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We use gravity implied by the Earth's rock‐equivalent topography (RET) and modeled isostatic compensation masses to evaluate the new global gravity field models (GGMs) from European Space Agency (ESA)'s Gravity Field and Steady‐State Ocean Circulation Explorer (GOCE) satellite gravimetry mission. The topography is now reasonably well‐known over most of the Earth's landmasses, and also where conventional GGM evaluation is prohibitive due to the lack (or unavailability) of ground‐truth gravity data. We construct a spherical harmonic representation of Earth's RET to derive band‐limited topography‐implied gravity, and test the somewhat simplistic Airy/Heiskanen and Pratt/Hayford hypotheses of isostatic compensation, but which did not improve the agreement between gravity from the uncompensated RET and GOCE. The third‐generation GOCE GGMs (based on 12 months of space gravimetry) resolve the Earth's gravity field effectively up to spherical harmonic degree ∼200–220 (∼90–100 km resolution). Such scales could not be resolved from satellites before GOCE. From the three different GOCE processing philosophies currently in use by ESA, the time‐wise and direct approaches exhibit the highest sensitivity to short‐scale gravity recovery, being better than the space‐wise approach. Our topography‐implied gravity comparisons bring evidence of improvements from GOCE to gravity field knowledge over the Himalayas, Africa, the Andes, Papua New Guinea and Antarctic regions. In attenuated form, GOCE captures topography‐implied gravity signals up to degree ∼250 (∼80 km resolution), suggesting that other signals (originating, e.g., from the crust‐mantle boundary and buried loads) are captured as well, which might now improve our knowledge on the Earth's lithosphere structure at previously unresolved spatial scales. Key Points New‐generation GOCE models are evaluated from topo/isostatic gravity GOCE effectively resolves the gravity field to degree 200, attenuated to 250 Direct evidence of improvements over Africa, Himalayas, S America, and Antarctica

Using geodetic and oceanographic data, we show that the apparent north‐south slope between the Australian Height Datum (AHD) and the geoid is caused almost completely by the ocean's time‐mean dynamic topography (MDT). This is because the AHD was constrained to zero height at local mean sea level at multiple tide gauges around the Australian continent. Using MDT models and corrected leveling data, almost all of the apparent north‐south slope can be removed from the AHD. An auxiliary observation is that a satellite‐only MDT model based on only around one year of GOCE data generates results commensurate with geodetic, oceanographic and combined MDT models. Key Points MDT explains north‐south tilt in AHD The apparent sea level slope problem in Australia is solved GOCE‐based MDT models show promise

Cognitive neuroimaging studies typically require fast whole brain image acquisition with maximal sensitivity to small BOLD signal changes. To increase the sensitivity, higher field strengths are often employed, since they provide an increased image signal-to-noise ratio (SNR). However, as image SNR increases, the relative contribution of physiological noise to the total time series noise will be greater compared to that from thermal noise. At 7 T, we studied how the physiological noise contribution can be best reduced for EPI time series acquired at three different spatial resolutions (1.1 mm × 1.1 mm × 1.8 mm, 2 mm × 2 mm × 2 mm and 3 mm × 3 mm × 3 mm). Applying optimal physiological noise correction methods improved temporal SNR (tSNR) and increased the numbers of significantly activated voxels in fMRI visual activation studies for all sets of acquisition parameters. The most dramatic results were achieved for the lowest spatial resolution, an acquisition parameter combination commonly used in cognitive neuroimaging which requires high functional sensitivity and temporal resolution (i.e. 3mm isotropic resolution and whole brain image repetition time of 2s). For this data, physiological noise models based on cardio-respiratory information improved tSNR by approximately 25% in the visual cortex and 35% sub-cortically. When the time series were additionally corrected for the residual effects of head motion after retrospective realignment, the tSNR was increased by around 58% in the visual cortex and 71% sub-cortically, exceeding tSNR ~140. In conclusion, optimal physiological noise correction at 7 T increases tSNR significantly, resulting in the highest tSNR per unit time published so far. This tSNR improvement translates into a significant increase in BOLD sensitivity, facilitating the study of even subtle BOLD responses.

Earth observation (EO) satellites facilitate hazard monitoring and mapping over large-scale and remote areas. Despite Synthetic Aperture Radar (SAR) satellites being well-documented as a hazard monitoring tool, the uptake of these data is geographically variable, with the Australian continent being one example where the use of SAR data is limited. Consequently, less is known about how these data apply in the Australian context, how they could aid national hazard monitoring and assessment, and what new insights could be gleaned for the benefit of the international disaster risk reduction community. The European Space Agency Sentinel-1 satellite mission now provides the first spatially and temporally complete global SAR dataset and the first opportunity to use these data to systematically assess hazards in new locations. Using the example of Australia, where floods and uncontrolled bushfires, earthquakes, resource extraction (groundwater, mining, hydrocarbons) and geomorphological changes each pose potential risks to communities, we review past usage of EO for hazard monitoring and present a suite of new case studies that demonstrate the potential added benefits of SAR. The outcomes provide a baseline understanding of the potential role of SAR in national hazard monitoring and assessment in an Australian context. Future opportunities to improve national hazard identification will arise from: new SAR sensing capabilities, which for Australia includes a first-ever civilian EO capability, NovaSAR-1; the integration of Sentinel-1 SAR with other EO datasets; and the provision of standardised SAR products via Analysis Ready Data and Open Data Cubes to support operational applications.

After more than 6 years in full operational mode, the Gravity Recovery and Climate Experiment (GRACE) satellite mission provides the opportunity to derive global secular mass changes from space‐geodetic observations. Crucial for a reliable estimate of secular mass changes is the ability to correct for spectral and spatial leakage effects. In order to account for any leakage signal, we present and apply a four‐step procedure, including a validation step based on forward gravity modeling. Most notably, our method is characterized by the separation and quantification of individual leakage sources. We test and apply our procedure to the Greenland area, which exhibits the strongest secular trend signal. On the basis of simulation studies, we demonstrate that leakage‐out effects are dominant for the Greenland area, and if not accounted for, mass‐change rates will be underestimated. Analyzing time‐variable GRACE gravity fields covering 6 whole years (August 2002 to July 2008, inclusive), we estimate the ice‐volume loss over Greenland to be −177 ± 12 km3 a−1. This value is the average derived from monthly gravity field models provided by CSR, GFZ and JPL, with individual contributions of −242 ± 14 km3 a−1, −194 ± 24 km3 a−1 and −96 ± 23 km3 a−1, respectively. We highlight that without taking leakage effects into account, mass‐change amplitudes over Greenland are reduced by a factor of 2. Despite the rather large spread of the results among GRACE processing centers, our results are in better agreement with the findings from alternative GRACE analysis methods and InSAR observations.

Past ground-based geodetic measurements in the Perth Basin, Australia, record small-magnitude subsidence (up to 7 mm/y), but are limited to discrete points or traverses across parts of the metropolitan area. Here, we investigate deformation over a much larger region by performing the first application of Sentinel-1A InSAR data to Australia. The duration of the study is short (0.7 y), as dictated by the availability of Sentinel-1A data. Despite this limited observation period, verification of Sentinel-1A with continuous GPS and independent TerraSAR-X provides new insights into the deformation field of the Perth Basin. The displacements recorded by each satellite are in agreement, identifying broad (>5 km wide) areas of subsidence at rates up to 15 mm/y. Subsidence at rates greater than 20 mm/y over smaller regions (∼2 km wide) is coincident with wetland areas, where displacements are temporally correlated with changes in groundwater levels in the unconfined aquifer. Longer InSAR time series are required to determine whether these measured displacements are representative of long-term deformation or (more likely) seasonal variations. However, the agreement between datasets demonstrates the ability of Sentinel-1A to detect small-magnitude deformation over different spatial scales (from 2 km–10 s of km) in the Perth Basin. We suggest that, even over short time periods, these data are useful as a reconnaissance tool to identify regions for subsequent targeted studies, particularly given the large swath size of radar acquisitions, which facilitates analysis of a broader portion of the deformation field than ground-based methods or single scenes of TerraSAR-X.

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 of 10 cm 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 estimates improve EGM2008 height anomaly differences by 10 cm 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 these findings 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, RTM omission error estimates will allow refined validation of global gravity field models like EGM2008 from GPS/levelling data.

Over the past decade, numerous advantages of a gravimetric geoid model and its possible suitability for the Indian national vertical datum have been discussed and advocated by the Indian scientific community and national geodetic agencies. However, despite several regional efforts, a state-of-the-art gravimetric geoid model for the whole of India remains elusive due to a multitude of reasons. India encompasses one of the most diverse topographies on the planet, which includes the Gangetic plains, the Himalayas, the Thar desert, and a long peninsular coastline, among other topographic features. In the present study, we have developed the first national geoid and quasigeoid models for India using Curtin University's approach. Terrain corrections were found to reach an extreme of 187 mGal, Faye gravity anomalies 617 mGal, and the geoid-quasigeoid separation 4.002 m. We have computed both geoid and quasigeoid models to analyse their representativeness of the Indian normal-orthometric heights from the 119 GNSS-levelling points that are available to us. A geoid model for India has been computed with an overall standard deviation of ±0.396 m but varying from ±0.03 to ±0.158 m in four test regions with GNSS-levelling data. The greatest challenge in developing a precise gravimetric geoid for the whole of India is data availability and its preparation. More densely surveyed precise gravity data and a larger number of GNSS/levelling data are required to further improve the models and their testing.

We provide a historical review and critique of regional geoid and quasigeoid modelling over the Australian continent, covering the earliest models from the late 1960s through to the present day and beyond. The most recently released official model for GPS/GNSS surveyors, AUSGeoid2020, was specifically calculated to enable them to determine Australian Height Datum heights from Geocentric Datum of Australia 2020 ellipsoidal heights in a more direct manner without the need for post-surveying adjustments. We summarise the deficiencies in the Australian Height Datum and how they are now being addressed by a proposed new vertical height system that is underpinned by a gravimetric-only quasigeoid model. We also summarise the results of some experiments that we have conducted to explore potential refinements that could be made to our computational processes, and future plans to acquire gravity data in the problematic coastal zones using airborne methods.

The contingent negative variation (CNV) is a long-latency electroencephalography (EEG) surface negative potential with cognitive and motor components, observed during response anticipation. CNV is an index of cortical arousal during orienting and attention, yet its functional neuroanatomical basis is poorly understood. We used functional magnetic resonance imaging (fMRI) with simultaneous EEG and recording of galvanic skin response (GSR) to investigate CNV-related central neural activity and its relationship to peripheral autonomic arousal. In a group analysis, blood oxygenation level dependent (BOLD) activity during the period of CNV generation was enhanced in thalamus, somatomotor cortex, bilateral midcingulate, supplementary motor, and insular cortices. Enhancement of CNV-related activity in anterior and midcingulate, SMA, and insular cortices was associated with decreases in peripheral sympathetic arousal. In a subset of subjects in whom we acquired simultaneous EEG and fMRI data, we observed activity in bilateral thalamus, anterior cingulate, and supplementary motor cortex that was modulated by trial-by-trial amplitude of CNV. These findings provide a likely functional neuroanatomical substrate for the CNV and demonstrate modulation of components of this neural circuitry by peripheral autonomic arousal. Moreover, these data suggest a mechanistic model whereby thalamocortical interactions regulate CNV amplitude.