Temporal aliasing errors induced by high-frequency tidal and non-tidal mass variability in the Earth system are among the three most important error sources that limit the accuracy of present-day surface mass estimates from satellite gravimetry. By means of end-to-end simulations, we demonstrate that the Kalman Smoother approach developed by Kurtenbach et al. (2012) effectively captures non-tidal submonthly variability, and thereby reduces temporal aliasing errors way beyond the level of simply subtracting the standard dealiasing model AOD1B. Validation against in situ ocean bottom pressure observations confirms that the Kalman Smoother solutions published together with the ITSG-Grace2016 monthly gravity fields contain high-frequency signal over the oceans not predicted by AOD1B. The daily gravity fields therefore reduce aliasing artefacts in the monthly gravity fields, and at the same time provide observational evidence on submonthly bottom pressure variability presently not reflected in state-of-the-art numerical ocean circulation models. It is thus recommended to include a Kalman Smoother approach into any standard GRACE processing scheme. For a hypothetical double-pair configuration currently under consideration as a future mass change mission, we find that the benefit of the Kalman Smoother is much smaller thanks to the increased number of observations taken at different inclinations, which lead to generally reduced aliasing errors and much more isotropic spatial error correlations. We also reassess the idea of pre-eliminating low-resolution daily gravity fields and find large distortions in the monthly-mean gravity solution at spatial wavelengths around the cutoff-degree of the daily fields. We thus recommend further study for any satellite gravity mission concept that critically relies on such pre-elimination schemes for reaching its science objectives.
Fields of Expertise
- Sustainable Systems