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

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 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.