A simulation study on temporally tailored satellite gravity products for future missions

The record of satellite gravity missions over more than two decades enables unique insights into global mass transport processes on Earth. Past and current missions, like GRACE and GRACE-FO contribute valuable information for climate research and to Essential Climate Variables (ECV), e.g., terrestrial water storage, sea-level, and ice sheets. To ensure continuous monitoring of these climate variables, GRACE-C is planned to be launched in 2028, followed by Next Generation Gravity Mission (NGGM) in 2032. The combination of NGGM and GRACE-C will provide enhanced spatial and temporal resolution.This study employs closed-loop numerical simulations to evaluate current and future mission concepts, as well as applying different temporal basis functions to optimize the gravity field retrieval for climate applications. The results are based on input models representing global changes over a period of 12 years (ESA ESM) as well as extended timeseries up to 100 years (CMIP6 climate model run from GDFL). The models represent continental hydrology, and cryosphere, while the simulation environment takes instrument errors and background models errors into account. For different mission concepts, namely in-line single-pair missions and a double-pair mission, the recoverability of a time variable mass signal is evaluated with different temporal resolutions and for different processing strategies.In a comparison of the retrieval performance, it is shown that the double pair observations contribute to a reduced noise-level in the time variable gravity field retrieval compared to single pair observations. Less strong impact than the improved observation system, but still visible further improvements can be archived with direct parameterization strategies. Here, spherical harmonic estimates can be improved in the low degrees by taking sub-monthly correlations in the trend estimates into account. Further individual basins, if they have a large signal to noise ratio, can benefit from higher spatial resolution estimates of the long-term trend.