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.

Evaluation of GRACE/GRACE Follow-On Time-Variable Gravity Field Models for Earthquake Detection above Mw8.0s in Spectral Domain

This study compares the Gravity Recovery And Climate Experiment (GRACE)/GRACE Follow-On (GFO) errors with the coseismic gravity variations generated by earthquakes above Mw8.0s that occurred during April 2002~June 2017 and evaluates the influence of monthly model errors on the coseismic signal detection. The results show that the precision of GFO monthly models is approximately 38% higher than that of the GRACE monthly model and all the detected earthquakes have signal-to-noise ratio (SNR) larger than 1.8. The study concludes that the precision of the time-variable gravity fields should be improved by at least one order in order to detect all the coseismic gravity signals of earthquakes with M ≥ 8.0. By comparing the spectral intensity distribution of the GFO stack errors and the 2019 Mw8.0 Peru earthquake, it is found that the precision of the current GFO monthly model meets the requirement to detect the coseismic signal of the earthquake. However, due to the limited time length of the observations and the interference of the hydrological signal, the coseismic signals are, in practice, difficult to extract currently.

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.

Preliminary monthly gravity field models from kinematic orbits of SWARM Satellites

<p>      Although the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE FO) satellite missions play an important role in monitoring global mass changes within the Earth system, there is a data gap of about one year spanning July 2017 to May 2018, which leads to discontinuous gravity observations for monitoring global mass changes. As an alternative mission, the SWARM satellites can provide gravity observations to close this data gap. In this paper, we are dedicated to developing alternative monthly time-variable gravity field solutions from SWARM data. Using kinematic orbits of SWARM from ITSG for the period January 2015 to September 2020, we have generated a preliminary time series of monthly gravity field models named Tongji-Swarm2019 up to degree and order 60. The comparisons between Tongji-Swarm2019 and GRACE/GRACE-FO monthly solutions show that Tongji-Swarm2019 solutions agree with GRACE/GRACE-FO models in terms of large-scale mass change signals over amazon, Greenland and other regions. We can conclude that Tongji-Swarm2019 monthly gravity field models are able to close the gap between GRACE and GRACE FO.</p>

GRACE-FO accelerometer data: An alternative approach using Least Squares Spectral Analysis

<p>Technological advances in satellite geodesy have been demanding more and more accurate gravity field models but also precise measurements of the movement of water along the Earth system. GRACE-FO (GFO) mission is dedicated to monitor the Earth with a purpose of estimating the gravity field and the hydrological cycles. For the extraction of monthly gravity field models the non-gravitational accelerations are essential. The performance of GFO accelerometers (ACC) is not the optimal.  The ACC measurements present immense spikes, spurious signals and bias jumps on all three axes affecting the validity of the measurements. The bias jumps are similar to those presented at GRACE measurements and they have been related to the satellites’ entrance to and exit from the Earth’s shadow. The dominant spikes, mainly appearing in the equatorial region, have been connected to the thermal sensitivity of the instrument or the orientation of the magnetic field lines. We propose an alternative dataset generated from Level 1A of GFO C with corresponding Gaussian weights and an optimal correction of the bias jumps, along with the estimation of linear and quadratic trends using the Least Squares methodology in the frequency domain and in all three axes. The method does not remove spikes, nor does it interpolate missing values. The new 1B dataset with estimated variances shows no spike effects in the frequency domain contrastingly to the existing ACT Level 1B data. Also, a preliminary analysis of the daily amplitudes of the orbital period and semi-period components of the ACT Level 1B data set spanning one year, reveals a strong periodic signal of ~ 153 days. This signal vanishes when the proposed weighted data set is used. This signal could be related to calibration deficiencies or a systematic error in the ACC data that requires further study. The same weighted filtering approach is proposed for the ACC measurements of Swarm C satellite, a LEO constellation that measures the magnetic field of the Earth. The ACC measurements of Swarm display low signal to noise ratio due to an increased thermal sensitivity of the instrument. A weighted Gaussian filter applied on the Swarm ACC measurements reduces the contribution of the dominant spikes in the frequency domain and displays the non-gravitational signals more clearly leading to a more extended use of Swarm non-gravitational accelerations measurements.</p>

Seven years of monthly low-degree gravity field models from Swarm GPS data

<p>The Swarm satellite constellation provides GPS data with sufficient accuracy to observe the large-scale mass transport processes occurring at the Earth’s surface since 2013. We illustrate the signal content of the time series of monthly gravity field models. The models are published on quarterly basis and are the result of a combination of the individual models produced by different gravity field estimation approaches, by the Astronomical Institute of the University of Bern, the Astronomical Institute of the Czech Academy of Sciences, the Institute of Geodesy of the Graz University of Technology and the School of Earth Sciences of the Ohio State University. We combine the models at the solution level, using weights derived from a Variance Component Estimation, under the framework of the International Combination Service for Time-variable Gravity Fields (COST-G).</p><p> </p><p>We estimate the monthly quality of the models by comparing with GRACE and GRACE-FO products and illustrate the improvement of the combined model as compared to the individual models. We present the high signal-to-noise ratio of this uninterrupted time series of models, smoothed to 750km radius, over large hydrological basins. Finally, we compare the behavior of degree 2 and 3 coefficients with GRACE/GRACE-FO and SLR.</p>

Validations of Three Global Gravity Field Models Using the QDaedalus System Observed Astrogeodetic Vertical Deflections in the Munich Region, Germany

<p>The total station-based QDaedalus system, developed in 2014 by ETH Zurich in Switzerland, incorporates a charge-coupled device (CCD) camera in support of daytime geodetic and nighttime astrogeodetic observations. The successful realization of astrogeodetic observations has resulted in astrogeodetic vertical deflection (VD) data collection in Germany, Italy, Hungary, Australia, and Turkey. Astrogeodetic observations carried out in Munich, Germany were used to determine the precision and accuracy of the newly installed QDaedalus system, which was found to be ~0.2 arcseconds for both the North-South (N-S) and East-West (E-W) VD components. In this study, 10 benchmark observations in the Munich region were also used to assess the quality of three global gravity field models—Global Gravitation Model Plus (GGMplus), Earth Residual Terrain Modelled 2160 (ERTM2160) and Earth Gravitational Model 2008 (EGM2008)—through comparison with the QDaedalus observations. The results of these comparisons between the predicted and observed VD data are: (i) The GGMplus predicted VD values were found to be closer to the observed VDs, with the differences for both the N-S and E-W VD components being ~0.2″, and reaching a maximum of 0.3″ and 0.4″ for the N-S and E-W components, respectively; (ii) The ERTM2160 predicted values were also found to be closer to the observed VDs, with differences of 0.4″ or less for the N-S component, with the exception of one benchmark (BM 8), and 0.2″ or less for the E-W component, with the exception of one benchmark (BM 9); and, (iii) When the predicted VDs computed using EGM2008 were analysed, we found that they were less accurate than the predicted GGMplus and ERTM2160 values. Therefore, the maximum differences between the observed and EGM2008 predicted VD data were for 0.9″ N-S and 1.8″ for E-W. Finally, we conclude with a comparison of the results of this Munich Region study with the results of a prior QDaedalus study, which was conducted in Istanbul (Albayrak et al. 2020), to assess the accuracy of the EGM2008 and GGMplus models.</p><p> </p><p>Albayrak, M., Hirt, C., Guillaume, S., Halicioglu, K., Özlüdemir, M.T., Shum, C.K., 2020. Quality assessment of global gravity field models in coastal zones: a case study using astrogeodetic vertical deflections in Istanbul, Turkey, Studia Geophysica et Geodaetica, <strong>64</strong>(3), 306–329. doi: 10.1007/s11200-019-0591-2</p>

Stochastic noise modelling of kinematic orbit positions in the Celestial Mechanics Approach

Gravity field models may be derived from kinematic orbit positions of Low Earth Orbiting satellites equipped with onboard GPS (Global Positioning System) receivers. An accurate description of the stochastic behaviour of the kinematic positions plays a key role to calculate high quality gravity field solutions. In the Celestial Mechanics Approach (CMA) kinematic positions are used as pseudo-observations to estimate orbit parameters and gravity field coefficients simultaneously. So far, a simplified stochastic model based on epoch-wise covariance information, which may be efficiently derived in the kinematic point positioning process, has been applied. We extend this model by using the fully populated covariance matrix, covering correlations over 50 min. As white noise is generally assumed for the original GPS carrier phase observations, this purely formal variance propagation cannot describe the full noise characteristics introduced by the original observations. Therefore, we sophisticate our model by deriving empirical covariances from the residuals of an orbit fit of the kinematic positions. We process GRACE (Gravity Recovery And Climate Experiment) GPS data of April 2007 to derive gravity field solutions up to degree and order 70. Two different orbit parametrisations, a purely dynamic orbit and a reduced-dynamic orbit with constrained piecewise constant accelerations, are adopted. The resulting gravity fields are solved on a monthly basis using daily orbital arcs. Extending the stochastic model from utilising epoch-wise covariance information to an empirical model, leads to a – expressed in terms of formal errors – more realistic gravity field solution.