Articles | Volume 18, issue 10
https://doi.org/10.5194/essd-18-7697-2026
https://doi.org/10.5194/essd-18-7697-2026
Data description article
 | 
26 Oct 2026
Data description article |  | 26 Oct 2026

HUST-Grace2026s: a high-resolution static gravity field product from GRACE and GRACE-FO observations (2002–2025)

Lijun Zheng, Hao Zhou, Xiang Guo, Zebing Zhou, and Zhicai Luo

Related authors

HUST-Grace2024: a new GRACE-only gravity field time series based on more than 20 years of satellite geodesy data and a hybrid processing chain
Hao Zhou, Lijun Zheng, Yaozong Li, Xiang Guo, Zebing Zhou, and Zhicai Luo
Earth Syst. Sci. Data, 16, 3261–3281, https://doi.org/10.5194/essd-16-3261-2024,https://doi.org/10.5194/essd-16-3261-2024, 2024
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Cited articles

Abrykosov, P., Murböck, M., Hauk, M., Pail, R., and Flechtner, F.: Data-driven multi-step self-de-aliasing approach for GRACE and GRACE-FO data processing, Geophys. J. Int., 2023, 232, https://doi.org/10.1093/gji/ggac340, 2023. 
Akyilmaz, O., Ustun, A., Aydin, C., Arslan, N., Doganalp, S., Guney, C., Mercan, H., Uygur, S. O., Uz, M., and Yagci, O.: ITU_GRACE16 The global gravity field model including GRACE data up to degree and order 180 of ITU and other collaborating institutions, GFZ Data Services, https://doi.org/10.5880/icgem.2016.006, 2016. 
Behzadpour, S., Mayer-Gürr, T., and Krauss, S.: GRACE Follow-On accelerometer data recovery, J. Geophys. Res.-Sol. Ea., 126, https://doi.org/10.1029/2020JB021297, 2021. 
Braitenberg, C., Maurizio, G., Pivetta, T., Pastorutti, A., and Cavazza, W.: Satellite gravity fields and the identification of accreted microplates, Geosci. Front., 16, https://doi.org/10.1016/j.gsf.2024.101976, 2025. 
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By combining over twenty years of satellite data from the GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Gravity Recovery and Climate Experiment Follow-on) missions, this research creates a unified and highly accurate map of Earth's gravity field. Our advanced processing technique significantly improves accuracy, reducing errors by over half compared to previous models. The resulting dataset is crucial for precisely tracking long-term environmental shifts, such as water storage changes and ice sheet melt, thereby improving our understanding of climate change impacts.
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