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.

In recent years, the enormous losses caused by urban surface deformation have received more and more attention. Traditional geodetic techniques are point-based measurements, which have limitations in using traditional geodetic techniques to detect and monitor in areas where geological disasters occur. Therefore, we chose Interferometric Synthetic Aperture Radar (InSAR) technology to study the surface deformation in urban areas. In this research, we discovered the land subsidence phenomenon using InSAR and Global Navigation Satellite System (GNSS) technology. Two different kinds of time-series InSAR (TS-InSAR) methods: Small BAseline Subset (SBAS) and the Permanent Scatterer InSAR (PSI) process were executed on a dataset with 31 Sentinel-1A Synthetic Aperture Radar (SAR) images. We generated the surface deformation field of Shenzhen, China and Hong Kong Special Administrative Region (HKSAR). The time series of the 3d variation of the reference station network located in the HKSAR was generated at the same time. We compare the characteristics and advantages of PSI, SBAS, and GNSS in the study area. We mainly focus on the variety along the coastline area. From the results generated by SBAS and PSI techniques, we discovered the occurrence of significant subsidence phenomenon in the land reclamation area, especially in the metro construction area and the buildings with a shallow foundation located in the land reclamation area.

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.

In order to derive high‐precision static Gravity Recovery and Climate Experiment (GRACE)‐only gravity field solutions, the following strategies were implemented in this study: (1) a refined accelerometer calibration model that treats monthly accelerometer scales as a third‐order polynomial and daily accelerometer biases as a fifth‐order polynomial was developed to calibrate accelerometer measurements; (2) the errors of the acceleration and attitude data were estimated together with the geopotential coefficients and accelerometer parameters on the basis of the weighted least squares adjustments; (3) a nearly complete observation series of GRACE mission was used to decrease the condition number of normal equation; and (4) the GRACE data collected in lower orbit altitude were also included to decrease the condition number. Our results show that (1) the refined accelerometer calibration model with much less parameters performs as well as previous methods (i.e., solving daily scales and hourly biases or estimating biases along with bias rates every 2 hr). However, it provides a system of more stable normal equation and less high‐frequency noise in gravity field solutions; (2) high‐frequency noise in the gravity field solution is reduced by modeling the errors of the acceleration and attitude data; (3) the geopotential coefficients at all degrees is greatly enhanced by using longer GRACE time series (especially the data by the end of 2010); and (4) due to lower orbit altitude, the GRACE data collected since 2014 lead to a significant improvement of the gravity field solution as the satellites are more sensitive to higher‐frequency signal. Using the refined strategies, an unconstrained static solution (named Tongji‐Grace02s) up to degree and order 180 was derived. For further suppressing the high‐frequency noise, a regularization strategy based on the Kaula rule is applied to the degrees and orders beyond 80, leading to a regularized model Tongji‐Grace02k. To validate the quality of the derived models, both Tongji‐Grace02s and Tongji‐Grace02k were compared to the latest GRACE‐only models (i.e., GGM05S, ITU_GRACE16, ITSG‐Grace2014s, and ITSG‐Grace2014k) and validated using independent data (i.e., Global Navigation Satellite Systems (GNSS)/Leveling data and DTU13 oceanic gravity data). Compared to other models, much less spatial noise in terms of global gravity anomalies with respect to the state‐of‐the‐art model EIGEN6C4 and far higher accuracy at high degrees are achieved by Tongji‐Grace02s. The same conclusions can be drawn for Tongji‐Grace02k when the same analyses were applied to the regularized solutions ITSG‐Grace2014k and Tongji‐Grace02k. Validations with independent data confirm that Tongji‐Grace02s has the least noise among the unconstrained GRACE‐only models and Tongji‐Grace02k is the one with the best accuracy among the regularized GRACE‐only solutions. For the tests up to degree and order 180 using GNSS/Leveling data, the improvements of Tongji‐Grace02s with respect to ITSG‐Grace2014s reach 13% over ...

The main contribution of this study is to improve the GRACE gravity field solution by taking errors of non-conservative acceleration and attitude observations into account. Unlike previous studies, the errors of the attitude and non-conservative acceleration data, and gravity field parameters, as well as accelerometer biases are estimated by means of weighted least squares adjustment. Then we compute a new time series of monthly gravity field models complete to degree and order 60 covering the period Jan. 2003 to Dec. 2012 from the twin GRACE satellites' data. The derived GRACE solution (called Tongji-GRACE02) is compared in terms of geoid degree variances and temporal mass changes with the other GRACE solutions, namely CSR RL05, GFZ RL05a, and JPL RL05. The results show that (1) the global mass signals of Tongji-GRACE02 are generally consistent with those of CSR RL05, GFZ RL05a, and JPL RL05; (2) compared to CSR RL05, the noise of Tongji-GRACE02 is reduced by about 21% over ocean when only using 300 km Gaussian smoothing, and 60% or more over deserts (Australia, Kalahari, Karakum and Thar) without using Gaussian smoothing and decorrelation filtering; and (3) for all examples, the noise reductions are more significant than signal reductions, no matter whether smoothing and filtering are applied or not. The comparison with GLDAS data supports that the signals of Tongji-GRACE02 over St. Lawrence River basin are close to those from CSR RL05, GFZ RL05a and JPL RL05, while the GLDAS result shows the best agreement with the Tongji-GRACE02 result.