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See detailAn optimized short-arc approach: methodology and application to develop refined time series of Tongji-Grace2018 GRACE monthly solutions
Chen, Qiujie; Shen, Yunzhong; Chen, Wu et al

in Journal of Geophysical Research. Solid Earth (2019), 124(6), 6010-6038

Abstract 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), an optimized short-arc method ... [more ▼]

Abstract 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), 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 the optimized short-arc method is able to stabilize the inversion and eventually prolong the arc length to 6 hours. 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 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. [less ▲]

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See detailAn improved accelerometer calibration model for gravity field estimates
Chen, Qiujie; Francis, Olivier UL; Shen, Yunzhong et al

Poster (2018, April)

During gravity field modelling, accelerometer measurements must be calibrated via scale and bias parameters. Klinger and Mayer-Gürr (2016) found that behaviors of both scales and biases are related to the ... [more ▼]

During gravity field modelling, accelerometer measurements must be calibrated via scale and bias parameters. Klinger and Mayer-Gürr (2016) found that behaviors of both scales and biases are related to the thermal control service for the accelerometers. This finding indicates that the scales and biases may change significantly after April 2011 as the thermal control service has been switched off since then. To improve gravity field estimates, the time-related variations in either scales or biases should be better modelled. For the purpose of considering the time-dependent changes of scales and biases, we propose an improved accelerometer calibration model in this study, where the scales and biases are modelled by polynomials besides estimating the errors of attitude and accelerometer data. Detailed discussions on the selection of the optimal orders of polynomials for scales and biases, their time-dependent changes and the benefits from the improved accelerometer calibration model are given in this investigation. Compared to other accelerometer calibration models, the improved model has the comparable ability to calibrate the accelerometer measurements, while it achieves better conditioned normal equation and noticeable improvement in gravity field determination. [less ▲]

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