AIUB-RL02: an improved time-series of monthly gravity fields from GRACE data

Meyer, Ulrich; Jäggi, Adrian; Jean, Yoomin; Beutler, Gerhard

doi:10.1093/gji/ggw081

Cited by 52 publications

(56 citation statements)

References 27 publications

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“…We believe that the adopted accuracies of KRR and RDO observations are fully justified. They are consistent with those assumed by other groups (Chen et al, ; Meyer et al, ) and promise the best monthly solutions when applying either data weighting scheme to KRR data.…”

Section: Methodssupporting

confidence: 88%

Improvements in the Monthly Gravity Field Solutions Through Modeling the Colored Noise in the GRACE Data

et al. 2018

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The Gravity Recovery And Climate Experiment (GRACE) mission has achieved a quantum leap in knowledge of the Earth's gravity field. However, current gravity field solutions still cannot reach the prelaunch baseline accuracy. One of the reasons for that is the presence of colored noise in GRACE data, which is typically ignored in the classical dynamic approach to gravity field modeling. In this research, we propose to account for colored noise in the classical dynamic approach by applying the frequency‐dependent data weighting (FDDW) scheme, so that enhanced estimates of gravity field solutions are produced. The monthly solutions are compared with those produced using the standard least squares adjustment without a data weighting scheme. The comparison is performed in both spectral and spatial domains, showing the positive effect of the FDDW scheme in all considered cases. For instance, the cumulative geoid height errors up to degree 96 are reduced by 18%. In the spatial domain, the FDDW scheme lowers noise level in mass changes over the oceans, Mississippi river basin, and Greenland by 20, 38, and 23%, respectively, when compared to the without a data weighting scheme. In addition, the consistency of mass changes over the Mississippi and Congo river basins with those inferred from the state‐of‐the‐art hydrology model WaterGAP is substantially improved when the FDDW scheme is applied. These results indicate that modeling colored noise in the GRACE data allows to significantly improve the recovered monthly solutions. This finding is likely applicable also to the GRACE Follow‐On mission.

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Section: Methodssupporting

confidence: 88%

Improvements in the Monthly Gravity Field Solutions Through Modeling the Colored Noise in the GRACE Data

et al. 2018

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“…Apart from three models published by SDS, the comparison results for some additional temporal gravity field model were also included, for instance Tongji02 (Q. Chen et al, ), ITSG‐Grace2016 (Klinger & Mayer‐Gürr, ), and AIUB RL02 (Meyer et al, ). The annual amplitudes of Amazon, yearly trends of Greenland, RMSEs of the Sahara Desert (12° × 6°), and the middle of the Pacific Ocean (12° × 6°) were independently estimated by every model.…”

Section: Results Based On Grace Datamentioning

confidence: 99%

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

et al. 2018

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During temporal gravity field model determination, the kinematic empirical parameters are mainly designed to remove the strong bias, drift, and 1‐cycle per revolution variations in range‐rates. In practice, two different strategies are commonly used to process these empirical parameters. One is to determine the empirical parameters before solving spherical harmonic coefficients, called Pure Predetermined Strategy (PPS). The other is to simultaneously determine the empirical parameters and spherical harmonic coefficients, called Pure Simultaneous Strategy (PSS). In this study, apart from these two strategies, a novel processing strategy called Filter Predetermined Strategy (FPS) is also discussed. These different processing strategies may result in different solutions. With the Gravity Recovery and Climate Experiment Level 1B data spanning 2005 to 2010, the impacts of different kinematic empirical parameters processing strategies were assessed in detail. The numerical results indicate that (1) using three different processing strategies and their hybrids can determine the temporal gravity field model, while (2) the solutions via PPS present apparent temporal signal attenuation, which is approximately 15% lower in annual amplitude in Amazon River Basin, and 15% lower in yearly trend in Greenland, and (3) the signal‐to‐noise ratios of the solutions via PPS are generally smaller than those of the solutions via FPS and PSS, and (4) the performance of FPS is superior in terms of postfit range‐rates, but compatible with PSS in terms of other cross comparisons. According to comprehensive comparison results in terms of temporal signals and noise, the performance of our Huazhong University of Science and Technology models determined via FPS is in excellent accordance with other representative temporal gravity field models, such as CSR RL05, GFZ RL05a, and JPL RL05.

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“…The vector x j to be estimated includes global (gravity field coefficients) and local (boundary positions and accelerometer scales and biases) parameters as elaborated in section . Because Meyer et al () showed that calibrating accelerometer data via daily scales can considerably mitigate the impacts of the solar activity on the derived gravity field models, we estimate daily accelerometer scales in three axes for both satellites throughout this study. To model any possible time‐related variation in daily accelerometer biases, the accelerometer bias in each axis of the accelerometer is treated as a 5‐order polynomial for each day in accordance with Chen et al ().…”

Section: Development Of Tongji‐grace2018 Monthly Solutionsmentioning

confidence: 99%

“…Note that both IGG RL01 and HUST‐Grace2016 are based on RL05 processing standards, but the others are all based on RL06 processing standards. It is well known that the GRACE‐based geopotential coefficients at low degrees (particularly below degree 30) are generally dominated by gravity field signals, while the high‐degree coefficients are contaminated by noise (Chen et al, ; Meyer et al, ). As presented in Figure , the signal levels (approximately below degree 30) of Tongji‐Grace2018 for both months are in good agreement with those models on the basis of the RL06 processing standards.…”

Section: Noise Analyses Of Monthly Gravity Field Solutionsmentioning

confidence: 99%

“…Though the GRACE mission ended operation last year due to degradation of the batteries, over 15 years of measurements collected by the GRACE mission have brought us unprecedented understanding of the Earth's mass transport processes. Various data processing methods to the GRACE measurements have been developed, resulting in various time‐variable gravity field models in terms of unconstrained spherical harmonics (Bettadpur, ; Chen et al, ; Dahle et al, ; Mayer‐Gürr et al, ; Meyer et al, ; Yuan, ; Guo & Zhao, ; Wang et al, ), regularized or filtered spherical harmonics (Farahani et al, ; Lemoine et al, ), and mascon grids (Luthcke et al, ; Save et al, ; Watkins et al, ). As the traditional and established representation of gravity field solutions, the spherical harmonic models are usually applied to most of geophysical signal analyses (Chen et al, ; Schumacher et al, ; Velicogna & Wahr, ).…”

Section: Introductionmentioning

confidence: 99%

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An Optimized Short‐Arc Approach: Methodology and Application to Develop Refined Time Series of Tongji‐Grace2018 GRACE Monthly Solutions

Chen

Shen

et al. 2019

JGR Solid Earth

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Considering the unstable inversion of ill‐conditioned intermediate matrix required in each integral arc in the short‐arc approach presented in Chen et al. (2015, https://doi.org/10.1002/2014JB011470), an optimized short‐arc method via stabilizing the inversion is proposed. To account for frequency‐dependent noise in observations, a noise whitening technique is implemented in the optimized short‐arc approach. Our study shows that the optimized short‐arc method is able to stabilize the inversion and eventually prolong the arc length to 6 hr. In addition, the noise whitening method is able to mitigate the impacts of low‐frequency noise in observations. Using the optimized short‐arc approach, a refined time series of Gravity Recovery and Climate Experiment (GRACE) monthly models called Tongji‐Grace2018 has been developed. The analyses allow us to derive the following conclusions: (a) During the analyses over the river basins (i.e., Amazon, Mississippi, Irrawaddy, and Taz) and Greenland, the correlation coefficients of mass changes between Tongji‐Grace2018 and others (i.e., CSR RL06, GFZ RL06, and JPL RL06 Mascon) are all over 92% and the corresponding amplitudes are comparable; (b) the signals of Tongji‐Grace2018 agree well with those of CSR RL06, GFZ RL06, ITSG‐Grace2018, and JPL RL06 Mascon, while Tongji‐Grace2018 and ITSG‐Grace2018 are less noisy than CSR RL06 and GFZ RL06; (c) clearer global mass change trend and less striping noise over oceans can be observed in Tongji‐Grace2018 even only using decorrelation filtering; and (d) for the tests over Sahara, over 36% and 19% of noise reductions are achieved by Tongji‐Grace2018 relative to CSR RL06 in the cases of using decorrelation filtering and combined filtering, respectively.

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AIUB-RL02: an improved time-series of monthly gravity fields from GRACE data

Cited by 52 publications

References 27 publications

Improvements in the Monthly Gravity Field Solutions Through Modeling the Colored Noise in the GRACE Data

Improvements in the Monthly Gravity Field Solutions Through Modeling the Colored Noise in the GRACE Data

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

An Optimized Short‐Arc Approach: Methodology and Application to Develop Refined Time Series of Tongji‐Grace2018 GRACE Monthly Solutions

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