What can be expected from GNSS tracking of satellite constellations for temporal gravity field model determination?

2020 ◽  
Vol 222 (1) ◽  
pp. 661-677
Author(s):  
Hao Zhou ◽  
Zebing Zhou ◽  
Zhicai Luo ◽  
Kang Wang ◽  
Min Wei
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Geoid Height ◽  
Error Component ◽  
Expected Improvement ◽  
Gps Tracking ◽  
Satellite Constellations ◽  
Gravity Field Model ◽  
Gravity Field Determination ◽  
Temporal Gravity

SUMMARY The goal of this contribution is to investigate the expected improvement of temporal gravity field determination via a couple of high-low satellite-to-satellite tracking (HLSST) missions. The simulation system is firstly validated by determining monthly gravity field models within situ GRACE GPS tracking data. The general consistency between the retrieved solutions and those developed by other official agencies indicates the good performance of our software. A 5-yr full-scale simulation is then performed using the full error sources including all error components. Analysis of each error component indicates that orbit error is the main contributor to the overall HLSST-derived gravity field model error. The noise level of monthly solution is therefore expected to reduce 90 per cent in terms of RMSE over ocean when the orbit accuracy improves for a magnitude of one order. As for the current HLSST mission consisting of a current GNSS receiver and an accelerometer (10−10 and 10−9 m s–2 noise for sensitive and non-sensitive axes), it is expected to observe monthly (or weekly) gravity solution at the spatial resolution of about 1300 km (or 2000 km). As for satellite constellations, a significant improvement is expected by adding the second satellite with the inclination of 70° and the third satellite with the inclination of 50°. The noise reduction in terms of cumulative geoid height error is approximately 51 per cent (or 62 per cent) when the observations of two (or three) HLSST missions are used. Moreover, the accuracy of weekly solution is expected to improve 40–70 per cent (or 27–59 per cent) for three (or two) HLSST missions when compared to one HLSST mission. Due to the low financial costs, it is worthy to build a satellite constellation of HLSST missions to fill the possible gaps between the dedicated temporal gravity field detecting missions.

scholarly journals Effects of New Level-1B Data on GRACE Temporal Gravity Field Models and Precise Orbit Determination Solutions

2021 ◽  
Vol 13 (20) ◽  
pp. 4119
Author(s):  
Nannan Guo ◽  
Xuhua Zhou ◽  
Kai Li
Keyword(s):  
Gravity Field ◽  
Orbit Determination ◽  
Field Model ◽  
Precise Orbit Determination ◽  
Satellite Orbit ◽  
Data Types ◽  
Gravity Field Model ◽  
Precise Orbit ◽  
Temporal Gravity ◽  
Gravity Field Models

The quality of Gravity Recovery and Climate Experiment (GRACE) observation is the prerequisite for obtaining the high-precision GRACE temporal gravity field model. To study the influence of new-generation GRACE Level-1B Release 03 (RL03) data and the new atmosphere and ocean de-aliasing (AOD1B) products on recovering temporal gravity field models and precise orbit determination (POD) solutions, we combined the global positioning system and K-band ranging-rate (KBRR) observations of GRACE satellites to estimate the effect of different data types on these solutions. The POD and monthly gravity field solutions are obtained from 2005 to 2010 by SHORDE software developed by the Shanghai Astronomical Observatory. The post-fit residuals of the KBRR data were decreased by approximately 10%, the precision of three-direction positions of the GRACE POD was improved by approximately 5%, and the signal-to-noise ratio of the monthly gravity field model was enhanced. The improvements in the new release of monthly gravity field model and POD solutions can be attributed to the enhanced Level-1B KBRR data and the AOD1B model. These improvements were primarily due to the enhanced of KBRR data; the effect of the AOD1B model was not significant. The results also showed that KBRR data slightly improve the satellite orbit precision, and obviously enhance the precision of the gravity field model.

scholarly journals Impact of Different Kinematic Empirical Parameters Processing Strategies on Temporal Gravity Field Model Determination

2018 ◽  
Vol 123 (11) ◽  
pp. 10,252-10,276 ◽  
Author(s):  
Hao Zhou ◽  
Zhicai Luo ◽  
Zebing Zhou ◽  
Qiong Li ◽  
Bo Zhong ◽  
...  
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Gravity Field Model ◽  
Processing Strategies ◽  
Model Determination ◽  
Temporal Gravity

scholarly journals The earth’s gravity field recovery using the third invariant of the gravity gradient tensor from GOCE

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lin Cai ◽  
Xiaoyun Wan ◽  
Houtse Hsu ◽  
Jiangjun Ran ◽  
Xiangchao Meng ◽  
...  
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Gravity Gradient ◽  
Gravity Gradients ◽  
Gravity Field Model ◽  
Gravity Gradient Tensor ◽  
The Third ◽  
Third Invariant ◽  
Gravity Field Determination ◽  
2D Fft

AbstractDue to the independence of the gradiometer instrument’s orientation in space, the second invariant $$I_2$$ I 2 of gravity gradients in combination with individual gravity gradients are demonstrated to be valid for gravity field determination. In this contribution, we develop a novel gravity field model named I3GG, which is built mainly based on three novel elements: (1) proposing to utilize the third invariant $$I_3$$ I 3 of the gravity field and steady-state ocean circulation explorer (GOCE) gravity gradient tensor, instead of using the $$I_2$$ I 2 , similar to the previous studies; (2) applying an alternative two-dimensional fast fourier transform (2D FFT) method; (3) showing the advantages of $$I_3$$ I 3 over $$I_2$$ I 2 in the effect of measurement noise from the theoretical and practical computations. For the purpose of implementing the linearization of the third invariant, this study employs the theory of boundary value problems with sphere approximation at an accuracy level of $$O(J_2^2\cdot T_{ij})$$ O ( J 2 2 · T ij ) . In order to efficiently solve the boundary value problems, we proposed an alternative method of 2D FFT, which uses the coherent sampling theory to obtain the relationship between the 2D FFT and the third invariant measurements and uses the pseudo-inverse via QR factorization to transform the 2D Fourier coefficients to spherical harmonic ones. Based on the GOCE gravity gradient data of the nominal mission phase, a novel global gravity field model (I3GG) is derived up to maximum degree/order 240, corresponding to a spatial resolution of 83 km at the equator. Moreover, in order to investigate the differences of gravity field determination between $$I_3$$ I 3 with $$I_2$$ I 2 , we applied the same processing strategy on the second invariant measurements of the GOCE mission and we obtained another gravity field model (I2GG) with a maximum degree of 220, which is 20 degrees lower than that of I3GG. The root-mean-square (RMS) values of geoid differences indicates that the effects of measurement noise of I3GG is about 20% lower than that on I2GG when compared to the gravity field model EGM2008 (Earth Gravitational Model 2008) or EIGEN-5C (EIGEN: European Improved Gravity model of the Earth by New techniques). Then the accuracy of I3GG is evaluated independently by comparison the RMS differences between Global Navigation Satellite System (GNSS)/leveling data and the model-derived geoid heights. Meanwhile, the re-calibrated GOCE data released in 2018 is also dealt with and the corresponding result also shows the similar characteristics.

scholarly journals A high-quality global gravity field model from CHAMP GPS tracking data and accelerometry (EIGEN-1S)

2002 ◽  
Vol 29 (14) ◽  
pp. 37-1-37-4 ◽  
Author(s):  
Christoph Reigber ◽  
Georges Balmino ◽  
Peter Schwintzer ◽  
Richard Biancale ◽  
Albert Bode ◽  
...  
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Gps Tracking ◽  
Tracking Data ◽  
High Quality ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Global Gravity Field Model

scholarly journals Impact of geophysical model error for recovering temporal gravity field model

2016 ◽  
Vol 130 ◽  
pp. 177-185 ◽  
Author(s):  
Hao Zhou ◽  
Zhicai Luo ◽  
Yihao Wu ◽  
Qiong Li ◽  
Chuang Xu
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Model Error ◽  
Gravity Field Model ◽  
Geophysical Model ◽  
Temporal Gravity

Gravity-field determination from laser observations

1977 ◽  
Vol 284 (1326) ◽  
pp. 515-527 ◽  
Keyword(s):  
Gravity Field ◽  
Spherical Harmonics ◽  
Field Model ◽  
Satellite Tracking ◽  
Sea Surface ◽  
Tracking Data ◽  
Gravity Field Model ◽  
Long Wavelength ◽  
Gravity Field Determination

Knowledge of long-wavelength features of the geopotential is significantly improved by the use of precision satellite tracking with lasers. Tracking data on nine satellites are combined with terrestrial gravimetry to obtain a spherical-harmonics representation of the geopotential complete through degree and order 24. An improved gravity-field model provides better satellite ephemerides and a reference for analysing satellite-to-sea-surface altimetry.

Investigation of systematic errors in GRACE temporal gravity field solutions using the Improved Energy Balance Approach

2020 ◽  
Author(s):  
Metehan Uz ◽  
Orhan Akyılmaz ◽  
Jürgen Kusche ◽  
Ck Shum ◽  
Aydın Üstün ◽  
...  
Keyword(s):  
Energy Balance ◽  
Gravity Field ◽  
Spherical Harmonic ◽  
Field Model ◽  
Systematic Errors ◽  
Gravity Field Model ◽  
Range Rate ◽  
Energy Balance Approach ◽  
Temporal Gravity

<p>In this study, we investigate systematic errors in our temporal gravity solutions computed using the improved energy balance approach (EBA) (Shang et al. 2015) by reprocessing the GRACE JPL RL03 L1B data product. Our processing consists of two steps: the first part is the estimation of in-situ geopotential differences (GPD) at the satellite altitude using the energy balance formalism, the second part is the estimation of spherical harmonic coefficients (SHCs) of the global temporal gravity field model using the estimated GPDs. The first step includes daily dynamic orbit reconstruction by readjusting the reduced-dynamic (GNV1B) orbit considering the reference model, and estimating the accelerometer calibration parameters. This is coupled with the alignment of the intersatellite velocity pitch from KBR range rate observations. Due to the strategy of using KBR range-rate in our processing algorithm, the estimation of in-situ geopotential differences (GPD) includes both the systematic errors and the high-frequency noise that result from the range-rate observations. Since estimated GPDs are linearly connected with the spherical harmonic coefficients (SHCs) of the global gravity field model, our temporal models are affected by these errors, especially in high-degree coefficients of the temporal gravity field solutions (from n=25 to n=60).</p><p>In order to increase our solution accuracy, we fit additional empirical parameters for different arc lengths to mitigate the systematic errors in our GPD estimates, thus improving our temporal gravity field solutions. Our EBA approach GRACE monthly gravity field models are validated by comparisons to the official L2 data products, including the official solutions from CSR (Bettadpur et al., 2018), JPL (Yuan et al., 2018) and GFZ (Dahle et al., 2018).</p>

A 1.5km-resolution gravity field model of the Moon

2012 ◽  
Vol 329-330 ◽  
pp. 22-30 ◽  
Author(s):  
C. Hirt ◽  
W.E. Featherstone
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
The Moon ◽  
Gravity Field Model
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