Articles | Volume 17, issue 2
https://doi.org/10.5194/essd-17-611-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-611-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GravIS: mass anomaly products from satellite gravimetry
Geodesy, GFZ Helmholtz Centre for Geosciences, 14473 Potsdam, Germany
Eva Boergens
Geodesy, GFZ Helmholtz Centre for Geosciences, 14473 Potsdam, Germany
Ingo Sasgen
Geosciences, Alfred Wegener Institute, 27568 Bremerhaven, Germany
Institute of Geography, University of Augsburg, 86159 Augsburg, Germany
Thorben Döhne
Institut für Planetare Geodäsie, Technische Universität Dresden, 01069 Dresden, Germany
Sven Reißland
Geodesy, GFZ Helmholtz Centre for Geosciences, 14473 Potsdam, Germany
Henryk Dobslaw
Geodesy, GFZ Helmholtz Centre for Geosciences, 14473 Potsdam, Germany
Volker Klemann
Geodesy, GFZ Helmholtz Centre for Geosciences, 14473 Potsdam, Germany
Michael Murböck
Geodesy, GFZ Helmholtz Centre for Geosciences, 14473 Potsdam, Germany
Institute of Geodesy, Technische Universität Berlin, 10623 Berlin, Germany
Rolf König
Geodesy, GFZ Helmholtz Centre for Geosciences, 14473 Potsdam, Germany
Institute of Geodesy, Technische Universität Berlin, 10623 Berlin, Germany
Robert Dill
Geodesy, GFZ Helmholtz Centre for Geosciences, 14473 Potsdam, Germany
Mike Sips
Geodesy, GFZ Helmholtz Centre for Geosciences, 14473 Potsdam, Germany
Ulrike Sylla
Geodesy, GFZ Helmholtz Centre for Geosciences, 14473 Potsdam, Germany
Andreas Groh
Institut für Planetare Geodäsie, Technische Universität Dresden, 01069 Dresden, Germany
Martin Horwath
Institut für Planetare Geodäsie, Technische Universität Dresden, 01069 Dresden, Germany
Frank Flechtner
Geodesy, GFZ Helmholtz Centre for Geosciences, 14473 Potsdam, Germany
Institute of Geodesy, Technische Universität Berlin, 10623 Berlin, Germany
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Cited
8 citations as recorded by crossref.
- Uncertainty Quantification of Satellite-Based Essential Climate Variables Derived from Deep Learning J. Gou et al. https://doi.org/10.1007/s10712-025-09919-2
- Gravity field recovery based on GNSS data of nano-satellites: a case study for the Spire CubeSat constellation T. Grombein et al. https://doi.org/10.1007/s00190-025-01998-8
- The changing mass of the Antarctic Ice Sheet during ENSO-dominated periods in the GRACE era (2002–2022) J. Ayabilah et al. https://doi.org/10.5194/tc-20-1237-2026
- GRACE and GRACE-FO mascons for ocean dynamic applications J. Bonin et al. https://doi.org/10.5194/essd-18-3481-2026
- A spatiotemporal deep learning framework integrating CNN-BiLSTM and attention mechanisms for GRACE data downscaling in Yunnan Province Y. He et al. https://doi.org/10.1016/j.cageo.2026.106117
- Assessing the Consistency Among Three Mascon Solutions and COST-G-Based Grid Products for Characterizing Antarctic Ice Sheet Mass Change Q. Long & X. Su https://doi.org/10.3390/rs17223699
- Technical note: GRACE-compatible filtering of water storage data sets via spatial autocorrelation analysis E. Sharifi et al. https://doi.org/10.5194/hess-29-6985-2025
- Assessing groundwater storage variations in the Volta River Basin combining remote sensing tools and machine learning downscaling techniques R. Djessou et al. https://doi.org/10.1016/j.ejrs.2025.06.001
8 citations as recorded by crossref.
- Uncertainty Quantification of Satellite-Based Essential Climate Variables Derived from Deep Learning J. Gou et al. https://doi.org/10.1007/s10712-025-09919-2
- Gravity field recovery based on GNSS data of nano-satellites: a case study for the Spire CubeSat constellation T. Grombein et al. https://doi.org/10.1007/s00190-025-01998-8
- The changing mass of the Antarctic Ice Sheet during ENSO-dominated periods in the GRACE era (2002–2022) J. Ayabilah et al. https://doi.org/10.5194/tc-20-1237-2026
- GRACE and GRACE-FO mascons for ocean dynamic applications J. Bonin et al. https://doi.org/10.5194/essd-18-3481-2026
- A spatiotemporal deep learning framework integrating CNN-BiLSTM and attention mechanisms for GRACE data downscaling in Yunnan Province Y. He et al. https://doi.org/10.1016/j.cageo.2026.106117
- Assessing the Consistency Among Three Mascon Solutions and COST-G-Based Grid Products for Characterizing Antarctic Ice Sheet Mass Change Q. Long & X. Su https://doi.org/10.3390/rs17223699
- Technical note: GRACE-compatible filtering of water storage data sets via spatial autocorrelation analysis E. Sharifi et al. https://doi.org/10.5194/hess-29-6985-2025
- Assessing groundwater storage variations in the Volta River Basin combining remote sensing tools and machine learning downscaling techniques R. Djessou et al. https://doi.org/10.1016/j.ejrs.2025.06.001
Saved (final revised paper)
Discussed (final revised paper)
Latest update: 08 Jun 2026
Short summary
GRACE and GRACE-FO are unique observing systems to quantify mass changes at the Earth’s surface from space. Time series of these mass changes are of high value for various applications, e.g., in hydrology, glaciology, and oceanography. GravIS (Gravity Information Service) provides easy access to user-friendly, regularly updated mass anomaly products. The portal visualizes and describes these data, aiming to highlight their significance for understanding changes in the climate system.
GRACE and GRACE-FO are unique observing systems to quantify mass changes at the Earth’s surface...
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