the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Aug 2026
Impact of the NGGM and MAGIC future satellite gravity missions for scientific applications and operational services
Abstract. The ESA SING project evaluates the added value of the Next Generation Gravity Mission (NGGM) and the Mass-change and Geoscience International Constellation (MAGIC) for scientific applications and operational services in hydrology, oceanography, glaciology, climate science, solid Earth science, and geodesy. Using a closed-loop simulation framework that incorporates realistic instrumental, tidal and aliasing errors, synthetic gravity observations were generated to assess the capability of future satellite gravity missions to monitor mass variations in the atmosphere, oceans, hydrosphere, cryosphere, and solid Earth. The enhanced spatial and temporal resolution of NGGM and MAGIC substantially improves the monitoring and prediction of hydrological extremes, including floods and droughts, while estimates of total water storage anomalies, combined with complementary in situ observations and hydrological models, enable improved estimation of key variables of the continental water cycle, including precipitation and total drainable water storage. The improved resolution, accuracy, and temporal coverage of gravity observations are also critical for detecting climate-driven changes in the Atlantic Meridional Overturning Circulation (AMOC). Enhanced monitoring of mass changes in glaciers and ice sheets enable the detection of more rapid melt and accumulation events at finer spatial scales. Improved closure of sea-level and energy budgets is achieved through more accurate observations of mass redistribution at smaller spatial and temporal scales. In solid Earth applications, NGGM and MAGIC increase sensitivity to co-seismic and post-seismic deformation associated with smaller earthquakes while reducing data latency, thereby strengthening geohazard assessment and early warning capabilities. In geodesy, the missions support improved gravity field and geoid modelling, contributing to the realization and temporal evolution of the International Height Reference Frame (IHRF) and to more accurate precise orbit determination. Overall, the ESA SING results provide strong evidence of the scientific and societal benefits of NGGM and MAGIC to prepare the integration of future Earth Observation data into operational services.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Earth Observation.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 11 Oct 2026)
- RC1: 'Comment on egusphere-2026-4642', Anonymous Referee #1, 04 Sep 2026 reply
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- 1
In this paper, the authors examine simulated gravity data sets from three different mission scenarios: a GRACE-like pair, a lower inclination pair (NGGM), and a combination (MAGIC). The simulation is quite robust and the analysis of error differences is generally strong, although I have two significant comments on that which I would like to be addressed in some form. I don't think the changes I ask for will be too time-consuming, so I would rate this as a "minor revision."
Major Comments
1. The authors continue to compute maps from all data based on a full expansion of the spherical harmonics to degree/order 70 with no smoothing to reduce error. I consider this a little disingenuous, as it is well documented that even a little spatial smoothing of GRACE data removes a lot of the high random error and large d/o. In fact, many of the authors of this paper have contributed to the literature on such filters. By not smoothing the simulated GRACE-C data (as will be done in reality), it looks worse than it really is. This is most noticeable in the ocean tests (especially AMOC calculations). While I understand the authors are trying to demonstrate that the NGGM and MAGIC solutions won't need any smoothing (or less), they really should at least demonstrate some results with a standard GRACE filtering scheme to show the effects if there is standard filtering applied. They can still show the unfiltered results, but by not showing the filtered results, they are basically ignoring 20+ years of knowledge on how to deal with errors in a GRACE-like system! I suspect NGGM and MAGIC will still do better, but by a lesser degree.
2. The authors look at several derived metrics (drought monitoring, flooding, precipitation skill, etc). Many of these show global statistics (e.g., precipitation skill), but several (drought monitoring, flooding, glaciology) only show a few specific areas. While the authors present some global stats in the text (e.g., “for about 86 and 95% of the 140 largest hydrological basins worldwide the drought agreement of NGGM and MAGIC assimilation with the simulated truth agrees better as compared to the GRACE-C-like assimilation.” ), it would be better to SHOW this with a summary figure (something like Figure 3 and Figure 4). Doing so will significantly improve the paper.
Minor Comments
1. In the precipitation improvement section (after assimilation), I'm a little confused about something that seems obvious to me in Figure 3, but is glossed over in the text. Perhaps I am not understanding it properly -- if so, this means the authors need to describe their statistics/tests a little better. Based on the % differences in Figure 3, it appears that GRACE-C assimilation does better (e.g., higher R2 values compared to truth) throughout the tropics. The authors gloss over this with this statement: “Improvements are most evident in mid- and high latitude regions, whereas humid tropical regions exhibit neutral or locally negative differences” which I interpret as NGGM and MAGIC perform worse there! I would argue the changes in Australia and southeast Asia are quite far from “neutral” of “locally negative.” Those are pretty large-scale and big (upwards of -40%) and suggest assimilation of only GRACE-C is "better." Some more discussion on this is warranted, IMO, clearly calling out which data is "better" and where.
2. In the glaciology section (Figure 7), the authors present a snapshot at a single time. It really is difficult to assess whether this is true at ALL (or most) times. Some better metric that accounts for all times is warranted to see if this is valid outside of this one snapshot. Also, I was surprised at how well NGGM does, considering it is a lower-inclination pair and should have much higher errors near the pole. One wonders if some spatial smoothing was performed to make NGGM look better here.