Over the past 20 years, the Gravity Recovery and Climate Experiment (GRACE), and its successor mission, GRACE‐Follow On (GRACE‐FO) have made significant contributions to time‐variable gravity field modeling. A Chinese low‐low satellite‐to‐satellite tracking gravimetry mission (i.e., Chinese future gravimetry mission) has been confirmed to be selected as the polar‐orbiting satellite gravimetry mission for China, because of the capability to collect gravity data globally. However, the analysis of potential contributions to geosciences from GRACE‐FO coupling with the Chinese future gravimetry mission is still limited. This study combines GRACE‐FO and Chinese future gravimetry missions as the Dual GRACE‐like Polar satellite Constellation (DGPC). By carefully choosing the initial orbit parameters of the Chinese future gravimetry mission with the differential evolution algorithm, the DGPC is expected to mitigate the temporal aliasing effects by improving the temporal resolution of time‐variable gravity solutions (i.e., 1‐day and 3‐day solutions). Regarding the spectral‐domain evaluation, zonal, tesseral, and sectorial coefficients estimated by the DGPC show approximately 6.01%–13.42% noise reductions compared with GRACE‐FO. Regarding the spatial‐domain evaluation, the DGPC can suppress noises of about 39.44% and 31.12% in annual amplitude and long‐term trend, respectively. On this basis, this paper analyzes the effectiveness of the DGPC in potential contributions to geosciences (e.g., hydrology, glaciology, and seismology). Specifically, the DGPC can improve accuracy by about 36.96%, 25.85%, and 33.16% with respect to GRACE‐FO for signals in the subhumid basin, signals of ice‐sheet mass balance over Greenland, and coseismic displacement of the fault zone, respectively. In general, the potential capability for high‐frequency signals recovery of the DGPC would facilitate contributions of satellite gravimetry to geosciences.

Based on its unique sensitivity to Earth's temporal gravity and since 2002, the Gravity Recovery And Climate Experiment (GRACE) twin-satellite, and its successor, GRACE Follow On (GRACE-FO) missions have accumulated a twodecade-long and continuing Earth's mass change climate data record. Additionally, the Chinese Gravimetry Mission (ChiGaM) was launched as the last polar-pair satellite formation in 2021. With the opportunity of two Existing Polar-pair Gravity satellite Formations (EPGF) in operations, we explore the ideal configuration to launch a third-pair satellite formation to construct the Triple-pair Gravity Satellite Constellation (TGSC). Here we examine the selection of initial orbit parameters of the third satellite formation based on subcycles and orbit parameters of EPGF to augment TGSC. The simulation study explores the effectiveness of the monthly temporal gravity field from the TGSC in potential contributions to geosciences. Our study reveals that TGSC improves continental hydrological signal recovery by approximately 24% and 38% in large and small basins (above/below 106 km2), as compared to GRACE-FO which would be the polar-pair gravity satellite in operations. TGSC's effectiveness varies across drainage systems of the Greenland Ice Sheet (GrIS) due to different ground track coverage. Compared to GRACE-FO, TGSC enhances GrIS mass balance recovery by 37 ∼ 56%. Simulations for six mega earthquakes (above Mw 7.7) reveal that TGSC outperforms GRACE-FO by approximately 46 ∼ 58% in extracting co-seismic signals. Our study reveals the importance of incorporating existing on-orbit gravity missions into the design of future gravity satellite constellations. This strategy aims to not only accomplish the predefined objectives of gravity satellite missions but also potentially provide additional benefits to the field of geosciences.

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 https://doi.org/10.5281/zenodo.15525467.

Filtering for GRACE temporal gravity fields is a necessary step before calculating surface mass anomalies. In this study, we propose a new denoising and decorrelation kernel (DDK) filtering scheme called adaptive DDK filter. The involved error covariance matrix (ECM) adopts nothing but the monthly time-variable released by several data centers. The signal covariance matrix (SCM) involved is monthly time-variable also. Specifically, it is parameterized into two parameters, namely the regularization coefficient and the power index of signal covariances, which are adaptively determined from the data themselves according to the generalized cross validation (GCV) criterion. The regularization coefficient controls the global constraint on the signal variances of all degrees, while the power index adjusts the attenuation of the signal variances from low to high degrees, namely local constraint. By tuning these two parameters for the monthly SCM, the adaptability to the data and the optimality of filtering strength can be expected. In addition, we also devise a half-weight polygon area (HWPA) of the filter kernel to measure the filtering strength of the anisotropic filter more reasonably. The proposed adaptive DDK filter and filtering strength metric are tested based on CSR GRACE temporal gravity solutions with their ECMs from January 2004 to December 2010. Results show that the selected optimal power indices range from 3.5 to 6.9, with the corresponding regularization parameters range from 1 × 10¹⁴ to 5 × 10¹⁹. The adaptive DDK filter can retain comparable/more signal amplitude and suppress more high-degree noise than the conventional DDK filters. Compared with the equivalent smoothing radius (ESR) of filtering strength, the HWPA has stronger a distinguishing ability, especially when the filtering strength is similar.

Regarding the terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (-FO) gravity satellite missions, a BEAST (Bayesian estimator of abrupt change, seasonal change and trend)+GMDH (group method of data handling) gap-filling scheme driven by hydrological and meteorological data is proposed. Considering these driving data usually cannot fully capture the trend changes of the TWSA time series, we propose first to use the BEAST algorithm to perform piecewise linear detrending for the TWSA series and then fill the gap of the detrended series using the GMDH algorithm. The complete gap-filling TWSAs can be readily obtained after adding back the previously removed piecewise trend. By comparing the simulated gap filled by BEAST + GMDH using Multiple Linear Regression and Singular Spectrum Analysis with reference values, the results show that the BEAST + GMDH scheme is superior to the latter two in terms of the correlation coefficient, Nash-efficiency coefficient, and root-mean-square error. The real GRACE/GFO gap filled by BEAST + GMDH is consistent with those from hydrological models, Swarm TWSAs, and other literature regarding spatial distribution patterns. The correlation coefficients there between are, respectively, above 0.90, 0.80, and 0.90 in most of the global river basins.

Limitations in the temporal resolution of contemporary gravity satellite missions hinder the precise monitoring of rapid Earth surface mass changes. By the early 2030s, unprecedented high‐temporal monitoring of Earth's dynamic mass redistribution will be available using the temporal gravity field derived from the Hybrid Gravity Satellite Ensemble (referred to as the “HGSE” in this study), which contains GRACE‐FO, ChiGaM, TIANQIN‐2, GRACE‐C, and NGGM. This paper proposes a Hybrid‐Augmented Resolution Dealiasing (HARD) algorithm that utilizes a sliding window technique to co‐estimate 3‐day low‐degree and daily high‐degree spherical harmonic coefficients. The HARD algorithm reduces temporal aliasing errors by 18.4%–30.7% compared to conventional processing strategies. Based on predefined noise levels for each satellite, closed‐loop simulations demonstrate that the HGSE yields daily gravity field solutions (with a maximum degree and order of 60) that can effectively reduce noise by approximately 76.2% in long‐term trends and 39.3% in annual amplitudes compared to classical monthly solutions. Applications in terrestrial water storage (TWS) change, glacier mass change, and co‐seismic deformation reveal significant improvements: 39.4% enhanced TWS signal recovery in large river basins, 21.2% higher accuracy in monitoring Tibetan Plateau glacier mass variation, and 69.4% superior co‐seismic signal recovery for megathrust earthquakes. These findings underscore the potential of HGSE to advance high‐frequency gravity field monitoring, offering critical references for the performance analysis of future gravity satellite missions monitoring the Earth's dynamic system processes on a daily scale.