scholarly journals Analysis of attitude errors in GRACE range-rate residuals - a comparison between SCA1B and the reprocessed attitude fused product (SCA1B +ACC1B)

2018 ◽  
Author(s):  
Sujata Goswami
Keyword(s):  
Gravity Field ◽  
Range Measurements ◽  
Precise Knowledge ◽  
Range Rate ◽  
The Impact ◽  
Temporal Gravity ◽  
Gravity Field Models ◽  
Fundamental Requirement

For the missions like GRACE, the precise knowledge of the satellite’s attitude is a fundamental requirement forthe realization of the inter-satellite ranging principle. It is not only essential for the realization of the precise in-orbit intersatellitepointing, but also for the recovery of accurate temporal gravity field models. Here we present a comparative studyof two attitude datasets. One of them is the standard SCA1B RL02 datasets provided by JPL NASA and another is a fusedattitude dataset computed at TU Graz, based on the combination of ACC1B angular accelerations and SCA1B quaternions.Further, we also present the impact of the attitude datasets on the inter-satellite range measurements by analyzing theirresiduals. Our analysis reveals the significant improvement in the attitude due to the reprocessed product and reducedvalue of residuals computed from the reprocessed attitude

The Impact of Temporal Gravity Variations on GOCE Gravity Field Recovery

2006 ◽  
pp. 255-269 ◽  
Author(s):  
Oleg Abrikosov ◽  
Focke Jarecki ◽  
Jürgen Müller ◽  
Svetozar Petrovic ◽  
Peter Schwintzer
Keyword(s):  
Gravity Field ◽  
Gravity Field Recovery ◽  
Gravity Variations ◽  
The Impact ◽  
Temporal Gravity

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 Gravity Field Recovery Using High-Precision, High–Low Inter-Satellite Links

2019 ◽  
Vol 11 (5) ◽  
pp. 537 ◽  
Author(s):  
Markus Hauk ◽  
Roland Pail
Keyword(s):  
Gravity Field ◽  
Radial Component ◽  
Mass Variation ◽  
The North ◽  
Alternative Processing ◽  
Gravity Field Recovery ◽  
Satellite Links ◽  
Error Behavior ◽  
The Impact ◽  
Temporal Gravity

Past temporal gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE), as well as current solutions from GRACE Follow-On, suffer from temporal aliasing errors due to undersampling of the signal to be recovered (e.g., hydrology), which arise in terms of stripes caused by the north–south observation direction. In this paper, we investigate the potential of the proposed mass variation observing system by high–low inter-satellite links (MOBILE) mission. We quantify the impact of instrument errors of the main sensors (inter-satellite link and accelerometer) and high-frequency tidal and non-tidal gravity signals on achievable performance of the temporal gravity field retrieval. The multi-directional observation geometry of the MOBILE concept with a strong dominance of the radial component result in a close-to-isotropic error behavior, and the retrieved gravity field solutions show reduced temporal aliasing errors of at least 30% for non-tidal, as well as tidal, mass variation signals compared to a low–low satellite pair configuration. The quality of the MOBILE range observations enables the application of extended alternative processing methods leading to further reduction of temporal aliasing errors. The results demonstrate that such a mission can help to get an improved understanding of different components of the Earth system.

scholarly journals Sensitivity of Lageos Orbits to Global Gravity Field Models

2012 ◽  
Vol 47 (2) ◽  
pp. 47-65 ◽  
Author(s):  
K. Sośnica ◽  
D. Thaller ◽  
A. Jäggi ◽  
R. Dach ◽  
G. Beutler
Keyword(s):  
Gravity Field ◽  
Orbit Determination ◽  
Measurement Techniques ◽  
Precise Orbit Determination ◽  
Satellite Orbits ◽  
Gravity Field Model ◽  
Global Gravity ◽  
The Impact ◽  
Gravity Field Models

Sensitivity of Lageos Orbits to Global Gravity Field ModelsPrecise orbit determination is an essential task when analyzing SLR data. The quality of the satellite orbits strongly depends on the models used for dynamic orbit determination. The global gravity field model used is one of the crucial elements, which has a significant influence on the satellite orbit and its accuracy. We study the impact of different gravity field models on the determination of the LAGEOS-1 and -2 orbits for data of the year 2008. Eleven gravity field models are compared, namely JGM3 and EGM96 based mainly on SLR, terrestrial and altimetry data, AIUB-CHAMP03S based uniquely on GPS-measurements made by CHAMP, AIUB-GRACE03S, ITG-GRACE2010 based on GRACE data, and the combined gravity field models based on different measurement techniques, such as EGM2008, EIGEN-GL04C, EIGEN51C, GOCO02S, GO-CONS-2-DIR-R2, AIUB-SST. The gravity field models are validated using the RMS of the observation residuals of 7-day LAGEOS solutions. The study reveals that GRACE-based models have the smallest RMS values (i.e., about 7.15 mm), despite the fact that no SLR data were used to determine them. The coefficient C20is not always well estimated in GRACE-only models. There is a significant improvement of the gravity field models based on CHAMP, GRACE and GOCE w.r.t. models of the pre-CHAMP era. The LAGEOS orbits are particularly sensitive to the long wavelength part of the gravity fields. Differences of the estimated orbits due to different gravity field models are noticeable up to degree and order of about 30. The RMS of residuals improves from about 40 mm for degree 8, to about 7 mm for the solutions up to degrees 14 and higher. The quality of the predicted orbits is studied, as well.

scholarly journals Signal and error assessment of GOCE-based high resolution gravity field models

2019 ◽  
Vol 9 (1) ◽  
pp. 71-86 ◽  
Author(s):  
T. Gruber ◽  
M. Willberg
Keyword(s):  
High Resolution ◽  
Gravity Field ◽  
Gravity Data ◽  
Geoid Height ◽  
Harmonic Series ◽  
Regional Data ◽  
Data Set ◽  
Global Models ◽  
The Impact ◽  
Gravity Field Models

Abstract The signal content and error level of recent GOCE-based high resolution gravity field models is assessed by means of signal degree variances and comparisons to independent GNSS-levelling geoid heights. The signal of the spherical harmonic series of these models is compared to the pre-GOCE EGM2008 model in order to identify the impact of GOCE data, of improved surface and altimetric gravity data and of modelling approaches. Results of the signal analysis show that in a global average roughly 80% of the differences are due to the inclusion of GOCE satellite information, while the remaining 20% are contributed by improved surface data. Comparisons of the global models to GNSS-levelling derived geoid heights demonstrate that a 1 cm geoid from the global model is feasible, if there is a high quality terrestrial gravity data set available. For areas with less good coverage an accuracy of several centimetres to a decimetre is feasible taking into account that GOCE provides now the geoid with a centimetre accuracy at spatial scales of 80 to 100 km. Comparisons with GNSS-levelling geoid heights also are a good tool to investigate possible systematic errors in the global models, in the spirit levelling and in the GNSS height observations. By means of geoid height differences and geoid slope differences one can draw conclusions for each regional data set separately. These conclusions need to be considered for a refined analysis e.g. to eliminate suspicious GNSS-levelling data, to improve the global modelling by using full variance-covariance matrices and by consistently weighting the various data sources used for high resolution gravity field models. The paper describes the applied procedures, shows results for these geoid height and geoid slope differences for some regional data sets and draws conclusions about possible error sources and future work to be done in this context.

Temporal gravity variations in GOCE release 6 gravitational gradients

2020 ◽  
Author(s):  
Betty Heller ◽  
Frank Siegismund ◽  
Roland Pail ◽  
Thomas Gruber
Keyword(s):  
Gravity Field ◽  
Spatial Scales ◽  
Low Frequency ◽  
Frequency Range ◽  
Gravitational Gradient ◽  
Grace Data ◽  
Gravitational Gradients ◽  
Gravity Variations ◽  
Temporal Gravity ◽  
Gravity Field Models

<p>As opposed to the level 1B release 5 GOCE gravitational gradient data, the newly reprocessed release 6 gradients provide reduced noise amplitudes in the low frequency-range, leading to reduced noise amplitudes of the derived gravity field models at large spatial scales, where temporal variations of the Earth’s gravity field have their highest amplitudes. This is the motivation to test the release 6 gradients for their ability to resolve temporal gravity variations.</p><p>For the gravity field processing, we apply a conventional spherical harmonics approach using the time-wise (TIM) processing method as well as a mass concentration (mascon) approach using point masses as base elements, which are grouped to land or ocean mascons by taking into account the coastlines.</p><p>By means of a closed-loop simulation study, we find that the colored instrument noise of the GOCE gravitational gradiometer introduces noise amplitudes into the derived gravity field models that lie above the amplitude of the gravity trend signal accumulated over 5 years. This indicates that detecting gravity variations taking place during the four-year GOCE data period from GOCE gradients only is challenging.</p><p>Using real GOCE data, we test bimonthly gradiometry-only gravity field models computed by both the spherical harmonic and the mascon approach for gravity signals that are resolved by GRACE data, being the temporal signals due to the ice mass trends in Greenland and Antarctica and the 2011 earthquake in Japan. Besides, corresponding GRACE/GOCE combination models are used to test whether the incorporation of GOCE data increases the resolution of temporal gravity signals.</p><p>We found that high-amplitude long-wavelength noise prevented the detection of temporal gravity variations among the bimonthly GOCE-only models. Using the SH approach, it was possible to detect the mean trend signal contained in the data by averaging multiple bimonthly models and considering their difference to a reference model. Using the mascon approach, trend signals contained in GOCE data could be recovered by including a GRACE model truncated to d/o 45 in a GRACE/GOCE combination model and thus let the GOCE data determine the short-scale signal structures instead of GRACE.</p><p>Finally, compared to the temporal gravity signal as resolved by GRACE data, no significant benefit of using or incorporating GOCE gravitational gradient data was found. The reason are the still rather high noise amplitudes in the derived models at large spatial scales, where the considered signal is strongest.</p><p>In order to detect temporal gravity variations in satellite gravitational gradiometry data, the measurement noise amplitudes in the low-frequency range would need to be reduced.</p>

On the impact of airborne gravity data to fused gravity field models

2016 ◽  
Vol 90 (6) ◽  
pp. 561-571 ◽  
Author(s):  
Dimitrios Bolkas ◽  
Georgia Fotopoulos ◽  
Alexander Braun
Keyword(s):  
Gravity Field ◽  
Gravity Data ◽  
Airborne Gravity ◽  
The Impact ◽  
Gravity Field Models

Temporal gravity field models inferred from GRACE data

2007 ◽  
Vol 39 (10) ◽  
pp. 1620-1629 ◽  
Author(s):  
Jean-Michel Lemoine ◽  
Sean Bruinsma ◽  
Sylvain Loyer ◽  
Richard Biancale ◽  
Jean-Charles Marty ◽  
...  
Keyword(s):  
Gravity Field ◽  
Grace Data ◽  
Temporal Gravity ◽  
Gravity Field Models

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>

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