Abstract Considering the inadequate modeling of sub‐monthly mass change aliasing signals in monthly gravity field estimation, a joint modeling approach that simultaneously estimates gravity field parameters and aliasing components was proposed. The effectiveness of the dealiasing strategy was validated using a closed‐loop full‐scale simulation and real data processing analysis. Consequently, this method effectively mitigates the treatment of sub‐monthly aliasing variations as high‐degree noise in gravity field processing, leading to approximately 9.5% improvement in the accuracy of gravity field estimation. Using this method, we developed the Tongji‐Grace2022 monthly gravity field solutions from Gravity Recovery and Climate Experiment/GRACE Follow‐On Level‐1B observations. Comprehensive analyses conducted in the spectral, temporal, and spatial domains demonstrate that the Tongji‐Grace2022 solution outperforms other gravity field solutions without modeling sub‐monthly aliasing signals, achieving an average noise reduction rate of approximately 9.5%. Compared with models that do not account for sub‐monthly aliasing effects, the signal‐to‐noise ratio (SNR) values obtained with Tongji‐Grace2022 are consistently higher across most regions of 40 global river basins. Additionally, comparisons with the official monthly models (e.g., CSR RL06.3, GFZ RL06.3, and JPL RL06.3) and ITSG‐Grace2018 solutions show that the mass change signals from Tongji‐Grace2022 are in close agreement with those from the other four models. Notably, both Tongji‐Grace2022 and ITSG‐Grace2018 exhibit lower noise levels than the three official models at the global scale. In particular, the average root‐mean‐square (RMS) values over ocean regions indicate that Tongji‐Grace2022 achieves noise reduction rates of approximately 40.6% and 10.5%, respectively, in comparison to CSR RL06.3 and ITSG‐Grace2018 when exclusively utilizing P4M6 decorrelation filtering.
Chen et al. (Sun,) studied this question.
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