EGU21-7422, updated on 12 Jan 2022
https://doi.org/10.5194/egusphere-egu21-7422
EGU General Assembly 2021
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Seven years of monthly low-degree gravity field models from Swarm GPS data

Joao de Teixeira da Encarnacao1,2, Daniel Arnold3, Ales Bezdek4, Christoph Dahle5, Junyi Guo6, Jose van den IJssel1, Adrian Jaeggi3, Jaroslav Klokocnik4, Sandro Krauss7, Torsten Mayer‐Guerr7, Ulrich Meyer3, Josef Sebera4, CK Shum6, Pieter Visser1, and Yu Zhang6
Joao de Teixeira da Encarnacao et al.
  • 1Delft University of Technology, Space Systems Engineering, Delft, Netherlands (j.g.deteixeiradaencarnacao@tudelft.nl)
  • 2Center for Space Research, University of Texas at Austin, Austin, Texas
  • 3Astronomical Institute of the University of Bern, Bern, Switzerland
  • 4Astronomical Institute of the Czech Academy of Sciences, Prague, Czech Republic
  • 5GFZ German Research Centre for Geosciences, Potsdam, Germany
  • 6School of Earth Science of the Ohio State University, Columbus, Ohio, USA
  • 7Institute of Geodesy of the Graz University of Technology, Graz, Austria

The Swarm satellite constellation provides GPS data with sufficient accuracy to observe the large-scale mass transport processes occurring at the Earth’s surface since 2013. We illustrate the signal content of the time series of monthly gravity field models. The models are published on quarterly basis and are the result of a combination of the individual models produced by different gravity field estimation approaches, by the Astronomical Institute of the University of Bern, the Astronomical Institute of the Czech Academy of Sciences, the Institute of Geodesy of the Graz University of Technology and the School of Earth Sciences of the Ohio State University. We combine the models at the solution level, using weights derived from a Variance Component Estimation, under the framework of the International Combination Service for Time-variable Gravity Fields (COST-G).

 

We estimate the monthly quality of the models by comparing with GRACE and GRACE-FO products and illustrate the improvement of the combined model as compared to the individual models. We present the high signal-to-noise ratio of this uninterrupted time series of models, smoothed to 750km radius, over large hydrological basins. Finally, we compare the behavior of degree 2 and 3 coefficients with GRACE/GRACE-FO and SLR.

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