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.

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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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 ...

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Summary Multichannel Singular Spectrum Analysis (MSSA) is a powerful tool to extract spatiotemporal signals and filter errors from the noisy time series of monthly gravity field models from the satellite data of Gravity Recovery and Climate Experiment (GRACE). Since the GRACE monthly gravity models are missed about 17 months, we develop an improved MSSA approach, which can directly process the incomplete time series without either data interpolation or iteration. The time series of 14-year (2002.04∼2016.08) monthly gravity field models of CSR-RL06 up to degree and order 60 are analyzed with improved MSSA compared to the MSSA with linear data interpolation and iteration MSSA. By using our improved MSSA approach, the first 11 principal components derived can capture 91.18% of the total variance, higher than 85.80% and 86.44% of the total variance, derived by linear interpolation MSSA and iteration MSSA, respectively. The ratios of the latitude weighted RMS over the land and ocean signals are used to evaluate the efficiency of eliminating noise by the MSSA approach. For improved MSSA, the mean RMS ratio of land and ocean signals of all available months is higher than linear interpolation and iteration MSSA, which indicates that improved MSSA can suppress noise more efficiently and extract more geophysical signals from real GRACE data. Furthermore, the 50 repeated experiments show that all the root mean squared errors (RMSE) and mean absolute errors (MAE) derived by our improved MSSA are smaller than other MSSA approaches. Moreover, the improved MSSA performs still better than other MSSA based approaches for the cases of large data gaps.

For lakes in desert hinterlands that are not recharged by river runoff, sediment input solely comes from wind transport. While the processes of sediment transport and deposition in these lakes differ significantly from those with river discharge, the spatial distribution of sediment grain size in these groundwater‐recharged lakes remains largely unknown. Moreover, whether the grain size distribution in these lake sediments can be used as a proxy in the study of past climatic change and environmental evolution studies is unclear. In this study, five lakes with a range of surface areas that had no runoff recharge were selected from the hinterland of the Badain Jaran Desert of north‐western China, and a total of 108 samples of lake surface sediments were collected to examine the spatial distribution of grain size. Moreover, an end‐member‐modeling algorithm was used to calculate end members from all grain size measurements. Our results showed that both the median and mean grain sizes in the lake sediments decreased from the nearshore to the offshore, deep‐water zone. However, the lowest median and mean grain sizes were not found in the center of the lakes, in contrast to lakes recharged by surface runoff. The median grain size of sediment in the lake center was negatively correlated with lake level, and thus could help reveal lake evolution at low resolutions. Moreover, EM1 and EM2 were interpreted as wind transported sediment, and sediment perturbed by lake waves after wind transport, respectively. The modal grain size of EM1 varied slightly between lakes, while changes in the modal grain size of EM2 were related to lake area. Given the positive relationship found between EM2 content and lake level, changes in the EM2 content (%) can serve as a rough indicator of lake level fluctuations at low temporal resolutions. Copyright © 2017 John Wiley & Sons, Ltd.

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