A technique for modeling the Earth?s gravity field on the basis of satellite accelerations

Ditmar, Pavel; Sluijs, A. van Eck van A. der

doi:10.1007/s00190-003-0362-1

Cited by 60 publications

(52 citation statements)

References 15 publications

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“…In this case, the exploited functional model would provide a unique link between the unknown parameters and the double-differentiated observations (namely, inter-satellite ranges). As shown by Ditmar and van Eck van der Sluijs (2004), the solution based on this functional model coincides with the solution obtained by the inversion of the original observations (i.e., ranges) themselves or their first-order derivatives (i.e., rangerates), provided that the statistically optimal estimation procedure is followed. In reality, the correction for centrifugal accelerations is not error-free.…”

Section: Discussionmentioning

confidence: 52%

“…can also be produced from a time series of point-wise intersatellite accelerations by applying an appropriate filter, called by Ditmar and van Eck van der Sluijs (2004) an "averaging filter". Let a (x i ) be a vector composed of 2n + 1 point-wise inter-satellite accelerations projected on the axis x i in the inertial frame (this axis is defined such that it coincides with the x-axis in the local frame at the time t i ).…”

Section: Functional Modelmentioning

confidence: 99%

“…At low frequencies, average and point-wise accelerations are almost identical. It is worth adding that the concept of range combinations is very similar to the concept of 3-D average accelerations of a single satellite (Ditmar and van Eck van der Sluijs 2004), which has been successfully applied, for instance, to gravity field modeling on the basis of CHAllenging Minisatellite Payload (CHAMP) satellite data (Ditmar et al 2006).…”

Section: Functional Modelmentioning

confidence: 99%

“…(6). According to Ditmar and van Eck van der Sluijs (2004), these two approaches are equivalent. We have implemented both approaches and verified that the obtained results indeed coincide (within a computation error).…”

Section: Computation Of Data Noisementioning

confidence: 99%

See 3 more Smart Citations

Understanding data noise in gravity field recovery on the basis of inter-satellite ranging measurements acquired by the satellite gravimetry mission GRACE

Ditmar

Encarnação

Farahani

2011

J Geod

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Spectral analysis of data noise is performed in the context of gravity field recovery from inter-satellite ranging measurements acquired by the satellite gravimetry mission GRACE. The motivation of the study is two-fold: (i) to promote a further improvement of GRACE data processing techniques and (ii) to assist designing GRACE follow-on missions. The analyzed noise realizations are produced as the difference between the actual GRACE inter-satellite range measurements and the predictions based on state-of-the-art force models. The exploited functional model is based on the so-called "range combinations," which can be understood as a finite-difference analog of inter-satellite accelerations projected onto the line-of-sight connecting the satellites. It is shown that low-frequency noise is caused by limited accuracy of the computed GRACE orbits. In the first instance, it leads to an inaccurate estimation of the radial component of the inter-satellite velocities. A large impact of this component stems from the fact that it is directly related to centrifugal accelerations, which have to be taken into account when the measured range-accelerations are linked with inter-satellite accelerations. Another effect of orbit inaccuracies is a miscalculation of forces acting on the satellites (particularly, the one described by the zero-degree term of the Earth's gravitational field). The major contributors to the noise budget at high frequencies (above 9 mHz) are (i) ranging sensor errors and (ii) limited knowledge of the Earth's static gravity field at high degrees. Importantly, we show that updating the model of the static field on the basis of the available data must be performed with a caution as the result may not be physical due to a non-unique recovery of high-degree coefficients. The source of noise in the range of intermediate frequencies (1-9 mHz), which is particularly critical for an accurate gravity field recovery, is not fully understood yet. We show, however, that it cannot be explained by inaccuracies in background models of time-varying gravity field. It is stressed that most of the obtained results can be treated as sufficiently general (i.e., applicable in the context of a statistically optimal estimation based on any functional model).

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Section: Discussionmentioning

confidence: 52%

Section: Functional Modelmentioning

confidence: 99%

Section: Functional Modelmentioning

confidence: 99%

Section: Computation Of Data Noisementioning

confidence: 99%

See 2 more Smart Citations

Understanding data noise in gravity field recovery on the basis of inter-satellite ranging measurements acquired by the satellite gravimetry mission GRACE

Ditmar

Encarnação

Farahani

2011

J Geod

View full text Add to dashboard Cite

show abstract

“…Furthermore, the a priori field is an approximation of the signal and might cause systematic effects. At the same time, it might be explainable by a reduced ability to handle correlated noise as high-frequency base functions are able to absorb part of this type of noise (Ditmar and van Eck van der Sluijs, 2004). In the reduction step, only the deterministic gravity signal has been removed.…”

Section: Utilization Of a Priori Informationmentioning

confidence: 99%

On the influence of the ground track on the gravity field recovery from high–low satellite-to-satellite tracking missions: CHAMP monthly gravity field recovery using the energy balance approach revisited

Weigelt

Sideris

Sneeuw

2009

J Geod

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In this paper, the influence of the ground track coverage on the quality of a monthly gravity field solution is investigated for the scenario of a high-low satellite-tosatellite tracking mission. Data from the CHAllenging Minisatellite Payload (CHAMP) mission collected in the period April 2002 to February 2004 has been used to recover the gravity field to degree and order 70 on a monthly basis. The quality is primarily restricted by the accuracy of the instruments. Besides, CHAMP passed through a 31 /2 repeat mode three times

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Monthly gravity field models derived from GRACE Level 1B data using a modified short‐arc approach

et al. 2015

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In this study, a new time series of Gravity Recovery and Climate Experiment (GRACE) monthly solutions, complete to degree and order 60 spanning from January 2003 to August 2011, has been derived based on a modified short-arc approach. Our models entitled Tongji-GRACE01 are available on the website of International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/ICGEM/). The traditional short-arc approach, with no more than 1 h arcs, requires the gradient corrections of satellite orbits in order to reduce the impact of orbit errors on the final solution. Here the modified short-arc approach has been proposed, which has three major differences compared to the traditional one: (1) All the corrections of orbits and range rate measurements are solved together with the geopotential coefficients and the accelerometer biases using a weighted least squares adjustment; (2) the boundary position parameters are not required; and (3) the arc length can be extended to 2 h. The comparisons of geoid degree powers and the mass change signals in the Amazon basin, the Antarctic, and Antarctic Peninsula demonstrate that our model is comparable with the other existing models, i.e., the Centre for Space Research RL05, Jet Propulsion Laboratory RL05, and GeoForschungsZentrum RL05a models. The correlation coefficients of the mass change time series between our model and the other models are better than 0.9 in the Antarctic and Antarctic Peninsula. The mass change rates in the Antarctic and Antarctic Peninsula derived from our model are À92.7 ± 38.0 Gt/yr and À23.9 ± 12.4 Gt/yr, respectively, which are very close to those from other three models and with similar spatial patterns of signals.

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A technique for modeling the Earth?s gravity field on the basis of satellite accelerations

Cited by 60 publications

References 15 publications

Understanding data noise in gravity field recovery on the basis of inter-satellite ranging measurements acquired by the satellite gravimetry mission GRACE

Understanding data noise in gravity field recovery on the basis of inter-satellite ranging measurements acquired by the satellite gravimetry mission GRACE

On the influence of the ground track on the gravity field recovery from high–low satellite-to-satellite tracking missions: CHAMP monthly gravity field recovery using the energy balance approach revisited

Monthly gravity field models derived from GRACE Level 1B data using a modified short‐arc approach

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