Monthly Gravity Field Models Research Articles

Changing characteristics of ecosystem and water storage under the background of warming and humidification in the Qilian Mountains, China

The Qilian Mountains provide an ecological security barrier, and the region is an important river runoff area in China. Water resources play an essential role in the natural environment of Northwest China. This study used daily temperature and precipitation data from meteorological stations in the Qilian Mountains from 2003 to 2019, Gravity Recovery and Climate Experiment, and Moderate Resolution Imaging Spectroradiometer satellite data. Additionally, we used the Gravity Recovery and Climate Experiment satellite's monthly gravity field model data. Furthermore, we analyzed the characteristics of climate warming and humidification in the eastern, central, and western sections of the Qilian Mountains based on spatial precipitation interpolation and linear trend analysis. Finally, we examined the relationship between water storage changes and precipitation and its impact on vegetation ecology. The results revealed a significant warming and humidification trend in the western Qilian Mountains. The temperature increased significantly, and the increased precipitation rate in summer reached 1.5–3.1 mm/10a. Water storage in the Qilian Mountains displayed an increasing trend, with an increase of approximately 14.3 × 108 m3 over the 17 years study period, averaging an increase of 8.4 mm/year. The spatial distribution of water storage in the Qilian Mountains increased from north to south and east to west. There were noticeable seasonal differences, with the largest surplus occurring in the western Qilian Mountains (71.2 mm in summer). The fractional vegetation coverage in 95.2 % of the western Qilian Mountains and net primary productivity in 90.4 % of the area displayed an increasing trend, and vegetation ecology improved significantly. This study aims to investigate the characteristics of ecosystem and water storage changes in the Qilian Mountain area under the background of climate warming and humidification. The results obtained from this study provided an assessment of the vulnerability of alpine ecosystems and helped in making spatially explicit decisions for the rational utilization of water resources.

Empirical mode decomposition for post-processing the GRACE monthly gravity field models

Considering the advantage of Empirical Mode Decomposition (EMD) for extracting the geophysical signals and filtering out the noise, this paper will first apply the EMD approach to post-process the Gravity Recovery and Climate Experiment (GRACE) monthly gravity field models. A 14-year time-series of Release 06 (RL06) monthly gravity field models from the Center for Space Research (CSR) truncated to degree and order 60 from the period April 2002 to August 2016 are analyzed using the EMD approach compared with traditional Gaussian smoothing filtering. Almost all fitting errors of GRACE spherical harmonic coefficients by the EMD approach are smaller than those by Gaussian smoothing, indicating that EMD can retain more information of the original spherical harmonic coefficients. The ratios of latitude-weighted RMS over the land and ocean signals are adopted to evaluate the efficiency of eliminating noise. The results show that almost all ratios of RMS for the EMD approach are higher than those of Gaussian smoothing, with the mean ratio of RMS of 3.61 for EMD and 3.41 for Gaussian smoothing, respectively. Therefore, we can conclude that the EMD method can filter noise more effectively than Gaussian smoothing, especially for the high-degree coefficients, and retain more geophysical signals with less leakage effects.

Monthly Gravity Field Solutions From Early LEO Satellites' Observations Contribute to Global Ocean Mass Change Estimates Over 1993∼2004

AbstractSince few time‐variable gravity field models were developed before the Gravity Recovery and Climate Experiment (GRACE) era, one normally estimates the global mean ocean mass (GMOM) changes by summing the mass contributions. In this study, new monthly gravity field models named Tongji‐LEO2021 developed from Early Low Earth Orbit (LEO) satellites' observations are used to directly estimate GMOM changes and quantify the closure of global sea‐level budget with Altimetry and Steric observations between 1993 and 2004. The resulting GMOM changes from Tongji‐LEO2021 solutions are comparable to those from the Center for Space Research RL06 GRACE solutions and the IGG‐SLR‐HYBRID solutions, with the correlation coefficients of 0.91 and 0.88. Our results show that the LEO‐derived GMOM change rate is 0.88 ± 0.16 mm/year, slightly larger than 0.83 ± 0.16 mm/year from IGG‐SLR‐HYBRID, which are both close to Altimetry‐Steric estimate (0.87 ± 0.33 mm/year). Overall, Tongji‐LEO2021 solutions can estimate the GMOM change and understand the global sea‐level change before the GRACE era.

Effects of New Level-1B Data on GRACE Temporal Gravity Field Models and Precise Orbit Determination Solutions

The quality of Gravity Recovery and Climate Experiment (GRACE) observation is the prerequisite for obtaining the high-precision GRACE temporal gravity field model. To study the influence of new-generation GRACE Level-1B Release 03 (RL03) data and the new atmosphere and ocean de-aliasing (AOD1B) products on recovering temporal gravity field models and precise orbit determination (POD) solutions, we combined the global positioning system and K-band ranging-rate (KBRR) observations of GRACE satellites to estimate the effect of different data types on these solutions. The POD and monthly gravity field solutions are obtained from 2005 to 2010 by SHORDE software developed by the Shanghai Astronomical Observatory. The post-fit residuals of the KBRR data were decreased by approximately 10%, the precision of three-direction positions of the GRACE POD was improved by approximately 5%, and the signal-to-noise ratio of the monthly gravity field model was enhanced. The improvements in the new release of monthly gravity field model and POD solutions can be attributed to the enhanced Level-1B KBRR data and the AOD1B model. These improvements were primarily due to the enhanced of KBRR data; the effect of the AOD1B model was not significant. The results also showed that KBRR data slightly improve the satellite orbit precision, and obviously enhance the precision of the gravity field model.

Time-Variable Gravity Field from the Combination of HLSST and SLR

The Earth’s time-variable gravity field is of great significance to study mass change within the Earth’s system. Since 2002, the NASA-DLR Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE follow-on mission provide observations of monthly changes in the Earth gravity field with unprecedented accuracy and resolution by employing low-low satellite-to-satellite tracking (LLSST) measurements. In addition to LLSST, monthly gravity field models can be acquired from satellite laser ranging (SLR) and high-low satellite-to-satellite tracking (HLSST). The monthly gravity field solutions HLSST+SLR were derived by combining HLSST observations of low earth orbiting (LEO) satellites with SLR observations of geodetic satellites. Bandpass filtering was applied to the harmonic coefficients of HLSST+SLR solutions to reduce noise. In this study, we analyzed the performance of the monthly HLSST+SLR solutions in the spectral and spatial domains. The results show that: (1) the accuracies of HLSST+SLR solutions are comparable to those from GRACE for coefficients below degree 10, and significantly improved compared to those of SLR-only and HLSST-only solutions; (2) the effective spatial resolution could reach 1000 km, corresponding to the spherical harmonic coefficient degree 20, which is higher than that of the HLSST-only solutions. Compared with the GRACE solutions, the global mass redistribution features and magnitudes can be well identified from HLSST+SLR solutions at the spatial resolution of 1000 km, although with much noise. In the applications of regional mass recovery, the seasonal variations over the Amazon Basin and the long-term trend over Greenland derived from HLSST+SLR solutions truncated to degree 20 agree well with those from GRACE solutions without truncation, and the RMS of mass variations is 282 Gt over the Amazon Basin and 192 Gt in Greenland. We conclude that HLSST+SLR can be an alternative option to estimate temporal changes in the Earth gravity field, although with far less spatial resolution and lower accuracy than that offered by GRACE. This approach can monitor the large-scale mass transport during the data gaps between the GRACE and the GRACE follow-on missions.

A comparison of GRACE temporal gravity field models recovered with different processing details

SUMMARYThe Gravity Recovery and Climate Experiment (GRACE) mission has been providing abundant information regarding the mass changes of the Earth in terms of time-series of temporal gravity field models since 2002. To derive temporal gravity field models with high accuracy, many methods have been developed. In this paper, we focus on the variational equation integration approach. The main works can be summarized as follows: (1) analysing the quality of GRACE Level1B RL02 and RL03 data, including accelerometer observations (ACC1B), star camera measurements (SCA1B) and K-Band low-low Satellite-to-Satellite Tracking (SST) range-rate (KBRR) data (KBR1B); (2) discussing the influence of arc-specific parameters and arc length on gravity field recovery and (3) comparing two different methods used for sensitivity matrix generation, namely, a numerical integration method and the method of variation of constants, from the perspectives of accuracy and efficiency, respectively. Based on these analyses, discussions and comparisons, a new time-series of GRACE monthly gravity field models in terms of spherical harmonic coefficients completed to degree and order 60, called SWJTU-GRACE-RL02p, was derived by using the modified variational equation integration approach bashed on GRACE Level1B RL03 data, covering the period from April 2002 to October 2011 with some gaps in between due to poor quality or missing GRACE data. Thus we are looking at the results some 10yrs in the past. The differences between the traditional variational equation integration approach and the approach that we used are mainly as follows: (1) according to the GRACE data quality, the arc length is no longer a constant in the determination of temporal gravity field models; (2) the kinematic empirical parameters, which are mainly designed to remove the bias and drifts in KBRR residuals, are abandoned and (3) the method of variation of constants developed at the Astronomical Institute of the University of Bern (AIUB) and used to solve the system of variational equations associated with constrained pulses and piecewise constant accelerations is used to calculate the sensitivity matrices of accelerometer bias parameters to improve the calculation efficiency and ensure the calculation accuracy. To validate the quality of SWJTU-GRACE-RL02p, these models were compared with the old models of SWJTU-GRACE-RL01, which have been published by the website of the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/series), and the official products [i.e. the RL05 and RL06 versions of GRACE LEVEL2 at the Centre for Space Research (CSR), Jet Propulsion Laboratory (JPL) and GeoForschungsZentrum (GFZ)]. Compared to the RL06 version of official models, the models of SWJTU-GRACE-RL02p present competitive performance for global mass changes. Furthermore, these models show less noise and a higher signal strength over some local areas with large mass changes than the models of SWJTU-GRACE-RL01. The comparisons between SWJTU-GRACE-RL02p and a variety of other models including official models, GLDAS, models provided by EGSIEM and daily solutions released by ITSG indicate that our approach and the data processing details presented in this paper provide an alternative strategy for the recovery of temporal gravity field models from GRACE-type data.

Weighted multichannel singular spectrum analysis for post-processing GRACE monthly gravity field models by considering the formal errors

SUMMARY Gravity Recovery and Climate Experiment (GRACE) Spherical Harmonics (SH) solutions are usually provided together with the corresponding formal errors, however, all multichannel singular spectrum analysis (MSSA) approaches neglect the formal errors of SH coefficients in processing the GRACE time-series. In this contribution, we propose a weighted MSSA to directly process the incomplete time-series of GRACE monthly gravity models by taking the formal errors into account. A 14-yr time-series of Release 06 (RL06) monthly gravity field models from the Center for Space Research (CSR) truncated to degree and order 60 for the period April 2002 to August 2016 is analysed by weighted MSSA, the results are compared with those from the improved MSSA that directly processes the incomplete time-series without considering the formal errors. The first 10 principal components derived by using the weighted MSSA can capture the variance in total up to 82.05 per cent, higher than 80.19 per cent by the improved MSSA. All the fitting errors of GRACE SH coefficients by the weighted MSSA are smaller than those by the improved MSSA, which indicates that weighted MSSA can extract more geophysical signals than the improved MSSA approach. To evaluate the efficiency of the weighted MSSA to eliminate noise, the ratios of the latitude weighted RMS of signals over lands and oceans are computed. Based on all the available months of GRACE solutions, the mean RMS ratio of signals over lands relative to oceans derived by the weighted MSSA is 5.04, higher than 4.96 by the improved MSSA. Therefore, we can conclude that the proposed weighted MSSA can suppress noise more efficiently and extract more geophysical signals from the GRACE time-series, which highlights the necessity of considering formal errors and the effectiveness of the weighted MSSA in post-processing the GRACE monthly gravity field models.

Joint analysis of selected GRACE monthly spherical harmonic solutions and monthly MASCON solutions

Study presented in this paper is focused on comparison and statistical assessment of differences between the selected Level 2 products of the satellite mission Gravity Recovery and Climate Experiment (GRACE). Global monthly gravity field models in terms of spherical harmonic coefficients produced by three institutes of GRACE Science Data System are compared with the partially independent MASCON global gravity field model. Detailed comparison and statistical analysis of differences is performed in 5 selected river basins: Amazon, Congo, Danube, Yenisei and Lena. For each spherical harmonic solution, 8 different filtrations available at International Center for Global Gravity Field Models (ICGEM) are tested over the time span from April 2002 to July 2016. Fischer test at two significance levels 10% and 5% has been performed in order to qualify the statistical significance between the particular solutions.

The impact of error covariance matrix structure of GRACE’s gravity solution on the mass flux estimates of Greenland ice sheet

The error variance-covariance matrices of the monthly GRACE gravity field models are usually well-structured (e.g., order-leading) to contain the error information of the monthly gravity field models, and they are important information to further improve the accuracy of the estimated mass transportation by a post-processing scheme. Intensive studies have been performed to understand the impact of different approximations of the full error variance-covariance matrix on the mass estimates obtained with the conventional GRACE-post processing methods (e.g., the de-striping method). In this study, based on the variants of the mascon approach which treat monthly GRACE solutions as input, we consider four different structures to the error variance-covariance matrix, (i) full matrix, (ii) block diagonal matrix, (iii) diagonal matrix, and (iv) identity matrix, and examine their impact on the mass transport estimates in Greenland. Using both synthetic and real data, we analyze the results at four temporal scales: (i) the long-term decadal scale, (ii) the inter-annual scale, (iii) the seasonal scale, and (iv) the monthly scale. Based on the synthetic study, we find that for the recovery of the long-term trend, the application of the diagonal structure obtains the best estimates. This is caused by the amplification of the model error in the mascon approach when considering the full and block diagonal structures of error variance-covariance matrix, not due to any imperfection of them. Therefore, we emphasize that one should be aware of mascon model deficiency in the mascon approach. Furthermore, the best inter-annual, seasonal and monthly mass estimates are derived by considering the block diagonal and full structures. This is caused by the behavior of the bias and the unique parameterization error in the case of different structures. A similar finding is also presented in the real data case. Finally, our analysis denotes the necessity of releasing the block diagonal structure together with the official monthly gravity field model for the GRACE Follow-On mission, instead of releasing only the diagonal structure as done for the GRACE mission.

Improved multichannel singular spectrum analysis for post-processing GRACE monthly gravity field models

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-yr (2002.04–2016.08) monthly gravity field models of CSR-RL06 up to degree and order 60 are analysed 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 per cent of the total variance, higher than 85.80 and 86.44 per cent 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 and mean absolute errors 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.

Description of the multi-approach gravity field models from Swarm GPS data

Abstract. Although the knowledge of the gravity of the Earth has improved considerably with CHAMP, GRACE, and GOCE (see appendices for a list of abbreviations) satellite missions, the geophysical community has identified the need for the continued monitoring of the time-variable component with the purpose of estimating the hydrological and glaciological yearly cycles and long-term trends. Currently, the GRACE-FO satellites are the sole dedicated provider of these data, while previously the GRACE mission fulfilled that role for 15 years. There is a data gap spanning from July 2017 to May 2018 between the end of the GRACE mission and start the of GRACE-FO, while the Swarm satellites have collected gravimetric data with their GPS receivers since December 2013. We present high-quality gravity field models (GFMs) from Swarm data that constitute an alternative and independent source of gravimetric data, which could help alleviate the consequences of the 10-month gap between GRACE and GRACE-FO, as well as the short gaps in the existing GRACE and GRACE-FO monthly time series. The geodetic community has realized that the combination of different gravity field solutions is superior to any individual model and set up the Combination Service of Time-variable Gravity Fields (COST-G) under the umbrella of the International Gravity Field Service (IGFS), part of the International Association of Geodesy (IAG). We exploit this fact and deliver the highest-quality monthly GFMs, resulting from the combination of four different gravity field estimation approaches. All solutions are unconstrained and estimated independently from month to month. We tested the added value of including kinematic baselines (KBs) in our estimation of GFMs and conclude that there is no significant improvement. The non-gravitational accelerations measured by the accelerometer on board Swarm C were also included in our processing to determine if this would improve the quality of the GFMs, but we observed that is only the case when the amplitude of the non-gravitational accelerations is higher than during the current quiet period in solar activity. Using GRACE data for comparison, we demonstrate that the geophysical signal in the Swarm GFMs is largely restricted to spherical harmonic degrees below 12. A 750 km smoothing radius is suitable to retrieve the temporal variations in Earth's gravity field over land areas since mid-2015 with roughly 4 cm equivalent water height (EWH) agreement with respect to GRACE. Over ocean areas, we illustrate that a more intense smoothing with 3000 km radius is necessary to resolve large-scale gravity variations, which agree with GRACE roughly at the level of 1 cm EWH, while at these spatial scales the GRACE observes variations with amplitudes between 0.3 and 1 cm EWH. The agreement with GRACE and GRACE-FO over nine selected large basins under analysis is 0.91 cm, 0.76 cm yr−1, and 0.79 in terms of temporal mean, trend, and correlation coefficient, respectively. The Swarm monthly models are distributed on a quarterly basis at ESA's Earth Swarm Data Access (at https://swarm-diss.eo.esa.int/, last access: 5 June 2020, follow Level2longterm and then EGF) and at the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/series/02_COST-G/Swarm, last access: 5 June 2020), as well as identified with the DOI https://doi.org/10.5880/ICGEM.2019.006 (Encarnacao et al., 2019).

Satellite Monitoring of Mass Changes and Ground Subsidence in Sudan’s Oil Fields Using GRACE and Sentinel-1 Data

Monitoring environmental hazards, owing to natural and anthropogenic causes, is an important issue, which requires proper data, models, and cross-validation of the results. The geodetic satellite missions, for example, the Gravity Recovery and Climate Experiment (GRACE) and Sentinel-1, are very useful in this respect. GRACE missions are dedicated to modeling the temporal variations of the Earth’s gravity field and mass transportation in the Earth’s surface, whereas Sentinel-1 collects synthetic aperture radar (SAR) data, which enables us to measure the ground movements accurately. Extraction of large volumes of water and oil decreases the reservoir pressure and form compaction and, consequently, land subsidence occurs, which can be analyzed by both GRACE and Sentinel-1 data. In this paper, large-scale groundwater storage (GWS) changes are studied using the GRACE monthly gravity field models together with different hydrological models over the major oil reservoirs in Sudan, that is, Heglig, Bamboo, Neem, Diffra, and Unity-area oil fields. Then, we correlate the results with the available oil wells production data for the period of 2003–2012. In addition, using the only freely available Sentinel-1 data, collected between November 2015 and April 2019, the ground surface deformation associated with this oil and water depletion is studied. Owing to the lack of terrestrial geodetic monitoring data in Sudan, the use of GRACE and Sentinel-1 satellite data is very valuable to monitor water and oil storage changes and their associated land subsidence over our region of interest. Our results show that there is a significant correlation between the GRACE-based GWS anomalies (ΔGWS) and extracted oil and water volumes. The trend of ΔGWS changes due to water and oil depletion ranged from –18.5 ± 6.3 to –6.2 ± 1.3 mm/year using the CSR GRACE monthly solutions and the best tested hydrological model in this study. Moreover, our Sentinel-1 SAR data analysis using the persistent scatterer interferometry (PSI) method shows a high rate of subsidence, that is, –24.5 ± 0.85, –23.8 ± 0.96, –14.2 ± 0.85, and –6 ± 0.88 mm/year over Heglig, Neem, Diffra, and Unity-area oil fields, respectively. The results of this study can help us to control the integrity and safety of operations and infrastructure in that region, as well as to study the groundwater/oil storage behavior.

What can be expected from GNSS tracking of satellite constellations for temporal gravity field model determination?

SUMMARYThe goal of this contribution is to investigate the expected improvement of temporal gravity field determination via a couple of high-low satellite-to-satellite tracking (HLSST) missions. The simulation system is firstly validated by determining monthly gravity field models within situ GRACE GPS tracking data. The general consistency between the retrieved solutions and those developed by other official agencies indicates the good performance of our software. A 5-yr full-scale simulation is then performed using the full error sources including all error components. Analysis of each error component indicates that orbit error is the main contributor to the overall HLSST-derived gravity field model error. The noise level of monthly solution is therefore expected to reduce 90 per cent in terms of RMSE over ocean when the orbit accuracy improves for a magnitude of one order. As for the current HLSST mission consisting of a current GNSS receiver and an accelerometer (10−10 and 10−9 m s–2 noise for sensitive and non-sensitive axes), it is expected to observe monthly (or weekly) gravity solution at the spatial resolution of about 1300 km (or 2000 km). As for satellite constellations, a significant improvement is expected by adding the second satellite with the inclination of 70° and the third satellite with the inclination of 50°. The noise reduction in terms of cumulative geoid height error is approximately 51 per cent (or 62 per cent) when the observations of two (or three) HLSST missions are used. Moreover, the accuracy of weekly solution is expected to improve 40–70 per cent (or 27–59 per cent) for three (or two) HLSST missions when compared to one HLSST mission. Due to the low financial costs, it is worthy to build a satellite constellation of HLSST missions to fill the possible gaps between the dedicated temporal gravity field detecting missions.

Applicability of GRACE and GRACE-FO for monitoring water mass changes of the Aral Sea and the Caspian Sea

The GRACE gravity satellite mission has provided monthly gravity field solutions for about 15 years enabling a unique opportunity to monitor large scale mass variation processes. By the end of the GRACE, the GRACE-FO mission was launched in order to continue the time series of monthly gravity fields. The two missions are similar in most aspects apart from the improved intersatellite range rate measurements, which is performed with lasers in addition to microwaves. An obvious demand for the geoscientific applications of the monthly gravity field models is to understand the consistency of the models provided by the two missions. This study provides a case-study related consistency investigation of GRACE and GRACE-FO monthly solutions for the Aral Sea region. As the closeness of the Caspian Sea may influence the monthly mass variations of the Aral Sea, it has also been involved in the investigations. According to the results, GRACE-FO models seem to continue the mass variations of the GRACE period properly, therefore their use jointly with GRACE is suggested. Based on the justified characteristics of the gravity anomaly by water volume variations in the case of the Aral Sea, GRACE models for the period March–June 2017 are suggested to be neglected. Though the correlation between water volume and monthly gravity field variations is convincing in the case of the Aral Sea, no such a correlation for the Caspian Sea could have been detected, which suggests to be the consequence of other mass varying processes, may be related to the seismicity of the Caspian Sea area.

Prograde and Retrograde Terms of Gravimetric Polar Motion Excitation Estimates from the GRACE Monthly Gravity Field Models

From 2002 to 2017, the Gravity Recovery and Climate Experiment (GRACE) mission’s twin satellites measured variations in the mass redistribution of Earth’s superficial fluids, which disturb polar motion (PM). In this study, the PM excitation estimates were computed from two recent releases of GRACE monthly gravity field models, RL05 and RL06, and converted into prograde and retrograde circular terms by applying the complex Fourier transform. This is the first such analysis of circular parts in GRACE-based excitations. The obtained series were validated by comparison with the residuals of observed polar motion excitation (geodetic angular momentum (GAM)–atmospheric angular momentum (AAM)–oceanic angular momentum (OAM) (GAO)) determined from precise geodetic measurements of the pole coordinates. We examined temporal variations of hydrological excitation function series (or hydrological angular momentum, HAM) in four spectral bands: seasonal, non-seasonal, non-seasonal short-term, and non-seasonal long-term. The general conclusions arising from the conducted analyses of prograde and retrograde terms were consistent with the findings from the equatorial components of PM excitation studies drawn in previous research. In particular, we showed that the new GRACE RL06 data increased the consistency between different solutions and improved the agreement between GRACE-based excitation series and reference data. The level of agreement between HAM and GAO was dependent on the oscillation considered and was higher for long-term than short-term variations. For most of the oscillations considered, the highest agreement with GAO was obtained for CSR RL06 and ITSG-Grace2018 solutions. This study revealed that both prograde and retrograde circular terms of PM excitation can be determined by GRACE with similar levels of accuracy. The findings from this study may help in choosing the most appropriate GRACE solution for PM investigations and can be useful in future improvements to GRACE data processing.

Mass Flux Solution in the Tibetan Plateau Using Mascon Modeling

Mascon modeling is used in this paper to produce the mass flux solutions in the Tibetan Plateau. In the mascon modeling, the pseudo observations and their covariance matrices are derived from the GRACE monthly gravity field models. The sampling density of the pseudo observations is determined based on the eigenvalues of the covariance matrices. In the Tibetan Plateau, the sampling density of per 1.5° is the most appropriate among all choices. The mass flux variations from 2003 to 2014 are presented in this paper, which show large mass loss (about −15.5 Gt/year) in Tianshan, North India, and Eastern Himalaya, as well as strong positive signals (about 9 Gt/year) in the Inner Tibetan Plateau. After the glacier isostatic adjustment effects from Pau-5-AUT model are removed, the mass change rates in the Tibetan Plateau derived from CSR RL05, JPL RL05, GFZ RL05a, and Tongji-GRACE02 monthly models are −6.41 ± 4.74 Gt/year, −5.87 ± 4.88 Gt/year, −6.08 ± 4.65 Gt/year, and −11.50 ± 4.79 Gt/year, respectively, which indicate slight mass loss in this area. Our results confirm that mascon modeling is efficient in the recovery of time-variable gravity signals in the Tibetan Plateau.

Annual variation detected by GPS, GRACE and loading models

Most GPS coordinate time series, surface displacements derived from the Gravity Recovery and Climate Experiment (GRACE), and loading models display significant annual signals at many regions. This paper compares the annual signals of the GPS position time series from the Crustal Dynamics Data Information System (CDDIS), estimates of loading from GRACE monthly gravity field models calculated by three processing centers (Center of Spatial Research, CSR; Jet Propulsion Laboratory, JPL; GeoForschungsZentrum, GFZ) and three geophysical fluids models (National Center for Environmental Prediction, NCEP; Estimating the Circulation and Climate of the Ocean, ECCO; Global Land Data Assimilation System, GLDAS) for 270 globally distributed stations for the period 2003-2011. The results show that annual variations derived from the level-2 products from the three GRACE product centers are very similar. The absolute difference in annual amplitude between any two centers is never larger than 1.25 mm in the vertical and 0.11 mm in horizontal displacement. The mean phase differences of the GRACE results are less than ten days for all three components. When we correct the GPS vertical coordinate time series using the GRACE annual amplitudes using the products from three GRACE analysis centers, we find that we are able to reduce the GPS annual signal in the vertical at about 80% stations and the average reduction is about 47%. In the north and the east, the annual amplitude is reduced on 77% and 72% of the stations with the average reduction 32% and 33%. We also compare the annual surface displacement signal derived from two environmental models; the two models use the same atmospheric and non-tidal ocean loading and differ only in the continental water storage model that we use, either NCEP or GLDAS. We find that the model containing the GLDAS continental water storage is able to better reduce the annual signal in the GPS coordinate time series.

An improved GRACE monthly gravity field solution by modeling the non-conservative acceleration and attitude observation errors

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.

What Can be Expected from the GRACE-FO Laser Ranging Interferometer for Earth Science Applications?

The primary objective of the gravity recovery and climate experiment followon (GRACE-FO) satellite mission, due for launch in August 2017, is to continue the GRACE time series of global monthly gravity field models. For this, evolved versions of the GRACE microwave instrument, GPS receiver, and accelerometer will be used. A secondary objective is to demonstrate the effectiveness of a laser ranging interferometer (LRI) in improving the satellite-to-satellite tracking measurement performance. In order to investigate the expected enhancement for Earth science applications, we have performed a full-scale simulation over the nominal mission lifetime of 5 years using a realistic orbit scenario and error assumptions both for instrument and background model errors. Unfiltered differences between the synthetic input and the finally recovered time-variable monthly gravity models show notable improvements with the LRI, on a global scale, of the order of 23 %. The gain is realized for wavelengths smaller than 240 km in case of Gaussian filtering but decreases to just a few percent when anisotropic filtering is applied. This is also confirmed for some typical regional Earth science applications which show randomly distributed patterns of small improvements but also degradations when using DDK4-filtered LRI-based models. Analysis of applied error models indicates that accelerometer noise followed by ocean tide and non-tidal mass variation errors are the main contributors to the overall GRACE-FO gravity model error. Improvements in these fields are therefore necessary, besides optimized constellations, to make use of the increased LRI accuracy and to significantly improve gravity field models from next-generation gravity missions.

Monthly gravity field solution from GRACE range measurements using modified short arc approach

In this paper we present a series of monthly gravity field solutions from Gravity Recovery and Climate Experiment (GRACE) range measurements using modified short arc approach, in which the ambiguity of range measurements is eliminated via differentiating two adjacent range measurements. The data used for developing our monthly gravity field model are same as Tongji-GRACE01 model except that the range measurements are used to replace the range rate measurements, and our model is truncated to degree and order 60, spanning Jan. 2004 to Dec. 2010 also same as Tongji-GRACE01 model. Based on the comparison results of the C2,0, C2,1, S2,1, and C15,15, S15,15, time series and the global mass change signals as well as the mass change time series in Amazon area of our model with those of Tongji-GRACE01 model, we can conclude that our monthly gravity field model is comparable with Tongji-GRACE01 monthly model.