The Release 04 CHAMP and GRACE EIGEN Gravity Field Models

2010 ◽  
pp. 41-58 ◽  
Author(s):  
Frank Flechtner ◽  
Christoph Dahle ◽  
Karl Hans Neumayer ◽  
Rolf König ◽  
Christoph Förste
Keyword(s):  
Gravity Field ◽  
Gravity Field Models

Orbit determination of the SELENE satellites using multi-satellite data types and evaluation of SELENE gravity field models

2011 ◽  
Vol 85 (8) ◽  
pp. 487-504 ◽  
Author(s):  
S. Goossens ◽  
K. Matsumoto ◽  
D. D. Rowlands ◽  
F. G. Lemoine ◽  
H. Noda ◽  
...  
Keyword(s):  
Gravity Field ◽  
Satellite Data ◽  
Orbit Determination ◽  
Data Types ◽  
Gravity Field Models

Preliminary monthly gravity field models from kinematic orbits of SWARM Satellites

2021 ◽  
Author(s):  
Xingfu Zhang ◽  
Qiujie Chen ◽  
Yunzhong Shen
Keyword(s):  
Gravity Field ◽  
Large Scale ◽  
Earth System ◽  
The Earth ◽  
Swarm Satellites ◽  
Variable Gravity ◽  
Data Gap ◽  
One Year ◽  
Gravity Recovery ◽  
Gravity Field Models

<p>      Although the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE FO) satellite missions play an important role in monitoring global mass changes within the Earth system, there is a data gap of about one year spanning July 2017 to May 2018, which leads to discontinuous gravity observations for monitoring global mass changes. As an alternative mission, the SWARM satellites can provide gravity observations to close this data gap. In this paper, we are dedicated to developing alternative monthly time-variable gravity field solutions from SWARM data. Using kinematic orbits of SWARM from ITSG for the period January 2015 to September 2020, we have generated a preliminary time series of monthly gravity field models named Tongji-Swarm2019 up to degree and order 60. The comparisons between Tongji-Swarm2019 and GRACE/GRACE-FO monthly solutions show that Tongji-Swarm2019 solutions agree with GRACE/GRACE-FO models in terms of large-scale mass change signals over amazon, Greenland and other regions. We can conclude that Tongji-Swarm2019 monthly gravity field models are able to close the gap between GRACE and GRACE FO.</p>

Analyzing spectral characteristics of the global Earth gravity field models obtained from the CHAMP, GRACE and GOCE space missions

2015 ◽  
Vol 6 (2) ◽  
pp. 101-108 ◽  
Author(s):  
A. P. Karpik ◽  
V. F. Kanushin ◽  
I. G. Ganagina ◽  
D. N. Goldobin ◽  
E. M. Mazurova
Keyword(s):  
Gravity Field ◽  
Spectral Characteristics ◽  
Space Missions ◽  
Earth Gravity Field ◽  
Earth Gravity ◽  
Gravity Field Models

Regionally Refined Gravity Field Models from In-Situ Satellite Data

2010 ◽  
pp. 255-264 ◽  
Author(s):  
Annette Eicker ◽  
Torsten Mayer-Gürr ◽  
Karl-Heinz Ilk ◽  
Enrico Kurtenbach
Keyword(s):  
Gravity Field ◽  
Satellite Data ◽  
Gravity Field Models

scholarly journals Performance Comparison of Geoid Refinement between XGM2016 and EGM2008 Based on the KTH and RCR Methods: Jilin Province, China

2020 ◽  
Vol 12 (2) ◽  
pp. 324
Author(s):  
Qiong Wu ◽  
Hongyao Wang ◽  
Bin Wang ◽  
Shengbo Chen ◽  
Hongqing Li
Keyword(s):  
Gravity Field ◽  
Mean Square Error ◽  
Gravity Data ◽  
Jilin Province ◽  
Mountainous Area ◽  
Mean Square ◽  
Geoid Model ◽  
Plain Area ◽  
Gravity Field Models ◽  
Global Mean

The selection of an appropriate global gravity field model and refinement method can effectively improve the accuracy of the refined regional geoid model in a certain research area. We analyzed the accuracy of Experimental Geopotential Model (XGM2016) based on the GPS-leveling data and the modification parameters of the global mean square errors in the KTH geoid refinement in Jilin Province, China. The regional geoid was refined based on Earth Gravitational Model (EGM2008) and XGM2016 using both the Helmert condensation method with an RCR procedure and the KTH method. A comparison of the original two gravity field models to the GPS-leveling benchmarks showed that the accuracies of XGM2016 and EGM2008 in the plain area of Jilin Province are similar with a standard deviation (STD) of 5.8 cm, whereas the accuracy of EGM2008 in the high mountainous area is 1.4 cm better than that of XGM2016, which may be attributed to its low resolution. The modification parameters between the two gravity field models showed that the coefficient error of XGM2016 model was lower than that of EGM2008 for the same degree of expansion. The different modification limits and integral radii may produce a centimeter level difference in global mean square error, while the influence of the truncation error caused by the integral was at the millimeter level. The terrestrial gravity data error accounted for the majority of the global mean square error. The optimal least squares modification obtained the minimum global mean square error, and the global mean square error calculated based on XGM2016 model was reduced by about 1~3 cm compared with EGM2008. The refined geoid based on the two gravity field models indicated that both KTH and RCR method can effectively improve the STD of the geoid model from about six to three centimeters. The refined accuracy of EGM2008 model using RCR and KTH methods is slightly better than that of XGM2016 model in the plain and high mountain areas after seven-parameter fitting. EGM2008 based on the KTH method was the most precise at ± 2.0 cm in the plain area and ± 2.4 cm in the mountainous area. Generally, for the refined geoid based on the two Earth gravity models, KTH produced results similar to RCR in the plain area, and had relatively better performance for the mountainous area where terrestrial gravity data is sparse and unevenly distributed.

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