SUMMARY: The joint ESA/NASA Mass-change And Geosciences International Constellation (MAGIC) has the objective to extend time-series from previous gravity missions, including an improvement of accuracy and spatio-temporal resolution. The long-term monitoring of Earth’s gravity field carries information on mass change induced by water cycle, climate change and mass transport processes between atmosphere, cryosphere, oceans and solid Earth. MAGIC will be composed of two satellite pairs flying in different orbit planes. The NASA/DLR-led first pair (P1) is expected to be in a near-polar orbit around 500 km of altitude; while the second ESA-led pair (P2) is expected to be in an inclined orbit of 65°–70° at approximately 400 km altitude. The ESA-led pair P2 Next Generation Gravity Mission shall be launched after P1 in a staggered manner to form the MAGIC constellation. The addition of an inclined pair shall lead to reduction of temporal aliasing effects and consequently of reliance on de-aliasing models and post-processing. The main novelty of the MAGIC constellation is the delivery of mass-change products at higher spatial resolution, temporal (i.e. subweekly) resolution, shorter latency and higher accuracy than the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO). This will pave the way to new science applications and operational services. In this paper, an overview of various fields of science and service applications for hydrology, cryosphere, oceanography, solid Earth, climate change and geodesy is provided. These thematic fields and newly enabled applications and services were analysed in the frame of the initial ESA Science Support activities for MAGIC. The analyses of MAGIC scenarios for different application areas in the field of geosciences confirmed that the double-pair configuration will significantly enlarge the number of observable mass-change phenomena by resolving smaller spatial scales with an uncertainty that satisfies evolved user requirements expressed by international bodies such as IUGG. The required uncertainty levels of dedicated thematic fields met by MAGIC unfiltered Level-2 products will benefit hydrological applications by recovering more than 90 per cent of the major river basins worldwide at 260 km spatial resolution, cryosphere applications by enabling mass change signal separation in the interior of Greenland from those in the coastal zones and by resolving small-scale mass variability in challenging regions such as the Antarctic Peninsula, oceanography applications by monitoring meridional overturning circulation changes on timescales of years and decades, climate applications by detecting amplitude and phase changes of Terrestrial Water Storage after 30 yr in 64 and 56 per cent of the global land areas and solid Earth applications by lowering the Earthquake detection threshold from magnitude 8.8 to magnitude 7.4 with spatial resolution increased to 333 km.

SUMMARY: The Mass Change and Geoscience International Constellation (MAGIC) is planned as the first realization of a double-pair low-low satellite-to-satellite (ll-sst) tracking gravity mission consisting of a polar and an inclined satellite pair. Due to the much increased spatial and temporal resolution and multidirectionality of the data to be collected by this mission, new possibilities regarding the resolvability of mass transport processes in space and time are expected. In order to maximize the scientific and societal outcome of this mission, an optimization of both the mission design as well as the methods to process the expected data is fundamental. Using numerical closed-loop simulations, we investigate the impact of several key mission design aspects on the gravity retrieval from a double-pair constellation such as the planned MAGIC mission. Specifically, we show how the choice of the second pair’s inclination poses a trade-off between a reduction of retrieval errors at latitudes covered by data from both pairs and at higher latitudes, thereby requiring a compromise between the latitude-dependent accuracy requirements of different user groups. One of the key mission goals is to provide fast-track gravity products with short latency for operational service applications. Towards the estimation of such short-term gravity fields of a few days, we investigate if coordinating the polar and inclined pairs’ orbits to achieve a stable ground-track coverage is necessary for obtaining a homogeneous accuracy of subsequent gravity solutions. Indeed, combining two freely drifting, uncontrolled orbits significantly degrades short-term gravity fields in time periods in which both pairs show coinciding ground track gaps. Finally, we analyse the relative performance of the two satellite pairs. Double-pair scenarios that are strongly dominated by the inclined pair’s data reveal degraded gravity solutions when co-estimating daily gravity fields as de-aliasing strategy. This effect can be mitigated by choosing a more balanced double-pair configuration, for example by choosing similar orbit heights and instrument noise levels for both satellite pairs. The findings presented in our study will serve to optimize the system design of the upcoming MAGIC constellation.

The reprocessing of the satellite gravitational gradiometry (SGG) data from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission in 2018/2019 considerably reduced the low-frequency noise in the data, leading to reduced noise amplitudes in derived gravity field models at large spatial scales, at which temporal variations of the Earth’s gravity field have their highest amplitudes. This is the motivation to test the reprocessed GOCE SGG data for their ability to resolve time-variable gravity signals. For the gravity field processing, we apply and compare a spherical harmonics (SH) approach and a mass concentration (mascon) approach. Although their global signal-to-noise ratio is <1, SH GOCE SGG-only models resolve the strong regional signals of glacier melting in Greenland and Antarctica, and the 2011 moment magnitude 9.0 earthquake in Japan, providing an estimation of gravity variations independent of Gravity Recovery and Climate Experiment (GRACE) data. The benefit of combined GRACE/GOCE SGG models is evaluated based on the ice mass trend signals in Greenland and Antarctica. While no signal contribution from GOCE SGG data additional to the GRACE models could be observed, we show that the incorporation of GOCE SGG data numerically stabilizes the related normal equation systems.

Satellite gravity data such as provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-up mission contain valuable information on all geophysical processes that involve a mass redistribution in the Earth system. However, as gravity is an integral quantity, only the sum of all signal components can be measured. In order to exploit gravity data for improving the geophysical understanding of the underlying mass redistribution processes, an algorithm to separate the superimposed signals is needed. We present two methods that solve the signal separation problem in global, spatial-temporal gravity data based on prior knowledge on the characteristic behavior of the individual signal components: A principal component analysis-based method is compared to a conditional generative adversarial network that has been originally developed for image-to-image translation tasks. Both methods are tested on synthetic model data in a closed-loop setup, and are shown to successfully accomplish the task of separating gravity signals related to atmospheric and oceanic processes from signals caused by processes in the continental hydrosphere. The two methods show a comparable level of prediction errors. For both methods, the preprocessing of the gravity signals before the signal separation step, more precisely, the choice of the gravity field functional, has a considerable impact on the predictive performance. All in all, we emphasize the potential of neural network algorithms for signal separation in spatial-temporal geodetic data, and suggest a more specific tuning of the algorithm to the task of interest.

Signal separation is one of the key challenges in exploiting geodetic data for downstream applications, including the possible extraction of mantle convection-related signals from gravimetric (satellite gravity mission-derived) and geometric (global navigation satellite systems) data. Using closed-loop simulations, we present a novel, neural network-based method for signal separation in global, temporal gravity data, that is based on a multi-channel U-Net architecture and which can be used to extract a large-scale, temporally linear signal from a sum of signals and noise. The algorithm solves the signal separation task by exploiting prior knowledge about the statistically typical behaviour of the individual superimposed signal components in the space and time domain. Compared to a two-step method using principal component analysis (PCA) and least-squares adjustment, the neural network method improves both the accuracy of the amplitudes (by one order of magnitude) and the temporal trend (by 94% in the noise-free case and 54% in the case including observation noise) of the target signal. While we investigate the performance of the methods using the example of extracting the gravimetric signal related to the glacial isostatic adjustment (GIA) process in North America, our findings pave the way towards algorithms that can extract mantle convection-related signals from geodetic spatio-temporal data.