Dynamic orbit determination and gravity field model improvement from GPS, DORIS and Laser measurements on TOPEX/POSEIDON satellite

1997 ◽  
Vol 71 (3) ◽  
pp. 160-170 ◽  
Author(s):  
F. Perosanz ◽  
J. C. Marty ◽  
G. Balmino
Keyword(s):  
Gravity Field ◽  
Orbit Determination ◽  
Field Model ◽  
Gravity Field Model ◽  
Laser Measurements ◽  
Model Improvement

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.

GFZ-1: A small laser satellite mission for gravity field model improvement

1996 ◽  
Vol 23 (22) ◽  
pp. 3143-3146 ◽  
Author(s):  
Rolf König ◽  
Peter Schwintzer ◽  
Albert Bode ◽  
Christoph Reigber
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Satellite Mission ◽  
Gravity Field Model ◽  
Model Improvement

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

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

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 Same-beam VLBI observations of SELENE for improving lunar gravity field model

Radio Science ◽  
2010 ◽  
Vol 45 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Q. Liu ◽  
F. Kikuchi ◽  
K. Matsumoto ◽  
S. Goossens ◽  
H. Hanada ◽  
...  
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Lunar Gravity ◽  
Gravity Field Model ◽  
Lunar Gravity Field ◽  
Vlbi Observations

A Gravity Field Model Derived From the Short Dynamical Arcs of Champ

2007 ◽  
Vol 50 (1) ◽  
pp. 110-115 ◽  
Author(s):  
Xing-Fu ZHANG ◽  
Yun-Zhong SHEN ◽  
Lei-Ming HU
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Gravity Field Model
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