From Kinematic Orbit Determination to Derivation of Satellite Velocity and Gravity Field

Švehla, Dražen; Földváry, Lóránt

doi:10.1007/3-540-29522-4_13

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…Švehla & Rothacher 2005), whilst for intersatellite baselines the accuracy increases by about one order of magnitude to 1–5 mm (Kroes et al 2005; Švehla & Rothacher 2005). Accuracies of absolute velocities from numerical differentiation are expected to be of the order of some 0.1–0.3 mm s –1 (Švehla & Földváry 2006). Assuming the same ratio between position and velocity errors for both absolute and relative positioning, we find an expected error level for the baseline velocities on the level of about 0.01 mm s –1 (see Table 1).…”

Section: Methodsmentioning

confidence: 99%

Time-variable gravity field from satellite constellations using the energy integral

Wang

Gerlach

Rummel

2012

Geophysical Journal International

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SUMMARY The magnetic field mission Swarm, expected to be launched in 2012, comprises a constellation of three satellites. As all of them are equipped with GPS receivers and accelerometers, they can be used for gravity field recovery. We study the capability of a Swarm‐like constellation for (time‐variable) gravity field recovery and compare it with a gravity field tandem mission of GRACE‐type. Due to the lower accuracy of the GPS measurements compared with GRACE low–low satellite‐to‐satellite tracking (SST), the accuracy of a Swarm derived gravity field cannot compete with the state‐of‐the‐art GRACE models. However, unlike the GRACE mission, Swarm allows for the derivation of GPS‐baselines between the satellites in directions other than purely along‐track. This makes Swarm an interesting case for studying the error structure of gravity field models derived from various constellation geometries. Therefore, one focus of this study is the general analysis of different baseline constellations independent of the observation accuracy, that is, we do not restrict ourselves to just the actual Swarm case of pure GPS‐baselines. To make the results comparable to GRACE, we explicitly study the error behaviour of the different Swarm baseline geometries assuming GRACE‐type K‐band ranging (KBR) links. This gives an indication of candidate constellations for future missions; at least in regards to the general error structure. To study the potential of Swarm for recovering time‐variable components of the gravity field, we have set up a 2‐yr simulation to recover annual and semi‐annual components of continental hydrology. We show that Swarm has the potential to recover the annual signal up to spherical harmonic degree 6. This is of interest should there be a gap between the end of the GRACE mission and the launch of a follow‐on mission. All our simulations make use of the energy integral method. This method is usually (implicitly) formulated for static potential fields. Therefore, it is necessary for our study to investigate the properties of this method when applied to the analysis of time‐variable fields.

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Section: Methodsmentioning

confidence: 99%

Time-variable gravity field from satellite constellations using the energy integral

Wang

Gerlach

Rummel

2012

Geophysical Journal International

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“…Satellites of the mission called Gravity Recovery and Climate Experiment (GRACE) were launched and placed in orbit in 2002, and provide information about the temporal variation of the gravity field of the Earth (Tapley et al, 2004). Satellites are on a low, 500-km orbit at a distance from each other about 220 kilometers -between 170 and 270 km (Svehla and Foldvary, 2006;Somodi and Foldvary, 2011). Deviations of the gravitational field of the Earth cause a slight change in the distance between the two GRACE satellites.…”

Section: Grace Datamentioning

confidence: 99%

The Creation of Flood Risks Model Using a Combination of Satellite and Meteorological Models _ the first step

Biryło¹

2015

Acta Geodynamica Et Geomaterialia

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“…The kinematic baselines have relatively large errors, and the magnitude of the vector ṙ⊥ c is rather significant: about 250 m/s. Assume that noise of along-track and cross-track component of velocity differences is at the level of ±0.5 × 10 −3 m/s, whereas the radial component may have 2 times higher noise than other two components [ Švehla and Földváry, 2006]. Because the orthogonal component to the LOS is comprised of the cross-track and radial components, its noise could be at the level of ± √ 5 × 0.5 × 10 −3 m/s.…”

Section: Classical Acceleration Approachmentioning

confidence: 99%

Global gravity field recovery from satellite-to-satellite tracking data with the acceleration approach

Liu¹

2008

Publications on Geodesy

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Global gravity field recovery from satellite-to-satellite tracking data with the acceleration approachThis thesis is focused on the development of new techniques for global gravity field recovery from high-low (hl) and low-low (ll) satellite-to-satellite tracking (SST) data. There are a number of approaches to global gravity field recovery known from literature, including the variational equations approach, short arc approach, energy balance approach and acceleration approach. The focus of the thesis is the acceleration approach with an aim to produce high-quality global gravity field models using real data from CHAMP and GRACE satellite missions.In the first part, the research is devoted to a refinement of CHAMP hl-SST data processing methodology, which was developed at DEOS earlier. The refinement includes two major updates. The first update is usage of smoothed kinematic orbits, instead of reduced-dynamic ones, in data processing. A procedure based on B-splines has been developed for smoothing kinematic orbits by means of a regularised least-squares adjustment. The second update is the implementation of a data noise estimation procedure from the data themselves, with the aim to obtain a statistically optimal gravity field solution. The refined procedure is used to compute both regularised and a non-regularised models from a nearly one-year set of CHAMP accelerations. The regularized model is proved to be better than the regularized ITG-CHAMP01E model, and slightly better than the older DEOS CHAMP-01C 70 model computed at DEOS. The non-regularized solution is compared to a few non-regularized CHAMP-only models produced by several research groups. The comparison shows that the obtained solution clearly outperforms most of the alternative models.In the second part of the research, the methodology of processing CHAMP hl-SST data is extended to the case of GRACE hl-SST data, including the GRACE kinematic baselines. The kinematic positions and baselines are processed both individually and jointly. It is found that the kinematic baselines themselves are, in general, not favorable for the derivation of gravity field models. We explain this, first of all, by a poor sensitivity of the baseline data to East-West variations of the gravity field. Nevertheless, kinematic baselines slightly improve the quality xii Abstract of gravity field modeling if added to a set of kinematic positions.In the third part of the research, two innovative methodologies of gravity field modeling from GRACE ll-SST data, i.e. so-called 3-point Range Rate Combination (3RRC) approach and 3-point Range Combination (3RC) approach, are developed as extensions of the classic acceleration approach. Corresponding functional models are derived and a comprehensive procedure for processing real GRACE data is developed. The data processing procedure contains two major steps: pre-processing and inversion. The pre-processing includes the computation of purely dynamic orbits as reference ones on the basis of state-of-the-art background models of static and rap...

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From Kinematic Orbit Determination to Derivation of Satellite Velocity and Gravity Field

Cited by 8 publications

References 12 publications

Time-variable gravity field from satellite constellations using the energy integral

Time-variable gravity field from satellite constellations using the energy integral

The Creation of Flood Risks Model Using a Combination of Satellite and Meteorological Models _ the first step

Global gravity field recovery from satellite-to-satellite tracking data with the acceleration approach

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