effects of temporal aliasing due to non-tidal high-frequency mass variability into the retrieved gravity fields. In parallel, (ii) low-frequency (i.e., monthly to interannual) variability needs to be modelled with realistic amplitudes, particularly at small spatial scales, in order to assess to what extent a new mission concept might provide further insight into physical processes currently not observable. The new source model documented here attempts to fulfil both requirements: Based on ECMWF’s recent atmospheric reanalysis ERA-Interim and corresponding simulations from numerical models of the other Earth system components, it offers spherical harmonic coefficients of the time-variable global gravity field due to mass variability in atmosphere, oceans, the terrestrial hydrosphere including the ice-sheets and glaciers, as well as the solid Earth. Simulated features range from sub-daily to multiyear periods with a spatial resolution of spherical harmonics degree and order 180 over a period of 12 years. In addition to the source model, a de-aliasing model for atmospheric and oceanic high-frequency variability with augmented systematic and random noise is required for a realistic simulation of the gravity field retrieval process, whose necessary error characteristics are discussed. The documentation of the updated ESA Earth System Model (updated ESM) for gravity mission simulation studies is organized as follows: The characteristics of the updated ESM along with some basic validation is presented in Volume 1. A detailed comparison to the original ESA ESM (Gruber et al., 2011) is provided in Volume 2, while Volume 3 contains the description of a strategy to derive realistic errors for the de-aliasing model of high-frequency mass variability in atmosphere and ocean.

Description: The influence of the elastic Earth properties on seasonal or shorter periodic surface deformations due to atmospheric surface pressure and terrestrial water storage variations is usually modeled by applying a local half-space model or an one dimensional spherical Earth model like PREM from which a unique set of elastic load Love numbers, or alternatively, elastic Green's functions are derived. The first model is valid only if load and observer almost coincide, the second model considers only the response of an average Earth structure. However, for surface loads with horizontal scales less than 2500 km2, as for instance, for strong localized hydrological signals associated with heavy precipitation events and river floods, the Earth elastic response becomes very sensitive to inhomogeneities in the Earth crustal structure. We derive a set of local Green's functions defined globally on a 1° × 1° grid for the 3-layer crustal structure TEA12. Local Green's functions show standard deviations of ±12% in the vertical and ±21% in the horizontal directions for distances in the range from 0.1° to 0.5°. By means of Green's function scatter plots, we analyze the dependence of the load response to various crustal rocks and layer thicknesses. The application of local Green's functions instead of a mean global Green's function introduces a variability of 0.5 − 1.0 mm into the hydrological loading displacements, both in vertical and in horizontal directions. Maximum changes due to the local crustal structures are from −25% to +26% in the vertical and −91% to +55% in the horizontal displacements. In addition, the horizontal displacement can change its direction significantly. The lateral deviations in surface deformation due to local crustal elastic properties are found to be much larger than the differences between various commonly used one-dimensional Earth models.

Description: GRACE/GRACE-FO Level-3 product based on GFZ RL06 Level-2B products (Dahle & Murböck, 2019) 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/GFZ/Level-3/OBP

Description: Over the last 15 years, the Gravity Recovery and Climate Experiment (GRACE) mission has provided measurements of temporal changes in mass redistribution at and within the Earth that affect polar motion. The newest generation of GRACE temporal models, are evaluated by conversion into the equatorial components of hydrological polar motion excitation and compared with the residuals of observed polar motion excitation derived from geodetic measurements of the pole coordinates. We analyze temporal variations of hydrological excitation series and decompose them into linear trends and seasonal and non-seasonal changes, with a particular focus on the spectral bands with periods of 1000–3000, 450–1000, 100–450, and 60–100 days. Hydrological and reduced geodetic excitation series are also analyzed in four separated time periods which are characterized by different accuracy of GRACE measurements. The level of agreement between hydrological and reduced geodetic excitation depends on the frequency band considered and is highest for interannual changes with periods of 1000–3000 days. We find that the CSR RL06, ITSG 2018 and CNES RL04 GRACE solutions provide the best agreement with reduced geodetic excitation for most of the oscillations investigated.

Description: Different Earth orientation parameter (EOP) time series are publicly available that typically arise from the combination of individual space geodetic technique solutions. The applied processing strategies and choices lead to systematically differing signal and noise characteristics particularly at the shortest periods between 2 and 8 days. We investigate the consequences of typical choices by introducing new experimental EOP solutions obtained from combinations at either normal equation level processed by Deutsches Geodätisches Forschungsinstitut at the Technical University of Munich (DGFI‐TUM) and Federal Agency for Cartography and Geodesy (BKG), or observation level processed by European Space Agency (ESA). All those experiments contribute to an effort initiated by ESA to develop an independent capacity for routine EOP processing and prediction in Europe. Results are benchmarked against geophysical model‐based effective angular momentum functions processed by Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences (ESMGFZ). We find, that a multitechnique combination at normal equation level that explicitly aligns a priori station coordinates to the ITRF2014 frequently outperforms the current International Earth Rotation and Reference Systems Service (IERS) standard solution 14C04. A multi‐Global Navigation Satellite System (GNSS)‐only solution already provides very competitive accuracies for the equatorial components. Quite similar results are also obtained from a short combination at observation level experiment using multi‐GNSS solutions and SLR from Sentinel‐3A and Sentinel‐3B to realize space links. For ΔUT1, however, very long baseline interferometry (VLBI) information is known to be critically important so that experiments combining only GNSS and possibly SLR at observation level perform worse than combinations of all techniques at normal equation level. The low noise floor and smooth spectra obtained from the multi‐GNSS solution nevertheless illustrates the potential of this most rigorous combination approach so that further efforts to include in particular VLBI are strongly recommended.

Description: Spherical harmonic coefficients that are zero over the continents, and provide the anomalous simulated ocean bottom pressure that includes non-tidal air and water contributions elsewhere during the specified timespan. These coefficients differ from GLO (or GAC) coefficients over the ocean domain by disregarding upper air density anomalies. The anomalous signals are relative to the mean field from 2003-2014.

Description: The ability of any satellite gravity mission concept to monitor mass transport processes in the Earth system is typically tested well ahead of its implementation by means of various simulation studies. Those studies often extend from the simulation of realistic orbits and instrumental data all the way down to the retrieval of global gravity field solution time-series. Basic requirement for all these simulations are realistic representations of the spatio-temporal mass variability in the different sub-systems of the Earth, as a source model for the orbit computations. For such simulations, a suitable source model is required to represent (i) high-frequency (i.e., sub-daily to weekly) mass variability in the atmosphere and oceans, in order to realistically include the effects of temporal aliasing due to non-tidal high-frequency mass variability into the retrieved gravity fields. In parallel, (ii) low-frequency (i.e., monthly to interannual) variability needs to be modelled with realistic amplitudes, particularly at small spatial scales, in order to assess to what extent a new mission concept might provide further insight into physical processes currently not observable. The new source model documented here attempts to fulfil both requirements: Based on ECMWF’s recent atmospheric reanalysis ERA-Interim and corresponding simulations from numerical models of the other Earth system components, it offers spherical harmonic coefficients of the time-variable global gravity field due to mass variability in atmosphere, oceans, the terrestrial hydrosphere including the ice-sheets and glaciers, as well as the solid Earth. Simulated features range from sub-daily to multiyear periods with a spatial resolution of spherical harmonics degree and order 180 over a period of 12 years. In addition to the source model, a de-aliasing model for atmospheric and oceanic high-frequency variability with augmented systematic and random noise is required for a realistic simulation of the gravity field retrieval process, whose necessary error characteristics are discussed. The documentation is organized as follows: The characteristics of the updated ESM along with some basic validation are presented in Volume 1 of this report (Dobslaw et al., 2014). A detailed comparison to the original ESA ESM (Gruber et al., 2011) is provided in Volume 2 (Bergmann-Wolf et al., 2014), while Volume 3 (Forootan et al., 2014) contains a description of the strategy to derive a realistically noisy de-aliasing model for the high-frequency mass variability in atmosphere and oceans. The files of the updated ESA Earth System Model for gravity mission simulation studies are accessible at DOI:10.5880/GFZ.1.3.2014.001.

Description: Spherical harmonic coefficients that represent anomalous contributions of the non-tidal atmosphere to the Earth's mean gravity field during the specified timespan. This includes the contribution of atmospheric surface pressure over the continents, the static contribution of atmospheric pressure to ocean bottom pressure elsewhere, and the contribution of upper-air density anomalies above both the continents and the oceans. The anomalous signals are relative to the mean field from 2003-2014.