Released
DatasetThe Earth's gravity field as seen by the GOCE satellite - an improved sixth release derived with the time-wise approach (GO_CONS_GCF_2_TIM_R6)
Cite as:
Brockmann, Jan Martin; Schubert, Till; Mayer-Gürr, Torsten; Schuh, Wolf-Dieter (2019): The Earth's gravity field as seen by the GOCE satellite - an improved sixth release derived with the time-wise approach (GO_CONS_GCF_2_TIM_R6). GFZ Data Services. https://doi.org/10.5880/ICGEM.2019.003Status
I N R E V I E W : Brockmann, Jan Martin; Schubert, Till; Mayer-Gürr, Torsten; Schuh, Wolf-Dieter (2019): The Earth's gravity field as seen by the GOCE satellite - an improved sixth release derived with the time-wise approach (GO_CONS_GCF_2_TIM_R6). GFZ Data Services. https://doi.org/10.5880/ICGEM.2019.003
Abstract
The static gravitational model GO_CONS_GCF_2_TIM_R6 Is the 6th release of the GOCE gravity field model by means of the time-wise approach. GOCE Input Data:
- Gradients: EGG_NOM_2 (re-calibration, released 2018, version 0202)
- Orbits:
-- SST_PKI (kinematic orbits); SST_PCV (variance information of kinematic orbit positions),
-- SST_RNX (original RINEX orbit data)
- Attitude: EGG_IAQ_2C
- Non-conservative accelerations: EGG_CCD_2C
- Data period: 09/10/2009 - 20/10/2013
No static a-priori gravity field information applied (neither as reference model, nor for constraining the solution)
Processing procedures:
- Gravity from orbits (SST):
- short-arc integral method applied to kinematic orbits, up to degree/order 150
- orbit variance information included as part of the stochastic model, it is refined by empirical covariance functions
- Gravity from gradients (SGG):
- parameterization up to degree/order 300
- observations used: Vxx, Vyy, Vzz and Vxz in the Gradiometer Reference Frame (GRF)
- realistic stochastic modelling by applying digital decorrelation filters to the observation equations; estimated separately for individual data segments applying a robust procedure
- Combined solution:
- addition of normal equations (SST D/O 150, SGG D/O 300)
- Constraints:
* Kaula-regularization applied to coefficients of degrees/orders 201 - 300 (constrained towards zero)
* observation equations for zero gravity anomaly observations in polar regions (>83°) to constrain polar gaps towards zero (degree 11 to 300)
- Optimum weighting (SST, SGG, constraints) based on variance component estimation
Specific features of resulting gravity field:
- Gravity field solution is independent of any other gravity field information
- Constraint towards zero starting from degree/order 201 to improve signal-to-noise ratio
- Related variance-covariance information represents very well the true errors of the coefficients
- Solution can be used for independent comparison and combination on normal equation level with other satellite-only models (e.g. GRACE), terrestrial gravity data, and altimetry
- Since in the low degrees the solution is based solely on GOCE orbits, it is not competitive with a GRACE model in this spectral region is available via this data publication and via ICGEM (Ince et al., 2019). Link to ICGEM Website: http://icgem.gfz-potsdam.de
- The reference epoch is 2010-01-01 (MJD 55197)
Further processing details can be found in Brockmann (2014), Brockmann et al. (2014, 2021) Mayer-Gürr et al. (2005) and Pail et al. (2014).
Authors
- Brockmann, Jan Martin
;Institute of Geodesy and Geoinformation, Theoretical Geodesy Group, University of Bonn, Germany - Schubert, Till;Institute of Geodesy and Geoinformation, Theoretical Geodesy Group, University of Bonn, Germany
- Mayer-Gürr, Torsten;Institute of Geodesy, Theoretical Geodesy and Satellite Geodesy Group, TU Graz, Austria
- Schuh, Wolf-Dieter;Institute of Geodesy and Geoinformation, Theoretical Geodesy Group, University of Bonn, Germany
Contact
- Brockmann, Jan Martin; Institute of Geodesy and Geoinformation, Theoretical Geodesy Group, University of Bonn, Germany;
- ICGEM Support; GFZ German Research Centre for Geosciences, Potsdam, Germany;
Contributors
Ince, Elmas Sinem; Reißland, Sven; ICGEM SupportKeywords
ICGEM, global gravitational model, GOCEGCMD Science Keywords
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