Browsing by Subject "Geodesy"

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Elevation and volume change of the ice sheets from GLAS : a comparison of methods
(2013-12) Felikson, Denis; Schutz, Bob E.
This report compares surface elevation change and volume change esti- mates from three methods: repeat track (RT), crossover (CX), and overlapping footprints (OFP). These three methods use different approaches to group- ing elevation point measurements taken at different measurement epochs and estimating elevation change. Volume changes are calculated from elevation changes in the same manner for all three methods but differences in sampling resolution between the methods affect volume change estimates in different ways. The recently reprocessed Release 633 version of elevation measurements from the Geoscience Laser Altimeter System (GLAS), flown on the Ice, Cloud and land Elevation Satellite (ICESat), are used in this analysis. Both elevation changes and volume changes are compared for both the Greenland Ice Sheet (GrIS) and the Antarctic Ice Sheet (AIS). Additionally, uncertainties in the estimates for each method are quantified and compared. Results are separated by drainage systems and by above/below 2000 m surface elevation for the GrIS. For the AIS, results are aggregated to the East, West, and Penin- vi sula regions. Volume change estimates agree well for the three methods for the GrIS, with estimates of -227.75 ± 2.12 km³/yr, -249.30 ± 3.42 km³/yr, and -218.24 ± 7.39 km³/yr for the RT, CX, and OFP methods, respectively. These estimates are similar to those published from previous studies. For the AIS, however, larger discrepancies are found in the estimates. This stems primarily from a large discrepancy in the volume change estimate of the East AIS, where the RT, CX, and OFP methods estimate volume changes of 33.39 ± 1.42 km³/yr, 46.42 ± 5.46 km³/yr, and -2.72 ± 2.12 km³/yr, respectively. It's not entirely clear why this large discrepancy exists in this particular region, and elevation change estimates for a few particular drainage systems in this region are examined. Previously published volume changes for the AIS also show a large scatter and more work must be done to reconcile the various estimates. Finally, the volume change uncertainties reported do not completely account for the discrepancies in most regions. Additional analysis must be done to completely quantify all error sources.
An ensemble solution for the Earth's time-varying gravitational field from the NASA/DLR GRACE mission
(2013-08) Sakumura, Carly Frances; Bettadpur, Srinivas Viswanath, 1963-
Several groups produce estimates of the Earth's time-varying gravitational field with data provided by the NASA/DLR Gravity Recovery and Climate Experiment (GRACE) mission. These unprecedented highly accurate global data sets track the time-variable transport of mass across and underneath the surface of the Earth and give insight into secular, seasonal, and sub seasonal variations in the global water supply. Knowledge gained from these products can inform and be incorporated into ocean and hydrological models and advise environmental policy planning. Therefore, a complete understanding of the accuracy and variations between these different fields is necessary, and the most accurate possible solutions desired. While the various gravity fields are similar, differences in processing strategies and tuning parameters result in solutions with regionally specific variations and error patterns. This study analyzed the spatial, temporal, and spectral variations between four different gravity field products. The knowledge gained in this analysis was used to develop an ensemble solution that harnesses the best characteristics of each individual field to create an optimal model. Multiple methods were used to combine and analyze the individual and ensemble solutions. First a simple mean model was created; then the different solutions were weighted based on the formal error estimates as well as the monthly deviation from the arithmetic mean ensemble. These ensemble models as well as the four individual data center solutions were analyzed for bias, long term trend, and regional variations between the solutions, evaluated statistically to assess the noise and scatter within the solutions, and compared to independent hydrological models. Therefore, the form and cause of the deviations between the models, as well as the impact of these variations, is characterized. The three ensemble solutions constructed in this analysis were all effective at reducing noise in the models and better correlate to hydrological processes than any individual solution. However, the scale of these improvements is constrained by the relative variation between the individual solutions as the deviation of these individual data products from the hydrological model output is much larger than the variations between the individual and ensemble solutions.
Gravity field estimation for next generation satellite missions
(2017-05) McCullough, Christopher Michael; Bettadpur, Srinivas Viswanath, 1963-; Tapley, Byron D; Fowler, Wallace T; Wilson, Clark R; Poole, Steve
For the past, nearly 15 years, the Gravity Recovery and Climate Experiment (GRACE) has provided an invaluable view of mass variability in the Earth system. During its time on orbit it has enabled unprecedented contributions to hydrology, oceanography, and the cryosphere; however, GRACE is currently approaching the end of its lifetime. As this approaches and future dedicated satellite gravity missions are poised to continue its legacy, it's important to highlight limitations in our current knowledge and explore areas of improvement for future analysis. This work returns to the first principles of gravity field estimation and explores some of the basic assumptions and idiosyncrasies inherent in the estimation of Earth's gravitational field. Current gravity field estimation from GRACE attempts to optimally combine GPS observables, which provide absolute positioning, with high accuracy, relative inter-satellite measurements (KBR). While an optimal data fusion procedure is utilized, empirical analysis has indicated that artificial down-weighting of the GPS observable provides significant improvements to estimates of the gravitational field. The necessity of this ad-hoc treatment signals a misunderstanding in the contribution of each observable to gravity field estimates and deficiencies in the modeling of each observable. The analysis of this misunderstanding begins with an examination of the GPS observable's ability to independently recover estimates of the spherical harmonic coefficients. This not only provides insight into the effect of GPS on the gravitational field, but examines the efficacy of using a single satellite to fill a possible gap between GRACE and its follow-on mission. While these single satellite derived gravitational fields have limited accuracy, their combination with satellite laser ranging (SLR) allows for the determination of large spatial scale, long term trends from low degree harmonics (7x7). Additionally, thorough examination of the combined gravity field solutions indicates that the GPS observable is vital to stabilization of estimated parameters which perturb at low frequencies, a significant weakness for the relative inter-satellite ranging observable. These low frequency parameters -- which include the satellite initial conditions, accelerometer dynamicals, low degree harmonics, sectorial harmonics, and harmonics of resonant order -- are also the most susceptible to contamination by dynamical modeling error. Therefore, it is necessary to stochastically model the observation error with high fidelity, most notably the frequency dependence caused by errors in the background dynamical models. Accurate stochastic modeling of the observables is achieved by reexamining the GRACE estimation problem from the Bayesian perspective. This viewpoint highlights typical assumptions made in nominal GRACE processing, most importantly that observation errors are independently Gaussian distributed. Analysis of this assumption indicates its inaccuracy, necessitating the utilization of algorithms which enable modeling of the frequency dependence of the observable errors, through the observation covariance. The most important of these error sources is the manifestation of dynamical modeling error, which perturbs predominantly at low frequency and the orbital period, similarly to the main contributions of the GPS observable. Accounting for the frequency dependence of the observation errors shows the ability to improve optimal data fusion, reduce error in estimates of the gravitational field by mitigating stripes and, most importantly, drastically improves the formal characterization of error in the estimated gravitational fields; facilitating scientific interpretation and prognostication of Earth's climate variability, optimal combination with independent datasets and a priori constraints, and optimal assimilation of GRACE data products with Earth system models.
Improving the observation of time-variable gravity using GRACE RL04 data
(2010-12) Bonin, Jennifer Anne; Tapley, Byron D.
The Gravity Recovery and Climate Experiment (GRACE) project has two primary goals: to determine the Earth’s mean gravitational field over the lifetime of the mission and to observe the time-variable nature of the gravitational field. The Center for Space Research's (CSR) Release 4 (RL04) GRACE solutions are currently created via a least-squares process that assimilates data collected over a month using a simple boxcar window and determines a spherical harmonic representation of the monthly gravitational field. The nature of this technique obscures the time-variable gravity field on time scales shorter than one month and spatial scales shorter than a few hundred kilometers. A computational algorithm is developed here that allows increased temporal resolution of the GRACE gravity information, thus allowing the Earth's time-variable gravity to be more clearly observed. The primary technique used is a sliding-window algorithm attached to a weighted version of batch least squares estimation. A number of different temporal windowing functions are evaluated. Their results are investigated via both spectral and spatial analyses, and globally as well as in localized regions. In addition to being compared to each other, the solutions are also compared to external models and data sets, as well as to other high-frequency GRACE solutions made outside CSR. The results demonstrate that a GRACE solution made from at least eight days of data will provide a well-conditioned solution. A series of solutions made with windows of at least that length is capable of observing the expected near-annual signal. The results also indicate that the signals at frequencies greater than 3 cycles/year are often smaller than the GRACE errors, making detection unreliable. Altering the windowing technique does not noticeably improve the resolution, since the spectra of the expected errors and the expected non-annual signals are very similar, leading any window to affect them in the same manner.
Surface deformation mapping and automatic feature detection over the Permian Basin using InSAR
(2022-06-08) Staniewicz, Scott; Chen, Jingyi "Ann"; Bettadpur, Srinivas; Hennings, Peter; Humphreys, Todd; Olson, Jon
The Permian Basin has become the United States' largest producer of oil and gas over the past decade. During the same time, it has experienced a sharp rise in the number of induced earthquakes. In order to better understand the damage potential from induced earthquakes, new data and monitoring approaches are critically needed. Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique that measures surface deformation over broad areas with 10s-100s meter spatial resolution and up to millimeter-to-centimeter accuracy. These measurements can be used to derive information about Earth’s subsurface and assess induced seismic risks. However, it is difficult to perform basin-scale surface deformation mapping and automatic feature detection using InSAR because the signal-to-noise ratio (SNR) of the deformation signals compared to tropospheric noise is extremely low. It is common to assume that the Permian Basin is rigid enough that the subtle deformation associated with oil and gas production and wastewater injection are not detectable by InSAR. In this dissertation, we develop methods for characterizing tropospheric noise and its power spectral density directly from InSAR observations. We show that the tropospheric noise distribution is non-Gaussian, and a small portion of SAR scenes are corrupted by up to ±15 cm noise outliers associated with storms and heat waves. This finding is significant because most of the InSAR time series solutions are optimal only when noise follows a Gaussian distribution. We design robust and scalable time series algorithms to reconstruct the temporal evolution of surface deformation in this challenging scenario, and we achieved basin-wide millimeter-level accuracy based on independent GPS validation. We observe numerous subsidence and uplift features near active production and disposal wells, as well as linear deformation patterns associated with fault activities near clusters of induced earthquakes. Furthermore, we designed a new computer vision algorithm for detecting the size and location of unknown deformation features in large volumes of InSAR data. We are able to determine whether a detected feature is associated with tropospheric artifacts or real deformation signals based on a realistic tropospheric noise model derived from InSAR data.