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

Liu, X.

doi:10.54419/rmsi6z

Cited by 37 publications

(14 citation statements)

References 71 publications

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“…Monthly GRACE solutions are computed by CSR (Bettadpur, 2007), GFZ (Flechtner, 2007), and JPL (Watkins and Yuan, 2007). DEOS optimally filtered monthly GRACE gravity fields are computed up to degree 120 (Liu, 2008;Klees et al, 2008b). Computing 10-day solutions is possible and regularly done by CNES, albeit at lower resolution (Biancale et al, 2007).…”

Section: Gravity Field Modelling From Satellite Datamentioning

confidence: 99%

“…where R (T →C) is the rotation matrix from TRF to CRF, and R (C→LoS.x) is the matrix that projects a 3D-vector defined in the CRF onto the x-axis of the LoS vector, which is directed along the line-of-sight (LoS) (Liu, 2008). E is the averaging filter that computes the averaged three-point accelerations (Ditmar and van Eck, 2004).…”

Section: Residual Accelerationsmentioning

confidence: 99%

“…where σ is the standard deviation of the k-band ranges, Δt is the sampling rate, and τ is the filter halfwidth. For the computation of the DEOS global models, the values used were σ = 40 μm, Δt = 5 s, and τ = 30 s (Liu, 2008).…”

Section: Stochastic Modelmentioning

confidence: 99%

“…It has been shown by Liu (2008) that in the case of the 3-point range combination approach, use of the stochastic model in eq. ( 5.21), instead of the assumption of white noise, does not lead to significant differences up to spherical harmonic degree 40, and small differences (about 5%) up to degree 120.…”

Section: Stochastic Modelmentioning

confidence: 99%

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Regional gravity field modeling with radial basis functions

Wittwer¹

2009

Publications on Geodesy

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This PhD thesis and the underlying research would not have been possible without the help of many people. First and foremost I want to thank my supervisor and promotor, Prof. Roland Klees, for his help and guidance during the five years of research. He was always available regardless of his busy schedule, and managed to keep me on track while giving me the freedom to pursue my own ideas.Valuable input came from the other scientific staff members, Pavel Ditmar, Jürgen Kusche, Brian Gunter, and David Lavalle. Invaluable help was provided by the "global GRACE people", Xianglin Liu and Christian Siemes, who also shared a room with me and were always good company. Other colleagues contributed, too:

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Section: Gravity Field Modelling From Satellite Datamentioning

confidence: 99%

Section: Residual Accelerationsmentioning

confidence: 99%

Section: Stochastic Modelmentioning

confidence: 99%

Section: Stochastic Modelmentioning

confidence: 99%

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Regional gravity field modeling with radial basis functions

Wittwer¹

2009

Publications on Geodesy

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“…(4.75) allows a direct analytical implementation of the corresponding noise covariance matrix (see e.g. Liu, 2008). Alternatively, the PSD function can be used to derive an ARMA model as discussed in the previous subsection, which is used in the low-level PCCG scheme as outlined in section 4.4.2.…”

Section: Analytical Filteringmentioning

confidence: 99%

Regional gravity field modeling using airborne gravimetry data

Alberts¹

2009

Publications on Geodesy

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Regional gravity field modeling using airborne gravimetry dataAirborne gravimetry is the most efficient technique to provide accurate high-resolution gravity data in regions that lack good data coverage and that are difficult to access otherwise. With current airborne gravimetry systems gravity can be obtained at a spatial resolution of 2 km with an accuracy of 1-2 mGal. It is therefore an ideal technique to complement ongoing satellite gravity missions and establish the basis for many applications of regional gravity field modeling.Gravity field determination using airborne gravity data can be divided in two major steps. The first step comprises the pre-processing of raw in-flight gravity sensor measurements to obtain gravity disturbances at flight level and the second step consists of the inversion of these observations into gravity functionals at ground level. The preprocessing of airborne gravity data consists of several independent steps such as low-pass filtering, a cross-over adjustment to minimize misfits at cross-overs of intersecting lines, and gridding. Each of these steps may introduce errors that accumulate in the course of processing, which can limit the accuracy and the resolution of the resulting gravity field.For the inversion of the airborne gravity data at flight level into gravity functionals at the Earth's surface, several approaches can be used. Methods that have been successfully applied to airborne gravity data are integral methods and least-squares collocation, but both methods have some disadvantages. Integral methods require that the data are available in a much larger area than for which the gravity functionals are computed. A large cap size is required to reduce edge effects that result from missing data outside the target area. Least-squares collocation suffers much less from these errors and can yield accurate results, provided that the auto-covariance function gives a good representation of data in-and outside the area. However, the number of base functions equals the number of observations, which makes least-squares collocation numerically less efficient.In this thesis a new methodology for processing airborne gravity data is proposed. It combines separate pre-processing steps with the estimation of gravity field parameters in one algorithm. Importantly, the concept of low-pass filtering is replaced by a frequencydependent data weighting to handle the strong colored noise in the data. Frequencies at which the noise level is high get a lower weight than frequencies at which the noise level is low. Furthermore, bias parameters are estimated jointly with gravity field parameters instead of applying a cross-over adjustment. To parameterize the gravity potential a spectral representation is used, which means that the estimation results in a set of coeffivii Summary cients. These coefficients are used to compute gravity functionals at any location on the Earth's surface within the survey area. The advantage of the developed approach is that it requires a minimum of pre-processing and th...

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

Cited by 37 publications

References 71 publications

Regional gravity field modeling with radial basis functions

Regional gravity field modeling with radial basis functions

Regional gravity field modeling using airborne gravimetry data

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

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