This paper reports on an innovative mass concentration (mascon) solution obtained with the short-arc approach, named "GCL-Mascon2024", for estimating spatially enhanced mass variations on the Earth's surface by analyzing K- and Ka-band ranging satellite-to-satellite tracking data collected by the Gravity Recovery And Climate Experiment (GRACE) mission. Compared to contemporary GRACE mascon solutions, this contribution has three notable and distinct features: first, this solution recovery process incorporates frequency-dependent data-weighting techniques to reduce the influence of low-frequency noise in observations. Second, this solution uses variably shaped mascon geometry with physical constraints such as coastline and basin boundary geometries to more accurately capture temporal gravity signals while minimizing signal leakage. Finally, we employ a solution regularization scheme that integrates climate factors and cryospheric elevation models to alleviate the ill-posed nature of the GRACE mascon inversion problem. Our research has led to the following conclusions: (a) GCL-Mascon2024 mass anomaly estimates from GRACE data show strong agreement with the (Release) RL06 versions of mascon solutions (GSFC, CSR, JPL) in both spatial and temporal domains; (b) in Greenland and global hydrologic basins, the correlation coefficients of estimated mass changes between GCL-Mascon2024 and other RL06 mascon solutions exceed 95.0 %, with comparable amplitudes, and, especially over non-humid river basins, the GCL-Mascon2024 suppresses random noise by 27.8 % compared to contemporary mascon products; and (c) in desert regions, the analysis of residuals calculated after removing the climatological components from the mass variations indicates that the GCL-Mascon2024 solution achieves noise reductions of over 29.3 % as compared to the GSFC and CSR RL06 mascon solutions. The GCL-Mascon2024 gravity field solution (Yan and Ran, 2025) is available at 10.5281/zenodo.15525467.

GRACE observations revealed that rapid mass loss in the West Antarctic Ice Sheet (WAIS) abruptly paused in 2015, followed by a much lower rate of mass loss ((Formula presented.) Gt yr⁻¹) until the decommissioning of GRACE in 2017. The critical 1-year GRACE intermission data gap raises the question of whether the reduced mass loss rate persists. The Swarm gravimetry data, which have a lower resolution, show good agreement with GRACE/GRACE-FO observations during the overlapping period, i.e., high correlation (0.78) and consistent trend estimates. Swarm data efficiently bridge the GRACE/GRACE-FO data gap and reveal that WAIS has returned to the rapid mass loss state ((Formula presented.) Gt yr⁻¹) that prevailed prior to 2015 during the GRACE intermission data gap. The changes in precipitation patterns, driven by the climate cycles, further explain and confirm the dramatic shifts in the WAIS mass loss regime implied by the Swarm observations.

ESA’s SWARM constellation of three near-polar satellites was launched on 22 November 2013, with the primary scientific objective to map the Earth’s magnetic field and its variations. Although not among its primary scientific objectives, the specific orbital formation geometry of the three identical SWARM accelerometer-equipped satellites allows recovery for more accurate low-degree temporal gravity field. Equipped with satellite laser ranging retro-reflectors, accelerometers and geodetic-quality GPS receivers, data from the SWARM satellites have been used to estimate low-degree temporal gravity field based on the acceleration, short-arc, and celestial mechanics approaches, with geopotential solutions complete to degree and order 15. Here we will use the improved formulation for the energy balance approach (EBA) to estimate the temporal gravity field using data from the SWARM satellites. The improved energy balance approach to generate in situ geopotential difference measurements using the GRACE KBR data has achieved the measurement accuracy by more than 3 orders of magnitude compared to previous studies. Specifically, we will: (1) assess the accuracy of SWARM temporal gravity field EBA solutions by comparing with solutions using other approaches and versus GRACE solutions using GPS and using KBR; (2) assess the impact on lowdegree temporal gravity field EBA solutions using kinematic or dynamic SWARM GPS orbits; (3) evaluate low-degree temporal gravity solutions with or without accelerometer-corrections for data from individual SWARM satellite or from the combined constellation of SWARM satellites; (4) evaluate and confirm the maximum achievable degree and order of temporal gravity field model using data from the SWARM constellation of satellites, and (5) investigate the fidelity of the estimated SWARM second degree zonal geopotential coefficients and other approaches, as well as the solutions using other data, including SLR, other GPS, and GRACE KBR solutions.