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By the middle of this decade, measurements from the CHAMP (CHAllenging of Minisatellite Payload) and GRACE (Gravity Recovery And Climate Experiment) gravity mapping satellite missions are expected to provide a significant improvement in our knowledge of the Earth's mean gravity field and its temporal variation. For this research, new observation eq...
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... Previous studies have demonstrated that both orbit and range rate data are dominated by correlated noise [25,42]. In order to achieve an optimal weighting for orbit data and KBR range rate measurements in the proposed acceleration method, the variance-covariance matrix Q s k is designed to account for the spectral characteristics present in both measurements according to auto-regressive filtering [29,35,42,43]. Since the unknown parameter vector y k in the sub-normal Equation (6) contains both gravity field coefficients and local parameters, the local parameters need to be eliminated in accordance with Beutler et al. (2010b) [44]. ...
During gravity field modeling, the conventional acceleration approach rarely incorporates KBR inter-satellite range rate data from the GRACE mission. To propose an improved acceleration method, this study introduces initial orbital position and velocity vectors to be estimated along with a combination of Cowell, KSG, and Adams integrators. In addition to achieving a full-rank design matrix regarding orbit corrections when constructing observation equations, the proposed method is capable of utilizing range rate observations for gravity field estimation. To verify the reliability of this approach, GRACE data from April 2002 to December 2016 was used to calculate a time series of monthly gravity solutions up to a degree and order of 96, referred to as Tongji-Acc RL06 in this paper. The computed time series are compared with the official models (i.e., CSR RL06, GFZ RL06, and JPL RL06) in terms of geoid degree variances, signal contents over distinct areas, and noise levels in desert regions. The investigations lead to the following conclusions: (a) the geoid degree variances indicate that Tongji-Acc RL06 exhibits comparable signal levels (approximately below 20 degrees) to the other three models while demonstrating lower noise at higher degrees (above 40 degrees); (b) the analysis over the globe, typical river basins, and land–ice regions illustrates that the solutions derived using the proposed acceleration method agree well with the official models based on the dynamic approach; (c) especially over the two large-scale river basins (i.e., Amazon and Zambezi) and another two small-scale river basins (i.e., Tennessee and Irrawaddy), Tongji-Acc RL06 significantly improves the SNR values; and (d) in the cases of the Sahara and Karakum deserts, Tongji-Acc RL06 achieves noise reductions of over 55.8% and 61.5% relative to CSR RL06, respectively. In general, the signal and noise analyses demonstrate that the proposed acceleration-based approach can effectively extract gravity field signals from KBR inter-satellite range rate observations with improved SNR, while significantly reducing the high-frequency noise.
... Also based on Kaula's representation of geo-potential, an analytical method for the Earth's gravity-field recovery was provided by [15] (see also [16][17][18][19][20][21][22][23][24][25]). The method shares the same observable with the inter-satellite line-of-sight differential gravitational acceleration approach [26][27][28][29][30], and contains an analytical expression of the differential gravitational acceleration of two GRACE satellites which fly along a circular reference orbit encircling the Earth in uniform rotation. ...
... with E mk , Υ mk (t), and ω mk given by Eqs. (27)(28)(29)(30)(31)(32)(33)(34)(35)(36). The formula consists of a series of cosine functions of time t. ...
TianQin is a proposed space-based gravitational wave detector designed to operate in circular high Earth orbits. As a sequel to [Zhang et al. Phys. Rev. D 103, 062001 (2021)], this work provides an analytical model to account for the perturbing effect of the Earth's gravity field on the range acceleration noise between two TianQin satellites. For such an "orbital noise," the Earth's contribution dominates above 5 × 10^−5 Hz in the frequency spectrum, and the noise calibration and mitigation, if needed, can benefit from in-depth noise modeling. Our model derivation is based on Kaula's theory of satellite gravimetry with Fourier-style decomposition, and uses circular reference orbits as an approximation. To validate the model, we compare the analytical and numerical results in two main scenarios. First, in the case of the Earth's static gravity field, both noise spectra are shown to agree well with each other at various orbital inclinations and radii, confirming our previous numerical work while providing more insight. Second, the model is extended to incorporate the Earth's time-variable gravity. Particularly relevant to TianQin, we augment the formulas to capture the disturbance from the Earth's free oscillations triggered by earthquakes, of which the mode frequencies enter TianQin's measurement band above 0.1 mHz. The analytical model may find applications in gravity environment monitoring and noise-reduction pipelines for TianQin.
... These methods are further distinguished based on the type of observation. One method based on precise orbit and KBRR observations uses the dynamic approach [6][7][8][9][10], short-arc approach [11][12][13], and energy balance approach [14][15][16] to develop the gravity field model. Another method joins GPS and KBRR observations using the dynamic approach [17][18][19] or celestial mechanics approach [20,21] to obtain the gravity field model and POD 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.
... In that context, the gravitational potential is treated as an in-situ observation of the Earth's gravitational field. Denoting the satellite position and velocity in the inertial frame as x i andẋ i , respectively, the gravitational potential at time t can be calculated by (Jekeli, 1999;Han, 2003): ...
... for each time epoch t, and using the observations derived from Eq. (2.21), the unknown spherical harmonic coefficientsC n,m andS n,m can be estimated using least-squares adjustment. Explicit mathematical equations for this task can be found in Han (2003). In the sequel, the bar accent is omitted from {C n,m ,S n,m } for simplicity. ...
The need for a reliable land hydrology model that can monitor the amount of water stored on and beneath the Earth’s surface on a regional and global scale has become very important, especially in overpopulated areas or regions that already suffer from shortage of freshwater. The main objective of this thesis is to examine the hydrology signal in North America using a combination of land hydrology models and satellite gravimetry products coming from the GRACE satellite mission. Our analysis emphasizes on the post-processing of GRACE data. More specifically, we define a detailed framework for the extraction of hydrological signals from GRACE data by removing the contribution of non-hydrologic geophysical components and using advanced processing techniques. In order to carry out this objective, we improve the most frequently-used filtering methods for the suppression of correlated errors from GRACE data, and develop more refined algorithms for their implementation. We formulate a selective decorrelation of GRACE data using machine learning and show that our new approach mitigates the over-filtering effects of the conventional decorrelation. We also solve the instability and inaccuracy problems related to the calculation of isotropic Gaussian filter coefficients and develop new expressions that simplify their evaluation. We assess the GRACE data and the hydrology models, and find a satisfactory level of agreement between them, with an averaged RMS difference of 3.9 cm in terms of equivalent water height. We then combine these independent datasets and develop two combined hydrology models for the monitoring of monthly terrestrial water storage and groundwater storage variations. We examine their seasonal and long-term variations and provide useful insights for the spatiotemporal evolution of water masses in North America from 2003 to 2014. For the most part, North America is affected by negative long-term trends of terrestrial and ground water changes that are more evident in Hudson Bay and southern North America, whereas strong accumulation of water masses is observed in central North America. The combined models developed in this study provide a basis for the continuous satellite-based monitoring of land hydrology in North America and can be used for the improved management of water resources.
... We mention that some previous studies have already derived the identical (e.g. Gerlach et al. 2003;Han 2003;Wang et al. 2012) or similar (e.g. Badura et al. 2006;Jäggi et al. 2008) formulation. ...
... Alternate solution techniques Han et al., 2006b] have demonstrated their initial promise to enhance temporal resolution as fine as 5 days Schmidt et al., 2006], spatial resolution up to 220 km or longer (halfwavelength, or o 4 x o 4 equal area blocks) for the mascon solutions Lemoine et al., 2005;Yuan & Watkins, 2006], as well as for the energy method [Jekeli, 1999;Han, 2003b;Han et al., 2006b] by employing stochastic regional inversion using 2-D FFT [Han et al., 2003a]. These techniques have demonstrated their capability to enhance temporal and spatial resolutions of geophysical signals as compared to spherical harmonic solutions which, at present, exhibit monthly resolutions and longer than 800 km (half-wavelength) resolutions. ...
... , and nonconservative acceleration i f , can be derived directly [Han, 2003b], ...
... Visser et. al. [2003] and Han [2003b] have both given the derivation in detail. The integral equation in the ECEF frame is, ...
... We mention that some previous studies have already derived the identical (e.g. Gerlach et al. 2003;Han 2003;Wang et al. 2012) or similar (e.g. Badura et al. 2006;Jäggi et al. 2008) formulation. ...
A new approach based on energy conservation principle for satellite gravimetry mission has been developed and yields more accurate estimation of in situ geopotential difference observables using K-band ranging (KBR) measurements from the Gravity Recovery and Climate Experiment (GRACE) twin-satellite mission. This new approach preserves more gravity information sensed by KBR range-rate measurements and reduces orbit error as compared to previous energy balance methods. Results from analysis of 11 yr of GRACE data indicated that the resulting geopotential difference estimates agree well with predicted values from official Level 2 solutions: with much higher correlation at 0.9, as compared to 0.5-0.8 reported by previous published energy balance studies. We demonstrate that our approach produced a comparable time-variable gravity solution with the Level 2 solutions. The regional GRACE temporal gravity solutions over Greenland reveals that a substantially higher temporal resolution is achievable at 10-d sampling as compared to the official monthly solutions, but without the compromise of spatial resolution, nor the need to use regularization or post-processing.
... The energy integral method in GIF was originally formulated by Jekeli (1999), and was reformulated, implemented and amended by several others (e.g. Han 2003;Cheng & Hsu 2006;Han et al. 2006;Ramillien et al. 2011;Guo et al. 2015). The method in EFF has been formulated several times (Han 2003;Visser et al. 2003;Wang et al. 2012), but a term [ (13)] has always been either neglected or ignored without rigorous verification of its effect on the result. ...
... Han 2003;Cheng & Hsu 2006;Han et al. 2006;Ramillien et al. 2011;Guo et al. 2015). The method in EFF has been formulated several times (Han 2003;Visser et al. 2003;Wang et al. 2012), but a term [ (13)] has always been either neglected or ignored without rigorous verification of its effect on the result. This term is similar, but different from the potential rotation term, which pertains to the energy integral in GIF, and was exactly evaluated by Guo et al. (2015). ...
... Here we follow Han's (2003) logic to provide a rigorous and complete derivation which we believe is also an optimal compromise for understandability and brevity. We particularly address the approximations made in the formulation, both theoretically and numerically using simulations based on realistic models of the Earth system, and provide an approach of computation that minimize the influence of the approximations to negligible level for application to GRACE. ...
Two methods for computing gravitational potential difference (GPD) between the GRACE satellites using orbit data have been formulated based on energy integral; one in geocentric inertial frame (GIF) and another in Earth fixed frame (EFF). Here we present a rigorous theoretical formulation in EFF with particular emphasis on necessary approximations, provide a computational approach to mitigate the approximations to negligible level, and verify our approach using simulations. We conclude that a term neglected or ignored in all former work without verification should be retained. In our simulations, 2 cycle per revolution (CPR) errors are present in the GPD computed using our formulation, and empirical removal of the 2 CPR and lower frequency errors can improve the precisions of Stokes coefficients (SCs) of degree 3 and above by 1-2 orders of magnitudes. This is despite of the fact that the result without removing these errors is already accurate enough. Furthermore, the relation between data errors and their influences on GPD is analysed, and a formal examination is made on the possible precision that real GRACE data may attain. The result of removing 2 CPR errors may imply that, if not taken care of properly, the values of SCs computed by means of the energy integral method using real GRACE data may be seriously corrupted by aliasing errors from possibly very large 2 CPR errors based on two facts: (1) errors of C2,0 manifest as 2 CPR errors in GPD and (2) errors of C2,0 in GRACE data-the differences between the CSR monthly values of C2,0 independently determined using GRACE and SLR are a reasonable measure of their magnitude-are very large. Our simulations show that, if 2 CPR errors in GPD vary from day to day as much as those corresponding to errors of C2,0 from month to month, the aliasing errors of degree 15 and above SCs computed using a month's GPD data may attain a level comparable to the magnitude of gravitational potential variation signal that GRACE was designed to recover. Consequently, we conclude that aliasing errors from 2 CPR errors in real GRACE data may be very large if not properly handled; and therefore, we propose an approach to reduce aliasing errors from 2 CPR and lower frequency errors for computing SCs above degree 2. © The Authors 2015. Published by Oxford University Press on behalf of The Royal Astronomical Society. All rights reserved.
... GRACE satellites are tracking each other in Low Earth Orbit (LEO), so GRACE is called low-low satellite to satellite tracking (LL-SST) mission. The Earth's gravity field variation can be determined by using the observed changes in the inter-satellite distance, position, and acceleration of each satellite (Han, 2003). As it is shown in Figure 3.1, these two satellites constantly maintain a two-way microwave-ranging link between them. ...
... This system detects the distance between the satellites with an accuracy of about 1μm. The variation in the computed inter-satellite range can be transformed into the variation of the Earth's gravity field (Han, 2003). ...
Data from the Gravity Recovery and Climate Experiment (GRACE) has significantly improved our knowledge of the terrestrial water cycle. With the availability of GRACE data from 2002, we are now able to perform even climate change studies with respect to water storage variations. However, as GRACE is already after its expected lifetime, we have to find methods for filling the missing months in the past data and to possibly bridge the gap until GRACE Follow On.
In this study, we, therefore, analyze the potential of Copula-based methods for simulating GRACE coefficients data from other hydrological data sources. The method exploits linear and non-linear relationships between two or more variables by fitting a theoretical Copula function into an empirical bivariate or multivariate distribution function. Finally, new data, which is then consistent with the previously derived dependence structure, can be simulated by evaluating the conditional distribution function given by the theoretical Copula.
First, we want to analyze the applicability of the proposed method to spherical harmonic coefficients data from GRACE. As the approach involves several drawings of random data, we are interested if this random nature has any impact on the results. We, therefore, generate filtered out of unfiltered GRACE coefficients, based on the previously derived dependence structure. The comparison between the simulated and filtered data shows a very good agreement with negligible differences in both of the spatial and spectral domain.
We also want to evaluate if Copula-based methods are able to estimate reliable water storage changes from the independent hydrological data. Therefore, we derive the dependence structure between filtered water storage changes from GRACE and global gridded precipitation data from the Global Precipitation Climatology Center GPCC. Based on the fitted theoretical Copula, we then simulate water storage changes from precipitation data. The Copula-based estimates are compared with filtered GRACE coefficients data in both of the spectral and spatial domain. We also perform a catchment-based analysis between area-aggregated time-series of simulated and GRACE-derived water storage change. The analysis shows that our estimates and the original filtered GRACE coefficients data are in very good agreement. Thus, we conclude that the proposed method is indeed able to fill the missing months in the GRACE-dataset and to extend even the time-series until the launch of GRACE Follow On.
... The degree zero coefficientc 00 in Eq. (2.1) is set equal to 1 due to the fact that M is the total mass of the Earth. The degree one coefficients are related to the location of the Earth's centre of mass, namely, c 10 = 1 R z TRF c ,c 11 = 1 R x TRF and the unknown coefficients: "energy balance approach" (Jekeli, 1999;Han, 2003Han, , 2004Gerlach et al., 2003), "variational equations approach" (Reigber, 1989;Reigber et al., 2002Reigber et al., , 2003Tapley et al., 2005;Beutler et al., 2010a,b;Prange et al., 2008;Jäggi et al., 2010), "short-arc approach" (Mayer-Gürr et al., 2005;Mayer-Gürr, 2006;Mayer-Gürr et al., 2010a), "point-wise acceleration approach" (Reubelt et al., 2003(Reubelt et al., , 2006, and "average acceleration approach" (Ditmar and van Eck van der Sluijs, 2004;Ditmar et al., 2006;Liu, 2008;Liu et al., 2010). ...
Modelling the Earth’s static and time-varying gravity field using a combination of GRACE and GOCE data The main focus of the thesis is modelling the static and time-varying parts of the Earth’s gravity field at the global scale based on data acquired by the Gravity Recovery And Climate Experiment (GRACE) and Gravity field and steady-state Ocean Circulation Explorer (GOCE). In addition, a new methodology is proposed to validate global static gravity field models. Furthermore, the added value of GOCE data to the static and time-varying gravity field retrieval is assessed. Finally, low-frequency noise in GRACE observables derived from its K-band ranging (KBR) data is studied and a new way to cope with it is proposed.
GRACE/GOCE global static gravity field modelling: DGM-1S A new global static gravity field model entitled DGM-1S (Delft Gravity Model, release 1, Satellite-only) is computed by a statistically optimal combination of GRACE and GOCE data. The model is based on seven years of GRACE KBR data, four years of GRACE satellites’ kinematic orbits, 14 months of GOCE kinematic orbits, and 10 months of GOCE Satellite Gravity Gradiometry (SGG) data. Kinematic orbit and KBR data are processed with a variant of the acceleration approach, in which these data are respectively transformed into “three-dimensional (3-D) average acceleration vectors” and “range combinations” (≈ inter-satellite accelerations) with a three-point differentiation. Gravity gradients are processed in the instrument frame. Stochastic models of data noise are built with an auto-regressive moving-average (ARMA) process. The usage of ARMA models ensures that (i) coloured noise in data is appropriately dealt with; and (ii) data are combined in a statistically optimal manner. DGM-1S is compiled up to spherical harmonic degree 250 with a Kaula regularization applied above degree 179. It is found that (a) the usage of GOCE kinematic orbits may not lead to an improvement of a static gravity field model if GRACE data and GOCE gravity gradients are already incorporated; and (b) GOCE gravity gradients manifest their contribution in a combined GRACE/GOCE model above degree 150. For the purpose of an assessment, the DGM-1S, GOCO01S, EIGEN-6S (only its static part), and GOCO02S geoid models are used to compute the corresponding oceanic mean dynamic topography models by subtracting the DNSC08 mean sea surface model. The results are confronted with the state-of-the-art CNES-CLS09 mean dynamic topography model, which shows the best agreement for DGM-1S. Furthermore, the test suggests that the GRACE/GOCE satellite-only models are influenced by a relatively strong high-frequency noise above degree 200. In addition, the test indicates that problems still seem to exist in satellite-only GRACE/GOCE models over the Pacific ocean, where considerable deviations of these models from EGM2008 are detected.
Validating global static gravity field models: quantifying GOCE mission’s added value and inspecting data combination optimality in models produced with surface data The ability of satellite gravimetry data to validate global static gravity field models is studied. Two types of control data are considered: GRACE KBR data and GOCE gravity gradients. The validation is based on an analysis of misfits computed as differences between data observed and those computed with a force model that includes, in particular, a static gravity field model to be assessed. Only “independent” data are used in the model validation, i.e., those that were not used in the production of models under assessment. The methodology is applied to eight models: EGM2008 (truncated at degree 250), EIGEN-6C (only its static part and truncated at degree 250), two GRACE-only models (ITGGrace03 and ITG-Grace2010s), and four GRACE/GOCE models: GOCO01S, EIGEN-6S (only its static part), GOCO02S, and DGM-1S. The validation shows that independent data of both types allow a difference in performance of the models to be observed, despite the fact that the duration of these data is much shorter than that of data used to produce those models. The KBR and SGG control data demonstrate relatively high inaccuracies of EGM2008 in 5 – 22 mHz (27 – 120 cycles-per-revolution, cpr) and 10 – 28 mHz (54 – 150 cpr) frequency ranges, respectively. The latter data also reveal inaccuracies of ITG-Grace2010s in 25 – 37 mHz (135 – 200 cpr) frequency range. The validation in the spatial domain shows that EGM2008 performs weaker than the GRACE/GOCE models. Considering root mean square (RMS) misfits related to the zz gravity gradient component (with z being the nadir axis of the instrument frame), the performance difference in the continental areas poorly covered by terrestrial gravimetry data (Himalayas, South America, and Equatorial Africa) is 76 – 83 %. This difference is explained mostly by a loss of information content of ITG-Grace03 when it was combined with terrestrial gravimetry/satellite altimetry data to produce EGM2008. Furthermore, the revealed performance differences are 4 – 16 % in the continental areas well covered by those data (Australia, North Eurasia, and North America) and 11 % in the world’s oceans. These differences are related to the GOCE mission’s added value to the static gravity field retrieval. It is shown that EIGEN-6C also suffers from a loss of information during data combination, but in a much less pronounced manner. In South America, for instance, this model is found to perform poorer than its satellite-only counterpart, i.e., EIGEN-6S, by only 12 %. The GRACE/GOCE models show in the poorly surveyed continental areas a higher accuracy than ITG-Grace2010s: by 23 – 36 %, which is attributed to the GOCE mission’s added value. The quantified added value is shown to be almost entirely related to the coefficients below degree 200. DGM-1S and GOCO02S show an almost similar performance against GOCE control gravity gradients. Nevertheless, the former model shows a slightly better agreement with KBR control data. Both models agree with control data of both types better than EIGEN-6S.
Assessing GOCE mission’s added value to time-varying gravity field modelling Temporal gravity field variations recovered from KBR data suffer, among others, from a limited spatial resolution and a relatively low accuracy of the East-West changes. I investigate whether a retrieval of these variations can be improved by incorporating GOCE data. To that end, I compare monthly solutions up to degree 120 computed (i) from KBR data alone and (ii) using a statistically optimal combination of KBR data with GOCE kinematic orbit and gravity gradients. The impact of GOCE data is analysed in the context of unconstrained solutions and after an optimal anisotropic filtering. This impact in these two cases is found to be radically different. In the case of unconstrained solutions, a usage of GOCE data allows the noise in these solutions to be reduced by 1 – 2 orders of magnitude. I demonstrate, however, that this reduction is a stabilization effect and is not driven by the information content in GOCE data. In the case of the filtered solutions, the impact stays, in average, at sub-millimeter level in terms of equivalent water heights. This is below the GRACE noise level. The peak impacts reach about 1 cm. This holds true for the combined impact of GOCE kinematic orbit data and gravity gradients as well as for the impact of these data types individually. Relatively, the peak impacts do not exceed 5 – 7 % of the signal amplitude, because they always occur at locations where the time-varying gravity field signal is strong. Nevertheless, I refrain from concluding that added value of GOCE data to the retrieval of temporal gravity changes is always negligible. A number of scenarios are discussed, in which the impact of GOCE data may exceed the level quantified in the study presented.
GRACE global time-varying gravity field modelling: DMT-2 The Delft Mass Transport model, release 2 (DMT-2), similar to its predecessor, i.e., DMT-1, is produced from KBR data. The model consists of a time series of 94 monthly solutions (February 2003 – December 2010). Each solution consists of spherical harmonic coefficients up to degree 120 with respect to DGM-1S. Both unconstrained and optimally filtered solutions are produced. The improvements applied in the production of this new model as compared to its predecessor are usage of: (i) an improved estimation and elimination of the low-frequency noise in residual range combinations, so that strong mass transport signals are not damped; (ii) an improved frequency-dependent data weighting, which allows statistically optimal solutions to be compiled; (iii) release 2 of GRACE level-1B data; (iv) a recent a priori static gravity field model, i.e., DGM-1S; (v) release 5 of the AOD1B model of non-tidal mass re-distribution in the atmosphere and ocean; (vi) the recent ocean tide model EOT11a; and (vii) an improved calibration scheme of the satellites’ accelerometers. It is shown that DMT-2 substantially outperforms its predecessor in terms of spatial resolution, which is proven to be mainly associated with the usage of a more advanced frequency-dependent data weighting. Furthermore, it is confirmed that the usage of release 2 of GRACE level-1B data leads to an elimination of the East-West artifacts. Finally, it is shown that choosing the maximum spherical harmonic degree lower than 120 in the context of monthly gravity field modelling could lead to an underestimation of the signal amplitude and the presence of the so-called “Gibbs” phenomenon in the vicinity of areas with strong mass variations. However, the higher spatial resolution of models produced up to degree 120 is almost entirely attributed to the optimal filtering and is not driven by the information content in unconstrained spherical harmonic coefficients.
The contributions of the thesis The primary contributions of this thesis are as follows:
1. Computing new global static gravity models of a competitive quality.
2. Development of a new methodology to validate global static gravity field models.
3. Quantification of the GOCE mission’s added value to the static and time-varying gravity field modelling.
4. Inspection of data combination optimality in models produced with satellite gravimetry and surface data. This paves the way to developing better strategies to combine satellite and surface gravimetry data in the production of future models.
5. Computing a new GRACE time-varying gravity field model, DMT-2.
6. Demonstrating the importance of an accurate computation and a proper exploitation of stochastic models of noise in satellite gravimetry data in the context of global gravity field modelling.
7. Identifying the origin of low-frequency noise in GRACE KBR-based observables and proposing a new way to cope with it.
