1,720,955 research outputs found
Next-generation satellite gravimetry for measuring mass transport in the Earth system
No full textThe main objective of the thesis is to identify the optimal set-up for future satellite gravimetry missions aimed at monitoring mass transport in the Earth’s system.The recent variability of climatic patterns, the spread of arid regions and associ- ated changes in the hydrological cycle, and vigorous modifications in the ice coverage at polar regions have been attributed to anthropogenic influence. As such, it is important to continue monitoring the Earth system in order to properly constrain and improve the geophysical and climatic models and to better interpret the causes and consequences of climate change. Satellite gravimetric data are also exploited to further the knowledge on other geophysical processes with high societal and scientific impact, such as megathrust earthquakes, drought monitoring and Glacial Isostatic Adjustment (GIA). The primary focus of the study is to properly quantify the errors in the gravimetric data to be collected by future gravimetric satellites, in particular those related to the measurement of the temporal gravitational field variations. One source of errors comes from the background force models describing rapid mass transport processes; another error source is related to the background static gravity field model. These models are used to complement geophysical signals that are missing or improperly represented in the gathered satellite gravity data. However, they are built on the basis of in situ data that lack global coverage and, therefore, suffer from a limited accuracy (particularly in remote areas). Although the fidelity of these models is constantly improving, the satellite data accuracy is also increasing with the on-going technological and methodological advances. etermining the net effect of these conflicting trends is the main driver to study the propagation of errors in background models into the estimated models. Other sources of errors arise from imperfections of the on-board sensors, such as the ranging sensor or the Global Navigation Satellite System (GNSS) receiver. The influence of the sensors errors is divided into the major independent contributions, with the corresponding frequency description, and assembled into a detailed noise model. The model predicts the effects of i) the inaccurately known orbital positions, ii) the noise in the inter-satellite metrology system, iii) the noise in the on-board accelerometers, iv) the wrongly-estimated Line of Sight (LoS) frame accelerations resulting from errors in the radial orbital velocities, and v) errors in the orientation of the LoS vector. The model has been validated with the help of actual Gravity Recov- ery And Climate Experiment (GRACE) a posteriori residuals, which are compared to the output of the noise model considering a simulated GRACE mission. Therefore, once the assumptions describing sensor and model accuracies are modified to reflect those predicted for future gravimetric missions, it is reasonable to expected that this noise model reproduces realistic errors for those missions. Also relevant is the analysis of the sensitivity of the data in terms of isotropy. As learned from the GRACE mission, the nearly-constant North-South alignment of the measurement direction makes the data less sensitive to gravitational changes along the East-West direction. Although formally not an error itself, the anisotropic data sensitivity amplifies the errors in the data. The sensor and model errors are propagated firstly to the gravimetric data and further to the gravitational field, in full-scale simulations of the cartwheel, trailing and pendulum satellite formations. The results are analysed in terms of i) the observation error in the frequency domain and ii) the estimated gravity field model error in the frequency and spatial domains. The error budgets for these formations are also quantified. The results indicate that the pendulum formation with no along-track displacement is least sensitive to model and sensors errors, in particular to temporal aliasing. The conducted study reveals serious limitations in the cartwheel mission concept, since the orbit errors are considerably amplified by the diagonal components of the gravity gradient tensor, while the pendulum and trailing formations are only affected by (small) off-diagonal components. The spatial error patterns provide valuable clues on how to best combine the different formation geometries in order to produce minimum anisotropy in the sensitivity of collected data. The data from the pendulum formation show some anisotropic sensitivity but the combination of such data with those from a trailing formation, such as the GRACE Follow On (GFO), would eliminate this disadvantage (as well as the low accuracy near the poles of the pendulum formation). Unlike alternative proposals for dual-pair satellite missions, such as the Bender constellation, the dual trailing/pendulum constellation would provide global coverage in case of failure of one satellite pair and dense temporal sampling at high latitudes. Furthermore, the data from gravimetric missions are shown to benefit greatly from the data gathered by numerous non-dedicated satellites. From the conducted simulations, it is predicted that the achievable temporal resolution is increased to a few days for the degrees below 10 and, crucially, with no significant level of temporal aliasing. Longer estimation periods allow for higher degrees to be estimated, with greatly reduced effects of temporal aliasing in the resulting gravity field models.Geoscience & Remote SensingCivil Engineering and Geoscience
Conversion of time-varying Stokes coefficients into mass anomalies at the Earth’s surface considering the Earth’s oblateness
No full textTime-varying Stokes coefficients estimated from GRACE satellite data are routinely converted into mass anomalies at the Earth’s surface with the expression proposed for that purpose by Wahr et al. (J Geophys Res 103(B12):30,205–30,229, 1998). However, the results obtained with it represent mass transport at the spherical surface of 6378 km radius. We show that the accuracy of such conversion may be insufficient, especially if the target area is located in a polar region and the signal-to-noise ratio is high. For instance, the peak values of mean linear trends in 2003–2015 estimated over Greenland and Amundsen Sea embayment of West Antarctica may be underestimated in this way by about 15%. As a solution, we propose an updated expression for the conversion of Stokes coefficients into mass anomalies. This expression is based on the assumptions that: (i) mass transport takes place at the reference ellipsoid and (ii) at each point of interest, the ellipsoidal surface is approximated by the sphere with a radius equal to the current radial distance from the Earth’s center (“locally spherical approximation”). The updated expression is nearly as simple as the traditionally used one but reduces the inaccuracies of the conversion procedure by an order of magnitude. In addition, we remind the reader that the conversion expressions are defined in spherical (geocentric) coordinates. We demonstrate that the difference between mass anomalies computed in spherical and ellipsoidal (geodetic) coordinates may not be negligible, so that a conversion of geodetic colatitudes into geocentric ones should not be omitted.Physical and Space Geodes
Regularization and analysis of GRACE mass anomaly time series by a minimization of month-to-month year-to-year double differences
No full textA methodology has been developed to estimate the accuracy of GRACE monthly solutions as functions of space or time without using independent geophysical models. An application of the methodology to several commonly-used solution time-series reveals that the ITSG-Grace2016 solutions show the highest accuracy among those considered. Furthermore, it is found that the accuracy of background models exploited to produce GRACE RL05 solutions was likely insufficient to describe adequately mass re-distribution in the oceans at both short (< 1 month) and long (>1 month) time scales. Insufficiently accurate modelling of ocean signals may reduce the accuracy of mass anomaly estimates not only over oceans, but also in the coastal areas of continents.Physical and Space Geodes
How to quantify the accuracy of mass anomaly time-series based on GRACE data in the absence of knowledge about true signal?
No full textA novel technique has been developed to assess noise levels in GRACE-based mass anomaly time-series when the true signal is not known. The technique is based on computing an optimal combination of analyzed time-series in the presence of a regularization. To find the optimal weights associated with individual time-series, variance component estimation is used. In this way, noise variance (and, therefore, noise standard deviation) for each time-series is estimated. To validate the developed technique, altimetry-based water level variations in several lakes are used as independent information. Those variations are compared with mass anomaly time-series extracted from eight GRACE models of time-varying Earth’s gravity field from different data processing centers. The lake tests demonstrate a good performance of the developed technique, provided that the regularization functional is properly chosen. The best results are obtained with a novel regularization functional, which can be understood as a minimization of year-to-year differences between the values of the second time-derivative of the unknown function. Finally, the GRACE models under consideration are analyzed globally. It is found that the models produced at the Institute of Geodesy at Graz University of Technology (ITSG) and at the Center of Space Research of the university of Texas at Austin (CSR) show, in general, the lowest noise levels. The aforementioned lake tests also allow the signal damping in GRACE models to be quantified. It is shown, among others, that regularized GRACE models may suffer from a noticeable signal damping (up to ∼ 15 %).Physical and Space Geodes
How to properly convert Stokes coefficients into mass anomalies?
No full textGeoscience & Remote SensingCivil Engineering and Geoscience
Gravity field modeling on the basis of GRACE range-rate combinations: Current results and challenge
No full textGeoscience and Remote SensingCivil Engineering and Geoscience
Analysis of mass variations in Greenland by a novel variant of the mascon approach
No full textThe Greenland ice sheet (GrIS) is currently losing mass, as a result of complex mechanisms of ice-climate interaction that need to be understood for reliable projections of future sea level rise. The thesis focuses on the estimation of mass anomalies in Greenland using data from the GRACE satellite gravity mission. Monthly GRACE gravity field solutions are post-processed using a new variant of the "mascon approach''. Greenland is covered with multiple distinctive "mascons'', assuming the mass anomalies within each one are laterally-homogeneous.Gravity disturbances at mean satellite altitude are synthesized from the GRACE spherical harmonic coefficients. They are used as pseudo-observations to estimate the mascon mass anomalies using weighted least-squares techniques. No regularization is applied. The full noise covariance matrix of gravity disturbances is propagated from the full noise covariance matrix of spherical harmonic coefficients using the law of covariance propagation. Those matrices represent a complete stochastic description of random noise in the data, provided that it is Gaussian. The inverse noise covariance matrix is used as a weight matrix in the weighted least-squares estimate of the mascon mass anomalies. The limited spectral content of the gravity disturbances is accounted for by applying a low-pass filter to the design matrix providing a spectrally consistent functional model.Using numerical experiments with simulated signal and data, we demonstrate the importance of the data weighting and of the spectral consistency between the mascon model and the pseudo-observations. The developed methodology is applied to process real GRACE data using CSR RL05 monthly gravity field solutions with full noise covariance matrices. We distinguish five GrIS drainage systems. The obtained mass anomaly estimates per mascon are integrated over individual drainage systems, as well as over entire Greenland. We find that using a weighted least-squares estimator reduces random noise in the estimates by factors ranging from 1.5 to 3.0, depending on the drainage system. Furthermore, we compare the de-trended mascon mass anomaly time-series with similar time-series from the Regional Atmospheric Climate Model (RACMO 2.3), which describes the Surface Mass Balance (SMB). We show that the weighted least-squares estimate reduces the discrepancies between the time-series by 24\%--47\%.Then, we combine GRACE mass anomaly estimates, SMB model outputs, and ice discharge data to systematically analyze the mass budget of Greenland at various temporal and spatial scales. Among others, we reveal a substantial seasonal meltwater storage, which peaks in July, reaching in total Gt. Meltwater storage is particularly intense in the northern, northwestern and southeastern drainage systems. An analysis of outlet glacier velocities shows that the contribution of ice discharge to the seasonal mass variations is minor, at a level of only a few Gt. In addition, we propose a simple way to use GRACE data for validating SMB model outputs in winter, based on the fact that ice discharge cannot be negative.Finally, we use numerical simulations and real data to identify the optimal GRACE data processing strategy (primarily the size of the mascons) for three temporal scales of interest: monthly mass anomalies, mean mass anomalies per calendar month, and long-term linear trends. We show that the two major contributors to the error budgets are random errors and parameterization (model) errors; the latter are caused by a spatial variability of actual mass anomalies within individual mascons. We find that the errors in long-term linear trend estimates are mainly caused by the parameterization errors, and that accurate estimates require small size mascons in combination with the ordinary least-squares estimator. The error budget of mean mass anomalies per calendar month is dominated by the parameterization error when the size of mascons is large and by random errors otherwise. Hence, accurate estimates require mascons of intermediate size in combination with a weighted least-squares estimator. Finally, we find that random errors are the dominant error source in monthly mass anomalies. We advise to use in this case large mascons and a weighted least-squares estimator.Our new variant of the mascon approach and the results of this thesis can be used in support of future research on GrIS hydrology, glacier dynamics, and surface mass balance, as well as their mutual interactions
A SAR-derived long-term record of glacier evolution in North-West Greenland
No full textObservations show that the Greenland ice sheet is losing mass with accelerating pace. Ice discharge trough outlet glaciers contributes approximately for half of the mass loss. However, the role of marine-terminating outlet glaciers on the response of the Greenland ice sheet to climate change is relatively unknown. In recent years, observations have shown dramatic changes in the velocity and front position in a number of marine-terminating outlet glaciers, but a questions remains why some outlet glaciers are stable and others are not. Consequently, predictions on the evolution of the Greenland ice sheet are uncertain until a realistic representation of marine-terminating outlet glaciers is possible. More specifically, significant uncertainty exists on the link between climate forcing and marine-terminating outlet glacier behaviour. This link has been associated to glaciers-specific factors, such as bedrock topography and fjord width, but more glaciers need to be studied. Here we present a study that focussed on the response of different glaciers to a similar climate forcing, thereby taking into account their topographic situation. SAR observations of seven marine-terminating glaciers in the Uummannaq-bay (West-Greenland) are used to estimate terminus positions and glacier flow velocity. The observations are acquired between 1991 - 2014 using ERS, Envisat and TerraSAR-X, thereby extending the length of the state-of-the-art records. Terminus positions are manually digitized and an equivalent position is determined on the glacier flowline, a significant improvement with respect to the box-method. Glacier flow velocities are obtained using ICC offset tracking. The estimated offsets of ERS and Envisat image pairs were noisy, but have been filtered using the along-track velocity profile that was accurately estimated with TerraSAR-X image pairs. The results showed that the outlet glaciers in this region have been stable during the 1990's, and that the warm winter of 2003 initiated retreat. Three glaciers (Lille Gletscher, Umiammakku Isbræ and Inngia Isbræ) have found to show terminus retreat, of which Lille Gletscher and Inngia Isbræ show long-term speed-up. All other glaciers have a stable terminus position, and show no long-term acceleration. Surface air temperature and sea surface temperature show an increasing trend since 1980, whereas the duration of high ice concentrations (sea-ice melange) becomes shorter each year. The retreat of Umiammakku Isbræ and Inngia Isbræ starts after the winter of 2003, in which surface air temperatures were exceptionally high and the period of sea ice was short. The years after, this retreat continued even under normal climatic conditions. Inngia Isbræ showed an ongoing retreat of approximately 6 km between 2003 and 2013, and its flow velocity is estimated to tripled from 500 m/yr to 1500 m/yr. On the contrary, Umiammakku Isbræ ceased its 4 km retreat in 2010, and did not show an increase in flow velocity. Next to long-term patterns, seasonal patterns are identified to differ from glacier to glacier. The terminus position of Rink Isbræ shows a seasonal variation of 1 km, whereas the terminus position of Støre Gletscher only fluctuates around 200 m. The three retreating glaciers are located in a shallow fjord (< 300 m depth) with a reversed bedrock slope. Their retreat into deeper waters initiated a positive feedback-loop leading to multi-year retreat. Glaciers located in a deep fjord (Rink Isbræ and Støre Gletscher) are exposed to warm Atlantic waters, and consequently subject to submarine-melting, but our results show that their front position remained stable and their flow velocity did not increase. The fact that the retreating glaciers are located in a shallow fjord, and exposed to cold Polar water, could indicate that submarine-melting is not a major factor controlling glacier retreat. This also indicates that the bedrock topography is important for a glaciers sensitivity to climate forcing. It is recommended that more effort is put into observations of the submarine-melting process and high resolution bedrock topography and bathymetry, as the relative importance of submarine-melting with respect to the surface mass balance and calving is still obscure. Nevertheless, our results suggest that there is an important link between the fjord depth at the terminus location and the sensitivity to climate forcing.Civil Engineering and GeosciencesGeoscience and Remote Sensin
Analysis of noise in K-Band Ranging data: An investigation of noise in KBR ranging data of NASA’s and GFZ’s GRACE Follow-On mission. Detection of outliers and estimation on the noise power spectral density.
No full textThe GRACE Follow-On mission is using classical K-Bandranging (KBR) and a new laser-ranging interferometry (LRI) method. The lattergives ranging data two orders of magnitude more accurate compared to theclassical K-Band ranging data (Dahl C et al. 2016). This gives a newopportunity for analyzing KBR noise by defining KBR noise as the differencebetween the KBR and LRI ranging data. In order to get a realization of KBR noise, the KBR andLRI epochs should be aligned and outliers have to be removed. An interpolation on the LRI data to make the LRI epochsaligned with the KBR epochs did not give sufficient results. Therefore only theoverlapping epochs were used as input for the next step of outlier detection, atotal of 2,250,000 epochs. The outlier detection method starts by taking theabsolute difference between the KBR and LRI range-rates. The largestdifferences were investigated where the original range-rate was compared to anestimation of the true value. This ‘true’ value is found by the use of a thirddegree polynomial function through the six range-rates that lie next to thesuspected outlier. The outlier detection method removes LRI range-rates thatdiffer more than 2.0*10-8 m/s from the estimated true value and KBRrange-rates that differ more than 7.7*10-7 m/s. 3764 LRI epochs and362 KBR epochs were labelled as outliers. After the outlier detection a histogram was made for theKBR range-rate noise. It was found that in order to get a normal distributionof KBR range-rate noise, the interval had to be in the range of <-3*10-7 m/s, 3*10-7m/s>. 59,000 still fell out of this noise range. 54,000 of these pointsformed 10 major clusters together where a subtraction of the KBR and LRIrange-rates did not give a realization of noise, but a trend similar to theoriginal signal and are therefore likely related to a clocking error. These54,000 points were therefore also removed from the dataset. To the remaining KBR range-rate noise values a thresholdof 3 was applied, removing an additional 4972 noisevalues that were larger than 4.03*10-7 m/s. Finally , an estimation of the PSD of the KBR range-ratenoise was made and compared to an oldPSD image of December 2008 of the GRACEmission. The PSD plot of this project shows three peaks between 10-4Hz and 10-3 Hz. These peaks are not present in the PSD plot found inliterature of the GRACE mission. Except from the second and the third peak, thePSD values of the KBR range-rate noise of this project were lower, up to twoorders of magnitude in the lower frequency range (around 10-4 Hz)and one order of magnitude in the higher frequency range (around 10-1Hz).</p
A comparison of physics-based model computed and GNSS observed vertical land displacement of Greenland
No full textBy combining the RACMO2.3 RCM output with GRACE non-tidal ocean and atmospheric pressure anomaly as well as geo-center motion corrections from 2003 to 2017, a physics-based model to estimate the vertical displacement of the Greenland surface bedrock is created. This model is able to convert the mass and pressure anomalies into vertical displacement in the spherical harmonic domain with the use of load deformation coefficients based on the PREM earth model. The computed vertical displacement has a weak correlation with the observed displacement as measured by the GNET GNSS station network. Both the computed and observed vertical movement show seasonal signals, however the computed signal has both a lower amplitude and different phase to the observed displacement. This could possibly be explained by the selection of the GNET stations used in the analysis or possibly the implementation of the non-tidal ocean and atmospheric pressure anomaly. Questions regarding the optimization of the processing within the model are discussed but remain open. Recommendations for further research into the complexity of the system and shortcomings of the model are discussed in detail.Geoscience and Remote Sensin
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