Impact of Different Kinematic Empirical Parameters Processing Strategies on Temporal Gravity Field Model Determination

Zhou, Hao; Luo, Zhicai; Zhou, Zebing; Li, Qiong; Zhong, Bo; Lu, Biao; Hsu, Houtse

doi:10.1029/2018jb015556

Cited by 23 publications

(28 citation statements)

References 68 publications

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“…HUST: All simulation works were implemented with a full numerical mission simulator, which has been successfully used to develop the HUST gravity field models [40,41]. The simulation environment is based on numerical orbit integration.…”

Section: Methodsmentioning

confidence: 99%

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Next-Generation Gravity Missions: Sino-European Numerical Simulation Comparison Exercise

et al. 2019

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Temporal gravity retrieval simulation results of a future Bender-type double pair mission concept, performed by five processing centers of a Sino-European study team, have been inter-compared and assessed. They were computed in a synthetic closed-loop simulation world by five independent software systems applying different gravity retrieval methods, but were based on jointly defined mission scenarios. The inter-comparison showed that the results achieved a quite similar performance. Exemplarily, the root mean square (RMS) deviations of global equivalent water height fields from their true reference, resolved up to degree and order 30 of a 9-day solution, vary in the order of 10% of the target signal. Also, co-estimated independent daily gravity fields up to degree and order 15, which have been co-estimated by all processing centers, do not show large differences among each other. This positive result is an important pre-requisite and basis for future joint activities towards the realization of next-generation gravity missions.

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Section: Methodsmentioning

confidence: 99%

“…Kinematic empirical parameters for range-rates are estimated to reduce the 1-cpr effect in the residuals of range-rate observations. The filtered predetermined strategy (FPS) was applied to process these kinematic empirical parameters [40]. No stochastic observation model was used in this simulation work.…”

Section: Methodsmentioning

confidence: 99%

Next-Generation Gravity Missions: Sino-European Numerical Simulation Comparison Exercise

et al. 2019

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“…The dynamic approach is adopted in our gravity field model recovery [43], which is widely used in real GRACE data processing by different scientific institutions [7,8,[44][45][46]. In this section, we outline basic formulas of the dynamic approach used in the article, as well as relevant references for more detailed descriptions.…”

Section: Mathematical Model For Gravity Field Recoverymentioning

confidence: 99%

“…In the backward simulation step, we recover the monthly gravity field model up to 100 d/o [18] by the dynamic approach using above simulated observations. For parameterizations, we choose the orbit arc length as 6 h for the initial position and velocity estimation [44][45][46], and estimate the In the backward simulation step, we recover the monthly gravity field model up to 100 d/o [18] by the dynamic approach using above simulated observations. For parameterizations, we choose the orbit arc length as 6 h for the initial position and velocity estimation [44][45][46], and estimate the accelerometer parameters (bias and linear drift) every 1.5 h (orbital period) [45].…”

Section: Numerical Simulation Proceduresmentioning

confidence: 99%

Combination Analysis of Future Polar-Type Gravity Mission and GRACE Follow-On

2019

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Thanks to the unprecedented success of Gravity Recovery and Climate Experiment (GRACE), its successive mission GRACE Follow-On (GFO) has been in orbit since May 2018 to continue measuring the Earth’s mass transport. In order to possibly enhance GFO in terms of mass transport estimates, four orbit configurations of future polar-type gravity mission (FPG) (with the same payload accuracy and orbit parameters as GRACE, but differing in orbit inclination) are investigated by full-scale simulations in both standalone and jointly with GFO. The results demonstrate that the retrograde orbit modes used in FPG are generally superior to prograde in terms of gravity field estimation in the case of a joint GFO configuration. Considering the FPG’s independent capability, the orbit configurations with 89- and 91-degree inclinations (namely FPG-89 and FPG-91) are further analyzed by joint GFO monthly gravity field models over the period of one-year. Our analyses show that the FPG-91 basically outperforms the FPG-89 in mass change estimates, especially at the medium- and low-latitude regions. Compared to GFO & FPG-89, about 22% noise reduction over the ocean area and 17% over land areas are achieved by the GFO & FPG-91 combined model. Therefore, the FPG-91 is worthy to be recommended for the further orbit design of FPGs.

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“…The second approach uses the monthly gravity field solutions (GRACE Level-2 data) as spherical harmonic (SH) Stokes coefficients provided by GRACE analysis centers [5]. Due to the errors of the satellite instruments and the imperfections of the background models [6], the SH coefficients are seriously polluted by errors. Therefore, the direct use of GRACE Level-2 data to measure the surface mass changes of the Earth is affected by different kinds of noises, and if they are not filtered in the post processing, it is difficult to extract useful geophysical signals from the Level-2 data.…”

Section: Introductionmentioning

confidence: 99%

Separation and Recovery of Geophysical Signals Based on the Kalman Filter with GRACE Gravity Data

et al. 2019

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Monthly gravitational field solutions as spherical harmonic coefficients produced by the GRACE satellite mission require post-processing to reduce the effects of shortwave-length noises and north–south stripe errors. However, the spatial smoothing and de-striping filter commonly used in the post-processing step will either reduce spatial resolution or remove short-wavelength features of geophysical signals, mainly at high latitudes. Here, by using prior covariance information that reflects the spatial and temporal features of the geophysical signals and the correlated errors derived from the synthetic model, together with the covariance matrix of the formal errors for the monthly gravity spherical harmonic coefficients, we apply the Kalman filter to separate the geophysical signal from GRACE Level-2 data and simultaneously to estimate the correlated errors. By increasing the number of observations, the iterative process is applied to update the state vector and covariance in the Kalman filter because the prior information is not accurate. Due to the inevitable truncation error, multiple gridded-gain factors method considering different temporal frequencies has been developed to recover the geophysical signal. The results show that the Kalman filter can reduce the high-frequency noises and correlated errors remarkably. When compared with the commonly used filter, no spatial filter (such as Gaussian filter) is used in the Kalman filter. Therefore, the estimated signal preserves its natural resolution, and more detailed information is retained. It shows good consistency when compared with mascon solutions in both secular trend and annual amplitude.

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Impact of Different Kinematic Empirical Parameters Processing Strategies on Temporal Gravity Field Model Determination

Cited by 23 publications

References 68 publications

Next-Generation Gravity Missions: Sino-European Numerical Simulation Comparison Exercise

Next-Generation Gravity Missions: Sino-European Numerical Simulation Comparison Exercise

Combination Analysis of Future Polar-Type Gravity Mission and GRACE Follow-On

Separation and Recovery of Geophysical Signals Based on the Kalman Filter with GRACE Gravity Data

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