Non-tidal ocean loading signals are known to be a significant source of geophysically induced noise in gravimetric and geodetic observations also far-away from the coast and especially during extreme events such as storm surges. Operational products suffer from a low temporal and spatial resolution and reveal only small amplitudes on continental stations. Dedicated high-resolution sea-level modelling of the North and Baltic Sea provides a largely improved prediction of non-tidal ocean loading signals. Superconducting gravimeter and GNSS observations on the small offshore island of Heligoland in the North Sea are used for a thorough evaluation of the model values revealing correlations of up to 0.9 and signal reductions of up to 50 % during a storm surge period of one month in Jan-Feb 2022. Additional continental superconducting gravimeter stations are used to assess the benefits from high-resolution modelling for an improved signal separation further away from the coast.

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 DGFI-TUM and BKG, or observation level processed by 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 ESMGFZ. We find, that a multi-technique combination at normal equation level that explicitly aligns a priori station coordinates to the ITRF2014 frequently outperforms the current IERS standard solution 14C04. A multi-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 -3B to realize space links. For ΔUT1, however, 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.

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 day forecast period. Integrated over the initial 3 day 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.

Recently, the continually increasing availability of seismic data has allowed high-resolution imaging of lithospheric structure beneath the African cratons. In this study, S-wave seismic tomography are combined with high resolution satellite gravity data in an integrated approach to investigate the structure of the cratonic lithosphere of Africa. A new model for the Moho depth and data on the crustal density structure are employed along with global dynamic models to calculate residual topography and mantle gravity residuals. Corrections for thermal effects of an initially juvenile mantle are estimated based on S-wave tomography and mineral physics. Joint inversion of the residuals yields necessary compositional adjustments that allow to recalculate the thermal effects. After several iterations, we obtain a consistent model of upper mantle temperature, thermal and compositional density variations, and Mg# as a measure of depletion, as well as an improved crustal density model. Our results show that thick and cold depleted lithosphere underlies West African, northern to central eastern Congo, and Zimbabwe Cratons. However, for most of these regions, the areal extent of their depleted lithosphere differs from the respective exposed Archean shields. Meanwhile, the lithosphere of Uganda, Tanzania, most of eastern and southern Congo, and the Kaapvaal Craton is thinner, warmer, and shows little or no depletion. Furthermore, the results allow to infer that the lithosphere of the exposed Archean shields of Congo and West African cratons was depleted before the single blocks were merged into their respective cratons.