The effect of EGM2008-based normal, normal-orthometric and Helmert orthometric height systems on the Australian levelling network

This paper investigates the normal-orthometric correction used in the definition of the Australian Height Datum, and also computes and evaluates normal and Helmert orthometric corrections for the Australian National Levelling Network (ANLN). Testing these corrections in Australia is important to establish which height system is most appropriate for any new Australian vertical datum. An approximate approach to assigning gravity values to ANLN benchmarks (BMs) is used, where the EGM2008-modelled gravity field is used to "re-construct" observed gravity at the BMs. Network loop closures (for first- and second-order levelling) indicate reduced misclosures for all height corrections considered, particularly in the mountainous regions of south eastern Australia. Differences between Helmert orthometric and normal-orthometric heights reach 44 cm in the Australian Alps, and differences between Helmert orthometric and normal heights are about 26 cm in the same region. Normal orthometric heights differ from normal heights by up to 18 cm in mountainous regions >2,000 m. This indicates that the quasigeoid is not compatible with normal-orthometric heights in Australia

A Synthetic Earth Gravity Model Designed Specifically for Testing Regional Gravimetric Geoid Determination Algorithms

A synthetic [simulated] Earth gravity model (SEGM) of the geoid, gravity and topography has been constructed over Australia specifically for validating regional gravimetric geoid determination theories, techniques and computer software. This regional high-resolution (1-arc-min by 1-arc-min) Australian SEGM (AusSEGM) is a combined source and effect model. The long-wavelength effect part (up to and including spherical harmonic degree and order 360) is taken from an assumed errorless EGM96 global geopotential model. Using forward modelling via numerical Newtonian integration, the short-wavelength source part is computed from a high-resolution (3-arc-sec by 3-arc-sec) synthetic digital elevation model (SDEM), which is a fractal surface based on the GLOBE v1 DEM. All topographic masses are modelled with a constant mass-density of 2,670 kg/m3. Based on these input data, gravity values on the synthetic topography (on a grid and at arbitrarily distributed discrete points) and consistent geoidal heights at regular 1-arc-min geographical grid nodes have been computed. The precision of the synthetic gravity and geoid data (after a first iteration) is estimated to be better than 30 μ Gal and 3 mm, respectively, which reduces to 1 μ Gal and 1 mm after a second iteration.The second iteration accounts for the changes in the geoid due to the superposed synthetic topographic mass distribution. The first iteration of AusSEGM is compared with Australian gravity and GPS-levelling data to verify that it gives a realistic representation of the Earth’s gravity field. As a by-product of this comparison, AusSEGM gives further evidence of the north–south-trending error in the Australian Height Datum. The freely available AusSEGM-derived gravity and SDEM data, included as Electronic Supplementary Material (ESM) with this paper, can be used to compute a geoid model that, if correct, will agree to in 3 mm with the AusSEGM geoidal heights, thus offering independent verification of theories and numerical techniques used for regional geoid modelling

Unification of New Zealand's local vertical datums: iterative gravimetric quasigeoid computations

New Zealand uses 13 separate local vertical datums (LVDs) based on normal-orthometric-corrected precise geodetic levelling from 12 different tide-gauges. We describe their unification using a regional gravimetric quasigeoid model and GPS-levelling data on each LVD. A novel application of iterative quasigeoid computation is used, where the LVD offsets computed from earlier models are used to apply additional gravity reductions from each LVD to that model. The solution converges after only three iterations yielding LVD offsets ranging from 0.24 m to 0.58 m with an average standard deviation of 0.08 m. The so-computed LVD offsets agree, within expected data errors, with geodetically levelled height differences at common benchmarks between adjacent LVDs. This shows that iterated quasigeoid models do have a role in vertical datum unification

Towards an International Height Reference System: insights from the Colorado geoid experiment using AUSGeoid computation methods

We apply the AUSGeoid data processing and computation methodologies to data provided for the International Height Reference System (IHRS) Colorado experiment as part of the International Association of Geodesy Joint Working Groups 0.1.2 and 2.2.2. This experiment is undertaken to test a range of different geoid computation methods from international research groups with a view to standardising these methods to form a set of conventions that can be established as an IHRS. The IHRS can realise an International Height Reference Frame to be used to study physical changes on and within the Earth. The Colorado experiment study site is much more mountainous (maximum height 4401 m) than the mostly flat Australian continent (maximum height 2228 m), and the available data over Colorado are different from Australian data (e.g. much more extensive airborne gravity coverage). Hence, we have tested and applied several modifications to the AUSGeoid approach, which had been tailored to the Australian situation. This includes different methods for the computation of terrain corrections, the gridding of terrestrial gravity data, the treatment of long-wavelength errors in the gravity anomaly grid and the combination of terrestrial and airborne data. A new method that has not previously been tested is the application of a spherical harmonic high-pass filter to residual anomalies. The results indicate that the AUSGeoid methods can successfully be used to compute a high accuracy geoid in challenging mountainous conditions. Modifications to the AUSGeoid approach lead to root-mean-square differences between geoid models up to ~ 0.028 m and agreement with GNSS-levelling data to ~ 0.044 m, but the benefits of these modifications cannot be rigorously assessed due to the limitation of the GNSS-levelling accuracy over the computation area

Combining EGM2008 and SRTM/DTM2006.0 residual terrain model data to improve quasigeoid computations in mountainous areas devoid of gravity data

A global geopotential model, like EGM2008, is not capable of representing the high-frequency components of Earth?s gravity field. This is known as the omission error. In mountainous terrain, omission errors in EGM2008, even when expanded to degree 2,190, may reach amplitudes of10cm and more for height anomalies. The present paper proposes the utilisation of high-resolution residual terrain model (RTM) data for computing estimates of the omission error in rugged terrain. RTM elevations may be constructed as the difference between the SRTM (Shuttle Radar Topography Mission) elevation model and the DTM2006.0 spherical harmonic topographic expansion. Numerical tests, carried out in the German Alps with a precise gravimetric quasigeoid model (GCG05) and GPS/levelling data as references, demonstrate that RTM-based omission error estimatesimprove EGM2008 height anomaly differences by 10cm in many cases. The comparisons of EGM2008-only height anomalies and the GCG05 model showed 3.7 cm standard deviation after a bias-fit. Applying RTM omission error estimates to EGM2008 reduces the standard deviation to 1.9 cm which equates to a significant improvement rate of 47%. Using GPS/levelling data strongly corroborates thesefindings with an improvement rate of 49%. The proposed RTM approach may be of practical value to improve quasigeoid determination in mountainous areas without sufficient regional gravity data coverage, e.g., in parts of Asia, South America or Africa. As a further application, RTMomission error estimates will allow refined validation of global gravity field models like EGM2008 from GPS/levelling data

Variance component estimation uncertainty for unbalanced data: Application to a continent-wide vertical datum

Variance component estimation (VCE) is used to update the stochastic model in least-squares adjustments, but the uncertainty associated with the VCE-derived weights is rarely considered. Unbalanced data is where there is an unequal number of observations in each heterogeneous dataset comprising the variance component groups. As a case study using highly unbalanced data, we redefine a continent-wide vertical datum from a combined least-squares adjustment using iterative VCE and its uncertainties to update weights for each data set. These are: (1) a continent-wide levelling network, (2) a model of the ocean’s mean dynamic topography and mean sea level observations, and (3) GPS-derived ellipsoidal heights minus a gravimetric quasigeoid model. VCE uncertainty differs for each observation group in the highly unbalanced data, being dependent on the number of observations in each group. It also changes within each group after each VCE iteration, depending on the magnitude of change for each observation group’s variances. It is recommended that VCE uncertainty is computed for VCE updates to the weight matrix for unbalanced data so that the quality of the updates for each group can be properly assessed. This is particularly important if some groups contain relatively small numbers of observations. VCE uncertainty can also be used as a threshold for ceasing iterations, as it is shown—for this data set at least—that it is not necessary to continue time-consuming iterations to fully converge to unity

Mutual Validation of GNSS Height Measurements and High-precision Geometric-astronomical Leveling

The method of geometric-astronomical leveling is presented as a suited technique for the validation of GNSS (Global Navigation Satellite System) heights. In geometric-astronomical leveling, the ellipsoidal height differences are obtained by combining conventional spirit leveling and astronomical leveling. Astronomical leveling with recently developed digital zenith camera systems is capable of providing the geometry of equipotential surfaces of the gravity field accurate to a few 0.1 mm per km. This is comparable to the accuracy of spirit leveling. Consequently, geometric-astronomical leveling yields accurate ellipsoidal height differences that may serve as an independent check on GNSS height measurements at local scales. A test was performed in a local geodetic network near Hanover. GPS observations were simultaneously carried out at five stations over a time span of 48 h and processed considering state-of-the-art techniques and sophisticated new approaches to reduce station-dependent errors. The comparison of GPS height differences with those from geometric-astronomical leveling shows a promising agreement of some millimeters. The experiment indicates the currently achievable accuracy level of GPS height measurements and demonstrates the practical applicability of the proposed approach for the validation of GNSS height measurements as well as the evaluation of GNSS height processing strategies

Contribution of Cystine-Glutamate Antiporters to the Psychotomimetic Effects of Phencyclidine

Altered glutamate signaling contributes to a myriad of neural disorders, including schizophrenia. While synaptic levels are intensely studied, nonvesicular release mechanisms, including cystine–glutamate exchange, maintain high steady-state glutamate levels in the extrasynaptic space. The existence of extrasynaptic receptors, including metabotropic group II glutamate receptors (mGluR), pose nonvesicular release mechanisms as unrecognized targets capable of contributing to pathological glutamate signaling. We tested the hypothesis that activation of cystine–glutamate antiporters using the cysteine prodrug N-acetylcysteine would blunt psychotomimetic effects in the rodent phencyclidine (PCP) model of schizophrenia. First, we demonstrate that PCP elevates extracellular glutamate in the prefrontal cortex, an effect that is blocked by N-acetylcysteine pretreatment. To determine the relevance of the above finding, we assessed social interaction and found that N-acetylcysteine reverses social withdrawal produced by repeated PCP. In a separate paradigm, acute PCP resulted in working memory deficits assessed using a discrete trial t-maze task, and this effect was also reversed by N-acetylcysteine pretreatment. The capacity of N-acetylcysteine to restore working memory was blocked by infusion of the cystine–glutamate antiporter inhibitor (S)-4-carboxyphenylglycine into the prefrontal cortex or systemic administration of the group II mGluR antagonist LY341495 indicating that the effects of N-acetylcysteine requires cystine–glutamate exchange and group II mGluR activation. Finally, protein levels from postmortem tissue obtained from schizophrenic patients revealed significant changes in the level of xCT, the active subunit for cystine–glutamate exchange, in the dorsolateral prefrontal cortex. These data advance cystine–glutamate antiporters as novel targets capable of reversing the psychotomimetic effects of PCP

Non-stationary covariance function modelling in 2D least-squares collocation

Standard least-squares collocation (LSC) assumes 2D stationarity and 3D isotropy, and relies on a covariance function to account for spatial dependence in the ob-served data. However, the assumption that the spatial dependence is constant through-out the region of interest may sometimes be violated. Assuming a stationary covariance structure can result in over-smoothing of, e.g., the gravity field in mountains and under-smoothing in great plains. We introduce the kernel convolution method from spatial statistics for non-stationary covariance structures, and demonstrate its advantage fordealing with non-stationarity in geodetic data. We then compared stationary and non-stationary covariance functions in 2D LSC to the empirical example of gravity anomaly interpolation near the Darling Fault, Western Australia, where the field is anisotropic and non-stationary. The results with non-stationary covariance functions are better than standard LSC in terms of formal errors and cross-validation against data not used in the interpolation, demonstrating that the use of non-stationary covariance functions can improve upon standard (stationary) LSC