scholarly journals Evaluation of gravity field model EIGEN-6C4 by means of various functions of gravity potential, and by GNSS/levelling

2015 ◽  
Vol 14 (1) ◽  
pp. 7-28 ◽  
Author(s):  
Jan Kostelecký ◽  
Jaroslav Klokočník ◽  
Blažej Bucha ◽  
Aleš Bezděk ◽  
Christoph Förste
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Gravity Potential ◽  
Data Sets ◽  
Satellite Gravity ◽  
Disturbing Potential ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Second Derivatives ◽  
Gravity Disturbances

<p>The combined gravity field model EIGEN-6C4 (Förste et al., 2014) is the latest combined global gravity field model of GFZ Potsdam and GRGS Toulouse. EIGEN-6C4 has been generated including the satellite gravity gradiometry data of the entire GOCE mission (November 2009 till October 2013) and is of maximum spherical degree and order 2190. In this study EIGEN-6C4 has been compared with EGM2008 to its maximum degree and order via gravity disturbances and T<sub>zz</sub> part of the Marussi tensor of the second derivatives of the disturbing potential. The emphasis is put on such areas where GOCE data (complete set of gradiometry measurements after reductions) in EIGEN-6C4 obviously contributes to an improvement of the gravity field description. </p><p>GNSS/levelling geoid heights are independent data source for the evaluation of gravity field models. Therefore, we use the GNSS/levelling data sets over the territories of Europe, Czech Republic and Slovakia for the evaluation of EIGEN-6C4 w.r.t. EGM2008.</p>

scholarly journals The combined global gravity field model XGM2019e

2020 ◽  
Vol 94 (7) ◽  
Author(s):  
P. Zingerle ◽  
R. Pail ◽  
T. Gruber ◽  
X. Oikonomidou
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Global Gravity Field Model

Fitting the GPS/Leveling Quasi-Geoid Using Bayesian-Regulation BP Neural Network

2011 ◽  
Vol 90-93 ◽  
pp. 2903-2906
Author(s):  
Lei Song ◽  
Xiao Qing Hu
Keyword(s):  
Neural Network ◽  
Gravity Field ◽  
Bp Neural Network ◽  
Field Model ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Fitting In ◽  
Gps Leveling ◽  
Global Gravity Field Model

The 2.5′×2.5′resolution local quasi-geoid is calculated using the global gravity field model and GPS/leveling data of region which points spacing is about 10km with the Bayesian- regulation BP neural network in this paper. The inner and outer precision of quasi-geoid are both superior 0.05m.The result indicat that the Bayesian regulation BP neural network could improve the precision of fitting and restrain the over-fitting in fitting. The region quasi-geoid excelled than 0.05m can be computed using the global gravity field model and about 10km baseline GPS/leveling data in smoothness region.

scholarly journals Gravity Field Model Determination Based on GOCE Satellite Point-Wise Accelerations Estimated from Onboard Carrier Phase Observations

2019 ◽  
Vol 11 (12) ◽  
pp. 1420
Author(s):  
Tangting Wu ◽  
Jiancheng Li ◽  
Xinyu Xu ◽  
Hui Wei ◽  
Kaifa Kuang ◽  
...  
Keyword(s):  
Gravity Field ◽  
Carrier Phase ◽  
Field Model ◽  
Satellite Gravity ◽  
Gravity Field Model ◽  
Differentiation Method ◽  
Phase Differentiation ◽  
Satellite Accelerations ◽  
Gravity Field Models ◽  
Goce Satellite

GPS-based, satellite-to-satellite tracking observations have been extensively used to elaborate the long-scale features of the Earth’s gravity field from dedicated satellite gravity missions. We proposed compiling a satellite gravity field model from Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) satellite accelerations directly estimated from the onboard GPS data using the point-wise acceleration approach, known as the carrier phase differentiation method. First, we composed the phase accelerations from the onboard carrier phase observations based on the sixth-order seven-point differentiator, which can eliminate the carrier phase ambiguity for Low Earth Orbiter (LEO). Next, the three-dimensional (3D) accelerations of the GOCE satellite were estimated from the derived phase accelerations as well as GPS satellite ephemeris and precise clock products. Finally, a global gravity field model up to the degree and order (d/o) 130 was compiled from the 71 days and nearly 2.5 years of 3D satellite accelerations. We also recovered three gravity field models up to d/o 130 from the accelerations derived by differentiating the kinematic orbits of European Space Agency (ESA), Graz, and School of Geodesy and Geomatics (SGG), which was the orbit differentiation method. We analyzed the accuracies of the derived accelerations and the recovered gravity field models based on the carrier phase differentiation method and orbit differentiation method in time, frequency, and spatial domain. The results showed that the carrier phase derived acceleration observations had better accuracy than those derived from kinematic orbits. The accuracy of the recovered gravity field model based on the carrier phase differentiation method using 2.5 years observations was higher than that of the orbit differentiation solutions for degrees greater than 70, and worse than Graz-orbit solution for degrees less than 70. The cumulative geoid height errors of carrier phase, ESA-orbit, and Graz-orbit solutions up to degree and order 130 were 17.70cm, 21.43 cm, and 22.11 cm, respectively.

scholarly journals GOCO06s – A satellite-only global gravity field model

2020 ◽  
Author(s):  
Andreas Kvas ◽  
Jan Martin Brockmann ◽  
Sandro Krauss ◽  
Till Schubert ◽  
Thomas Gruber ◽  
...  
Keyword(s):  
Gravity Field ◽  
Covariance Matrix ◽  
Field Model ◽  
Data Set ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Gravity Observation ◽  
Primary Model ◽  
Global Gravity Field Model ◽  
Variance Covariance Matrix

Abstract. GOCO06s is the latest satellite-only global gravity field model computed by the GOCO (Gravity Observation Combination) project. It is based on over a billion observations acquired over 15 years from 19 satellites with different complementary observation principles. This combination of different measurement techniques is key in providing consistent high-accuracy and best possible spatial resolution of the Earth's gravity field. The motivation for the new release was in the availability of reprocessed observation data for GRACE and GOCE, updated background models, and substantial improvements in the processing chains of the individual contributions. Due to the long observation period, the model consists not only of a static gravity field, but comprises additionally modeled temporal variations. These are represented by time variable spherical harmonic coefficients, using a deterministic model for a regularized trend and annual oscillation. The main focus within the GOCO combination process is on the proper handling of the stochastic behavior of the input data. Appropriate noise modelling for the used observations result in realistic accuracy information for the derived gravity field solution. This accuracy information, represented by the full variance-covariance matrix, is extremely useful for further combination with, for example, terrestrial gravity data and is published together with the solution. The primary model data (Kvas et al., 2019b) consisting of potential coefficients representing Earth's static gravity field, together with secular and annual variations are available on International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/, last accessed 2020-06-11). This data set is identified with the DOI https://doi.org/10.5880/ICGEM.2019.002. Supplementary material consisting of the full variance-covariance matrix of the static potential coefficients and estimated co-seismic mass changes are available on https://ifg.tugraz.at/GOCO (last accessed 2020-06-11).

scholarly journals GOCO06s – a satellite-only global gravity field model

2021 ◽  
Vol 13 (1) ◽  
pp. 99-118
Author(s):  
Andreas Kvas ◽  
Jan Martin Brockmann ◽  
Sandro Krauss ◽  
Till Schubert ◽  
Thomas Gruber ◽  
...  
Keyword(s):  
Gravity Field ◽  
Covariance Matrix ◽  
Field Model ◽  
Data Set ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Gravity Observation ◽  
Primary Model ◽  
Global Gravity Field Model ◽  
Variance Covariance Matrix

Abstract. GOCO06s is the latest satellite-only global gravity field model computed by the GOCO (Gravity Observation Combination) project. It is based on over a billion observations acquired over 15 years from 19 satellites with different complementary observation principles. This combination of different measurement techniques is key in providing consistently high accuracy and best possible spatial resolution of the Earth's gravity field. The motivation for the new release was the availability of reprocessed observation data for the Gravity Recovery and Climate Experiment (GRACE) and Gravity field and steady-state Ocean Circulation Explorer (GOCE), updated background models, and substantial improvements in the processing chains of the individual contributions. Due to the long observation period, the model consists not only of a static gravity field, but comprises additionally modeled temporal variations. These are represented by time-variable spherical harmonic coefficients, using a deterministic model for a regularized trend and annual oscillation. The main focus within the GOCO combination process is on the proper handling of the stochastic behavior of the input data. Appropriate noise modeling for the observations used results in realistic accuracy information for the derived gravity field solution. This accuracy information, represented by the full variance–covariance matrix, is extremely useful for further combination with, for example, terrestrial gravity data and is published together with the solution. The primary model data consisting of potential coefficients representing Earth's static gravity field, together with secular and annual variations, are available on the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/, last access: 11 June 2020). This data set is identified with the following DOI: https://doi.org/10.5880/ICGEM.2019.002 (Kvas et al., 2019b). Supplementary material consisting of the full variance–covariance matrix of the static potential coefficients and estimated co-seismic mass changes is available at https://ifg.tugraz.at/GOCO (last access: 11 June 2020).

scholarly journals Analysis of one month of CHAMP state vector and accelerometer data for the recovery of the gravity potential

2003 ◽  
Vol 1 ◽  
pp. 1-4 ◽  
Author(s):  
E. Howe ◽  
L. Stenseng ◽  
C. C. Tscherning
Keyword(s):  
Energy Conservation ◽  
Gravity Field ◽  
State Vector ◽  
Field Model ◽  
Gravity Data ◽  
Gravity Potential ◽  
Accelerometer Data ◽  
Gravity Field Model ◽  
Energy Conservation Method ◽  
Conservation Method

Abstract. The energy conservation method is based on knowledge of the state vector and measurements of nonconservative forces. This is or will be provided by CHAMP, GRACE and GOCE. Here the analysis of one month of CHAMP state vector and accelerometer data is described. The energy conservation method is used to estimate the gravity potential at satellite altitude. When doing so we consider the tidal potential from the sun and the moon, the explicit time variation of the gravity potential in inertial space and loss of energy due to external forces. Fast Spherical Collocation have been used to estimate a gravity field model to degree and order 90, UCPH2002 04. This gravity field model is compared to EGM96 and EIGEN-2. The largest differences with respect to EGM96 are found at those places where the gravity data used to determine EGM96 had the largest uncertainty. EIGEN-2 and UCPH2002 04 are similar, though there are some differences in Antarctica and Central Asia.

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