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• HF & VHF radars, making measurements of resident space objects, detect Doppler oscillations. • The Doppler oscillations are interpreted as waves propagating in the ionosphere. • Statistical analysis shows the waves have a frequency distribution in the 0.2–1 Hz range. • The waves are most likely observed during day with higher frequencies in weaker ionospheres. • The wave frequency and occurrence distributions suggest the most likely waves are EMIC waves. Radars performing observations of resident space objects (RSO) measure Doppler variations in wave events of several minutes duration, with frequencies in the 0.2–1 Hz range peaking near 0.5–0.7 Hz, consistent with variations in electron density induced by waves in the intervening ionosphere. The two mid-latitude radars used were a Very High Frequency (VHF) radar operating at 55 MHz, and a High Frequency (HF) radar operating at 30 MHz, both in southern Australia. The VHF radar wave observations exibited a peak in wave occurrence in the post-dawn sector (0600–1200 local solar time). The seasonal occurrence of the waves had a strong minimum during winter compared with the other seasons. Comparison between observations in 2018/19 (near solar minimum) and 2021/22 (mid-rise to cycle 25 peak) suggests wave occurrence is anti-correlated with sunspot number and hence with EUV and ionospheric strength. Generally the waves had higher frequencies during night than day, and low sunspot number than mid solar cycle, when there was a weaker ionosphere. Given the oscillation frequency of the wave events, the most likely geophysical phenomena that is consistent with the observations are electro-magnetic ion cyclotron (EMIC) plasma waves in the Pc1 (0.2–5 Hz) frequency range. No other candidate geophysical disturbance appears to fit the wave characteristics. The majority of geomagnetic field-guided transverse Pc1 EMICWs project from the outer magnetosphere down onto the polar ionosphere, where they can convert to compressional fast-mode waves propagating parallel to the Earths surface in the ionospheric F2 layer waveguide, both equatorwards to mid-latitudes and polewards. It is likely the majority of the Doppler oscillations in the Pc1 frequency range observed by the radars at mid-latitudes are these ducted compressional waves. However, sources of transverse EMICWs from the magnetosphere onto the mid-latitude ionosphere do exist, and these may cause some of the observed oscillations. The micro-physics of these compressional waves causing Doppler oscillations in radio observations is not inconsistent with the history of ionospheric Doppler measurements and theory, although the radar trans-ionospheric radio propagation and the observed waves being higher frequency than previous studies is different. If the observed Doppler oscillations are compressional EMICW in the ionospheric waveguide then several of the statistical results can be explained. The anti-correlation of the wave occurrence with sunspot number (and resultant ionospheric strength) can be attributed to lower ionospheric attenuation at the higher latitudes, between where geomagnetically field-guided transverse EMIC waves initially enter the high-latitude ionospheric waveguide from the magnetosphere above, and their observation at mid-latitudes. The observation of higher frequency waves during low sunspot number may also be explained by a source effect in the magnetosphere that preferentially selects the higher frequency field-guided transverse EMIC waves to propagate down to the ionosphere. Comparisons will be shown with results from ground and space based magnetometers of compressional waves in the waveguide, highlighting consistent results and areas where the measurement techniques differ. Theory suggests the radar Doppler measurements are far more sensitive to variations in in-situ ionospheric electron density than magnetic field variations. This agrees with existing literature which highlights the very strong contribution from the compressional component. Theory also suggests that the Doppler sensitivity of a radar to ionospheric electron density variations is frequency dependent, with lower frequencies being more sensitive. This is borne out by the measurements, with the HF radar being more sensitive than the VHF radar. [ABSTRACT FROM AUTHOR]

We present an integrated framework that leverages multiple weather radar calibration and monitoring techniques to provide real-time diagnostics on reflectivity calibration, antenna pointing, and dual-polarization moments. This framework uses a volume-matching technique to track the absolute calibration of radar reflectivity with respect to the Global Precipitation Measurement (GPM) spaceborne radar, the relative calibration adjustment (RCA) technique to track relative changes in the radar calibration constant, the solar calibration technique to track daily change in solar power and antenna pointing error, and techniques that track properties of light-rain medium to monitor the differential reflectivity and dual-polarization moments. This framework allows for an evaluation of various calibration and monitoring techniques. For example, we found that a change in the RCA is highly correlated to a change in absolute calibration, with respect to GPM, if a change in antenna pointing can first be ruled out. It is currently monitoring 67+ radars from the Australian radar network. Because of the diverse and evolving nature of the Australian radar network, flexibility and modularity are at the core of the calibration framework. The framework can tailor its diagnostics to the specific characteristics of a radar (band, beamwidth, etc.). Because of its modularity, it can be expanded with new techniques to provide additional diagnostics (e.g., monitoring of radar sensitivity). The results are presented in an interactive dashboard at different level of details for a wide and diverse audience (radar engineers, researchers, forecasters, and management), and it is operational at the Australian Bureau of Meteorology. Significance Statement: Weather radars, like all instruments, require maintenance and upgrades. Rainfall measurements are highly variable and sensitive to change, and this can lead to inconsistencies within a radar network. Calibration is the process to counteract those inconsistencies. Any calibration requires a fixed standard to which the changed/upgraded radar can be compared. The SCAR calibration framework presented herein makes use of several standards to retrieve a full set of diagnostics about the radar data. We apply these techniques over the entire Australian weather radar network and demonstrate that, by using this integrated approach, absolute calibration can be achieved to within 1 dBZ of reflectivity, antenna pointing can be monitored within 0.1°, and the various measurements of the radars can be quality controlled. [ABSTRACT FROM AUTHOR]

Weather radars are increasingly being used to study the interaction between wildfires and the atmosphere, owing to the enhanced spatio-temporal resolution of radar data compared to conventional measurements, such as satellite imagery and in situ sensing. An important requirement for the continued proliferation of radar data for this application is the automatic identification of fire-generated particle returns (pyrometeors) from a scene containing a diverse range of echo sources, including clear air, ground and sea clutter, and precipitation. The classification of such particles is a challenging problem for common image segmentation approaches (e.g. fuzzy logic or unsupervised machine learning) due to the strong overlap in radar variable distributions between each echo type. Here, we propose the following two-step method to address these challenges: (1) the introduction of secondary, texture-based fields, calculated using statistical properties of gray-level co-occurrence matrices (GLCMs), and (2) a Gaussian mixture model (GMM), used to classify echo sources by combining radar variables with texture-based fields from (1). Importantly, we retain all information from the original measurements by performing calculations in the radar's native spherical coordinate system and introduce a range-varying-window methodology for our GLCM calculations to avoid range-dependent biases. We show that our method can accurately classify pyrometeors' plumes, clear air, sea clutter, and precipitation using radar data from recent wildfire events in Australia and find that the contrast of the radar correlation coefficient is the most skilful variable for the classification. The technique we propose enables the automated detection of pyrometeors' plumes from operational weather radar networks, which may be used by fire agencies for emergency management purposes or by scientists for case study analyses or historical-event identification. [ABSTRACT FROM AUTHOR]

Background: Prevention and early intervention of alcohol use disorder (AUD) is a public health priority, yet there are gaps in our understanding of how AUD emerges, which symptoms of AUD come first, and whether there are modifiable risk factors that forecast the development of the disorder. This study investigated potential early‐warning‐sign symptoms for the development of AUD. Methods: Data were from the RADAR study, a prospective cohort study of contemporary emerging adults across Australia (n = 565, mean age = 18.9, range = 18–21 at baseline, 48% female). Participants were interviewed five times across a 2.5‐year period. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM‐5) AUD criteria and diagnoses were assessed by clinical psychologists using the Structured Clinical Interview for DSM‐IV (SCID‐IV), modified to cover DSM‐5 criteria. Hazard analyses modeled the time from first alcoholic drink to the emergence of any AUD criteria and determined which first‐emergent AUD criteria were associated with a faster transition to disorder. Results: By the final time point, 54.8% of the sample had experienced at least one DSM‐5 AUD criterion and 26.1% met criteria for DSM‐5 AUD. The median time from first AUD criterion to a diagnosis of AUD was 4 years. Social problems from drinking (hazard ratio [HR] = 3.24, CI95 = 2.14, 4.92, p < 0.001), major role (HR = 2.53, CI95 = 1.58, 4.06, p < 0.001), and drinking larger amounts/for longer than intended (HR = 2.04, CI95 = 1.20, 3.46, p = 0.008) were first‐onset criteria associated with a faster transition to AUD. Conclusion: In the context of a prospective general population cohort study of the temporal development of AUD, alcohol‐related social problems, major role problems, and using more or for longer than intended are key risk factors that may be targeted for early intervention. [ABSTRACT FROM AUTHOR]

In the classical model of mesoscale convective systems (MCSs), a system generates new convective cells on the downshear side of its cold pool, with the cells fed at low levels from the front, and the stratiform cloud trailing behind the system in the upshear direction, where "front" and "behind" typically refer to the system's ground-relative velocity. In this study we present an algorithm for identifying and tracking MCSs in radar reflectivity data, and objectively diagnosing organizational characteristics related to the classical model, namely, the offset of stratiform cloud from convective cloud relative to system velocity, the low-level inflow direction, and the shear-relative tilt and propagation directions. When applied to the 15-yr radar record covering the Darwin region of northern Australia, the algorithm indicates that 65%–80% of MCS observations are consistent with the classical model, at least when the four classifications can be made unambiguously. However, these observed characteristics occur almost entirely in the drier phases of the Australian monsoon. During the humid, active monsoon phase, observed characteristics consistent with the classical model are rare, and most systems exhibit nonclassical upshear propagation. Significance Statement: In this study we developed a computer program for tracking large storms in radar data. The program tracks storms through time and space, recording their three-dimensional structure. Knowledge of this structure allows our program to automatically put storms into different, commonly used categories. These categories provide clues about the processes that can make storms grow, persist, or decay. Our computer program is useful because the classification process is very time consuming when done by humans. Also, when applied to radar datasets from northern Australia, our program suggests most large storms are consistent with standard theories of what make storms grow and persist. However, when the air is very humid, large storms inconsistent with standard theories become common. [ABSTRACT FROM AUTHOR]

Background: Wire localisation (WL) is the "gold standard" localisation technique for wide local excision (WLE) of non-palpable breast lesions but has disadvantages that have led to the development of wireless techniques. This study compared the cost-effectiveness of radar localisation (RL) to WL., Methods: This was a single-institution study of 110 prospective patients with early-stage breast cancer undergoing WLE using RL with the SCOUT ® Surgical Guidance System (2021-2023) compared with a cohort of 110 patients using WL. Margin status, re-excision rates, and surgery delays associated with preoperative localisation were compared. Costs from a third-party payer perspective in Australian dollars (AUD$) calculated by using microcosting, break-even point, and cost-utility analyses., Results: A total of 110 WLEs using RL cost a total of AUD$402,281, in addition to the device cost of AUD$77,150. The average additional cost of a surgery delay was AUD$2318. Use of RL reduced the surgery delay rate by 10% (p = 0.029), preventing 11 delays with cost savings of AUD$25,496. No differences were identified in positive margin rates (RL: 11.8% vs. WL: 17.3%, p = 0.25) or re-excision rates (RL: 14.5% vs. WL: 21.8%, p = 0.221). In total, 290 RL cases are needed to break even. The cost of WLE using RL was greater than WL by AUD$567. There was a greater clinical benefit of 1.15 quality-adjusted life-years (QALYs) and an incremental cost-utility ratio of AUD$493 per QALY favouring RL., Conclusions: Routine use of RL was a more cost-effective intervention than WL. Close to 300 RL cases are likely needed to be performed to recover costs of the medical device., Clinical Trial Registration: ACTRN12624000068561., (© 2024. The Author(s).)

Warner, A.L.; Hesp, P.A.; Keane, R., and Gontz, A.M., 2022. Ground penetrating radar of paleoblowouts and transgressive dunes, Younghusband Peninsula, South Australia. Journal of Coastal Research, 38(5), 885–895. Coconut Creek (Florida), ISSN 0749-0208. Ground penetrating radar (GPR) can be used to elucidate the spatial and temporal relationships of paleoshorelines and dunefield evolution and aid in understanding past landscapes in order to help predict future evolutionary pathways of barrier development. GPR was used on the Younghusband Peninsula coast of Southern Australia at a location known as 42 Mile Crossing. The barrier is on the longest beach in Australia and hosts a Holocene transgressive dunefield system. The site also contains many active, modern, and stabilized blowouts. This study aimed to image buried (paleo) blowouts, analyze the subsurface strata of the area, and attempt to relate these findings to the past transgressive dunefield processes, phases, and architecture. The study was able to characterize the buried blowout's shape, size, and dominant wind direction, and the site's stratigraphic evolution. [ABSTRACT FROM AUTHOR]

There is a long history of coastal erosion caused by frequent storm surges in the coastal regions of Australia, which imposes great threats to communities and infrastructures alongside the beach. Old Bar Beach, New South Wales, Australia, is one such hotspot famous for its extreme coastal erosion. To apply remedial measures such as beach nourishment effectively and economically, estimating/reconstructing the subsurface hydrogeology over the coastal areas is essential. A geophysical tool such as a ground-penetrating radar (GPR) which works on the principle of reflecting electromagnetic (EM) waves, can be conveniently deployed to delineate the soil and rock profiling, water-table depth, bedrock depth, and the subsurface structural features. Here, DeepLabv3+ architecture based newly developed deep convolutional neural networks (DCNNs) were used to establish an inherent non-linear relationship between the GPR data and the EM wave velocity. The presented DCNNs have a lesser number of layers, a lesser number of trainable (learnable) parameters, a high convergence rate and, at the same time, achieve prediction accuracy comparable to that of well-established DeepLabv3+ networks, having high trainable parameters and a relatively low convergence rate. Here, firstly the DCNNs were trained and validated on small 1D datasets. Each dataset contains a 1D GPR trace and a corresponding EM velocity model. The DCNNs turned out to be quite promising in the 1D case, with training, validation, and testing accuracy of approximately 95%, 94%, and 95%, respectively. Secondly, 1D trained weights were applied to 2D synthetic GPR data for EM velocity prediction, and the accuracy of prediction achieved was approximately 95%. Seeing the excellent performance of the DCNNs in the 2D prediction case using 1D trained weights, a large amount of 1D synthetic datasets (approximately 1.2 million) were generated and gaussian noise was added to it to replicate the real field scenario. Thirdly, topographically corrected GPR data acquired over the Old Bar Beach were inverted using the DCNNs trained on 1.2 million 1D synthetic datasets to obtain the subsurface high-resolution, high-precision EM velocity, and ε r distribution information to understand the hydrogeology over the beach. The findings presented in this paper agree well with the previous hydrogeological studies carried out using GPR. Our findings show that DCNNs, along with GPR, can be successfully used in coastal environments for the quick and accurate hydrogeological investigation required for the implementation of coastal erosion mitigation methods such as beach nourishment. [ABSTRACT FROM AUTHOR]

In Australia, hailstorms present considerable public safety and economic risks, where they are considered the most damaging natural hazard in terms of annual insured losses. Despite these impacts, the current climatological distribution of hailfall across the continent is still comparatively poorly understood. This study aims to supplement previous national hail climatologies, such as those based on environmental proxies or satellite radiometer data, with more direct radar-based hail observations. The heterogeneous and incomplete nature of the Australian radar network complicates this task and prompts the introduction of some novel methodological elements. We introduce an empirical correction technique to account for hail reflectivity biases at C band, derived by comparing overlapping C- and S-band observations. Furthermore, we demonstrate how object-based hail swath analysis may be used to produce resolution-invariant hail frequencies, and describe an interpolation method used to create a spatially continuous hail climatology. The maximum estimated size of hail (MESH) parameter is then applied to a mixture of over 50 operational radars in the Australian radar archive, resulting in the first nationwide, radar-based hail climatology. The spatiotemporal distribution of hailstorms is examined, including their physical characteristics, seasonal and diurnal frequency, and regional variations of such properties across the continent. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) came into practical use as a geohazard monitoring tool in the 1990s. The uptake of this remote sensing technique however is geographically varying, and Australia has been one of many continents not fully utilizing the available InSAR data. As a result, limited knowledge exists on the application of InSAR data to obtain insights and benefits in geohazard risk monitoring and management. Due to the said limitation, this research carried out a concise technical background study and investigated a suite of case studies to demonstrate the potential for geohazard monitoring and assessment in Australia using InSAR. The differential InSAR workflow was applied to sites that experienced heaving and subsiding of the ground surface, mining, earthquake, landslides, and tunnelling in West Metro Melbourne (VIC), Musgrave (SA), Crinum mine (QLD), Pelham (TAS), Lake Muir (WA), and Sydney (NSW). The results of the Differential InSAR analysis resulted in acceptable ground movement and deformation estimates when compared with measured field data. Future opportunities related to new applications of the InSAR technique on geohazard reduction considering climate change contributions and probable coupling with artificial intelligence techniques were also discussed. This research has formed a conducive and scientific learning medium, which may encourage the growth of multidisciplinary research combining the fields of remote sensing and geotechnical engineering to investigate the impact of geohazards. [ABSTRACT FROM AUTHOR]

The sparse distribution of the observing network over the high latitudes of the Southern Hemisphere reduces the accuracy of regional atmospheric circulation (re)analysis and weather forecasting. Using operational medium‐range ensemble forecasts, we compared the forecast skill among forecast centres regarding a midlatitude cyclone close to western Australia on 17 December 2017. At forecast day 4.5, a large ensemble spread in an upper‐level trough over western Australia, which had travelled from coastal Antarctica, caused large uncertainty and error in the predicted position of the midlatitude cyclone. Using an ensemble data assimilation system and the Atmospheric General Circulation Model for the Earth Simulator and the Local Ensemble Transform Kalman Filter Experimental Ensemble Reanalysis version 2 (ALERA2), we conducted an operational forecast experiment to investigate the impact on ALERA2 and its forecast result of assimilation of 1‐hourly horizontal wind speed observational data from the PANSY radar located at the Japanese Syowa Station in Antarctica. An observing system experiment revealed that incorporation of PANSY radar data improved the reproducibility of atmospheric parameters in the troposphere and lower stratosphere as initial conditions for forecasting. To investigate the impact of PANSY radar data on forecast skill regarding the midlatitude cyclone, 63‐member ensemble forecast experiments were conducted using analysis data both with and without PANSY data as initial conditions. Comparison of the forecast results from the ensemble forecast experiments confirmed that incorporation of PANSY radar data reduced the ensemble spread and error in the upper troposphere over the Antarctic region as initial conditions, which enhanced the accuracy of the prediction of the positions of the upper‐level trough and surface cyclone over western Australia at forecast day 4.5. These experiments suggest that the PANSY radar represents an observing system suitable for continuous improvement in forecast skill of the atmospheric circulation in the Southern Hemisphere. [ABSTRACT FROM AUTHOR]

Data from an upward-pointing wind profiler radar pair at Darwin in tropical Australia are used to determine the characteristics of individual convective up- and downdrafts observed at the site. Drafts are identified as vertically contiguous regions of instantaneous upward or downward motion exceeding 0.2 m s−1. Most updrafts and downdrafts found are less than 2 km in vertical extent, and updrafts exceeding 5 km in vertical length carry no more than 33% of the total upward mass flux. Updraft length correlates positively with rain rates, and on very high rain rates (greater than 20 mm h−1), average updraft lengths are ~5 km. Typical peak updraft velocities increase from ~2.5 m s−1 for the smallest to ~4 m s−1 for the largest drafts, while those for downdrafts remain ~2 m s−1 regardless of size. These results are broadly consistent with other numerical modeling studies, but contrast with the common view of deep convection as being dominated by continuous, deep drafts. [ABSTRACT FROM AUTHOR]

Fractals are widely recognized as one of the best geometric models to depict soil roughness on various scales from tillage to micro-topography smaller than radar wavelength. However, most fractal approaches require an additional geometric description of experimental sites to be analysed by existing radiative transfer models. For example, fractal dimension or spectral parameter is often related to root-mean-square (RMS) height to be characterized as the microwave surface. However, field measurements hardly represent multi-scale roughness. In this study, we rescaled Power Spectral Density with Synthetic Aperture Radar (SAR)-inverted rms height, and estimated non-stationary fractal roughness to accommodate multi-scale roughness into a radiative transfer model structure. As a result, soil moisture was retrieved over the Yanco site in Australia. Local validation shows that the Integral Equation Model (IEM) poorly simulated backscatters using inverted roughness as compared to fractal roughness even in anisotropic conditions. This is considered due to a violation of time-invariance assumption used for inversion. Spatial analysis also shows that multi-scale fractal roughness better illustrated the hydrologically reasonable backscattering partitioning, as compared to inverted roughness. Fractal roughness showed a greater contribution of roughness to backscattering in dry conditions. Differences between IEM backscattering and measurement were lower, even when the isotropic assumption of the fractal model was violated. In wet conditions, the contribution of soil moisture to backscattering was shown more clearly by fractal roughness. These results suggest that the multi-scale fractal roughness can be better adapted to the IEM even in anisotropic conditions than the inversion to assume time-invariance of roughness. [ABSTRACT FROM AUTHOR]

Just about everyone suspects that the rush to enrol distance-learning students in on-campus degrees tends to disadvantage them. Monir Mir and Abu Shiraz Rahaman set out to test this intuition, using accounting students as their guinea-pigs. (Contains 6 figures and 5 tables.)

The impact of Doppler radar wind observations on forecasts from a developmental, high-resolution numerical weather prediction (NWP) system is assessed. The new 1.5-km limited-area model will be Australia's first such operational NWP system to include data assimilation. During development, the assimilation of radar wind observations was trialed over a 2-month period to approve the initial inclusion of these observations. Three trials were run: the first with no radar data, the second with radial wind observations from precipitation echoes, and the third with radial winds from both precipitation and insect echoes. The forecasts were verified against surface observations from automatic weather stations, against rainfall accumulations using fractions skill scores, and against satellite cloud observations. These methods encompassed verification across a range of vertical levels. Additionally, a case study was examined more closely. Overall results showed little statistical difference in skill between the trials, and the net impact was neutral. While the new observations clearly affected the forecast, the objective and subjective analyses showed a neutral impact on the forecast overall. As a first step, this result is satisfactory for the operational implementation. In future, upgrades to the radar network will start to reduce the observation error, and further improvements to the data assimilation are planned, which may be expected to improve the impact. [ABSTRACT FROM AUTHOR]

The dataset consists of ocean surface wind speed and direction at 10 m height and 1 km spatial resolution around the wider Australian coastal areas, spanning 4 years (2017 to 2021) of measurements from Sentinel-1 A and B imaging Synthetic Aperture Radar (SAR) platforms. The winds have been derived using a consistent SAR wind retrieval algorithm, processing the full Sentinel-1 archive in this region. The data are appropriately quality controlled, flagged, and archived as NetCDF files representing SAR wind field maps aligned with satellite along-track direction. The data have been calibrated against Metop-A/B Scatterometer buoy-calibrated, wind measurements and examined for potential changes in calibration over the duration of the data. The calibrated data are further validated by comparisons against independent Altimeter (Cryosat-2, Jason-2, Jason-3, and SARAL) wind speeds. Several methods for data access are also listed. The database is potentially useful for offshore industries (oil and gas, fisheries, shipping, offshore wind energy), public recreational activities (fishing, sailing, surfing), and protection and management of coasts and natural habitats. [ABSTRACT FROM AUTHOR]

Carrying a laser Doppler instrument, the Aeolus satellite was launched in 2018, becoming the first mission for atmospheric wind profile measurements from space. Before utilizing the Aeolus winds for different applications, evaluating their data quality is essential. With the help of ground-based wind profiling radar measurements and the European Centre for Medium-Range Weather Forecasts (ECMWF) model equivalents, this study quantifies the error characteristics of Aeolus L2B (baseline-11) near-real-time horizontal line-of-sight winds across Australia during October 2020–March 2021 by using both inter-comparison and triple collocation analysis. The results of the inter-comparison analysis indicate that both Rayleigh-clear winds and Mie-cloudy winds are in good agreement with the ground-based radar measurements with overall absolute mean biases smaller than 0.7 m s -1 and correlation coefficients larger than or equal to 0.9. Moreover, assuming the radar measurements as the reference data set, Mie-cloudy winds are shown to be more precise than Rayleigh-clear winds with an overall random error of 4.14 and 5.81 m s -1 , respectively. Similar results were also found from triple collocation analysis, with error standard deviations of 5.61 and 3.50 m s -1 for Rayleigh-clear winds and Mie-cloudy winds. In addition, the Mie channel is shown to be more capable of capturing the wind in the planetary boundary layer (< 1500 m). The findings of this study demonstrate the good performance of space-borne Doppler lidar for wind profiling and provide valuable information for data assimilation in numerical weather prediction. [ABSTRACT FROM AUTHOR]

Hail damage is a leading cause of insured losses in Australia, but changes in this hazard have not been robustly quantified. Here, we provide a continental-scale analysis of changes in hail hazard in Australia. A hail proxy applied to reanalysis data shows that from 1979–2021 annual hail-prone days decreased over much of Australia but increased in some heavily populated areas. For example, the annual number of hail-prone days increased by ~40% around Sydney and Perth, the largest cities on Australia's east and west coasts, respectively. Changes in atmospheric instability have driven the trends. Radar observations, while covering shorter time spans and a more limited area than the reanalysis, corroborate the broad pattern of results. This study shows consistent hail-frequency trends in radar indicators and atmospheric environments and demonstrates substantial increases in hail frequency in major Australian cities where hail impacts are most significant. [ABSTRACT FROM AUTHOR]

This study will highlight the diagnostic potential that radar plots display for reporting on performance benchmarking from patient admissions to hospital for surgical procedures. Two drawbacks of radar plots - the presence of missing information and ordering of indicators - are addressed. Ten different orthopaedic surgery procedures were considered in this study. Moreover, twelve outcome indicators were provided for each of the 10 surgeries of interest. These indicators were displayed using a radar plot, which we call a scorecard. At the hospital level, we propose a facile process by which to consolidate our 10 scorecards into one. We addressed the ordering of indicators in our scorecards by considering the national median of the indicators as a benchmark. Furthermore, our the consolidated scorecard facilitates concise visualisation and dissemination of complex data. It also enables the classification of providers into potential low and high performers that warrant further investigation. In conclusion, radar plots provide a clear and effective comparative tool for discerning multiple outcome indicators against the benchmarks of patient admission. A case study between two top and bottom performers on a consolidated scorecard (at hospital level) showed that medical provider charges varied more than other outcome indicators.

This paper assesses the ability of a recently installed 55 MHz multistatic meteor radar to measure gravity-wave-driven momentum fluxes around the mesopause and applies it in a case study of measuring gravity wave forcing on the diurnal tide during a period following the autumnal equinox of 2018. The radar considered is in the vicinity of Adelaide, South Australia (34.9 ∘ S, 138.6 ∘ E), and consists of a monostatic radar and bistatic receiver separated by approximately 55 km. The assessment shows that the inclusion of the bistatic receiver reduces the relative uncertainty of the momentum flux estimate from about 75 % to 65 % (for a flux magnitude of ∼20 m 2 s -2 , 1 d's worth of integration, and for a gravity wave field synthesized from a realistic spectral model). This increase in precision appears to be entirely attributable to the increased number of meteor detections associated with the combined monostatic and bistatic receivers rather than changes in the meteors' spatial distribution. The case study reveals large modulations in the diurnal tidal amplitudes, with a maximum tidal amplitude of ∼50 m s -1 and an associated maximum zonal wind velocity of around 140 m s -1. While the observed gravity wave forcing exhibits a complex relationship with the tidal winds during this period, the components of the forcing are seen to be approximately out of phase with the tidal winds above 88 km. No clear phase relationship has been observed below 88 km. [ABSTRACT FROM AUTHOR]

Regional understanding of severe surface winds produced by convective processes [severe convective winds (SCWs)] is important for decision-making in several areas of society, including weather forecasting and engineering design. Meteorological studies have demonstrated that SCWs can occur due to a number of different mesoscale and microscale processes, in a range of large-scale atmospheric environments. However, long-term observational studies of SCW characteristics often have not considered this diversity in physical processes, particularly in Australia. Here, a statistical clustering method is used to separate a large dataset of SCW events, measured by automatic weather stations around Australia, into three types, associated with strong background wind, steep lapse rate, and high moisture environments. These different types of SCWs are shown to have different seasonal and spatial variations in their occurrence, as well as different measured wind gust, lightning, and parent-storm characteristics. In addition, various convective diagnostics are tested in their ability to discriminate between measured SCW events and nonsevere events, with significant variations in skill between event types. Differences in environmental conditions and wind gust characteristics between event types suggests potentially different physical processes for SCW production. These findings are intended to improve regional understanding of severe wind characteristics, as well as environmental prediction of SCWs in weather and climate applications, by considering different event types. Significance Statement: The purpose of this study is to improve regional understanding of different types of severe wind events in Australia, specifically those associated with atmospheric convection. We did this by constructing a dataset of 413 severe convective wind events, using weather station and radar data within 20 regions around Australia. We then split those events into three different types, based on the environmental conditions that they occur within. We found that each event type tends to occur at different times of the year and in different regions, while also having different wind gust and lightning characteristics. In addition, the atmospheric conditions that are helpful for prediction of severe wind events differs between each type. These results are intended to be useful for prediction of severe wind events associated with convection and assessing their variability, characteristics, and impacts, in both weather forecasting and climate analysis. [ABSTRACT FROM AUTHOR]

Using ground penetrating radar (GPR) we investigate the near surface (~0–10 m depth) geophysical structure of neotectonic fault-propagation folds and thrust faults in south-central Australia in varying stages of fold and fault growth. Variations in neotectonic fold scarp heights are interpreted to reflect variations in accumulated slip on the underlying reverse faults. Fold scarps on the Nullarbor and Roe Plains are characterized by broad, asymmetric morphologies with vertical displacements of ~5 to ~40 m distributed over 1 to 2 km widths (~0.5 to ~4 m per 100 m). Within increasing scarp height there is an increase in the frequency and spatial density of strong reflector packages in the hanging wall that are attributed to material contrasts imposed by co-seismic fracturing and associated lithological and weathering variations. No evidence for discrete faulting is found at scarp heights up to 40 m (maximum relief of 4 m per 100 m). Where the principal slip zone of a fault ruptures to the surface, scarp morphologies are characterized by steep gradients (ca. 10 m per 100 m). Discrete faulting is imaged in GPR as structural lineaments, abrupt changes in the thickness of reflector packages with variations of amplitude, and/or hyperbolic diffraction packages indicative of the disturbance of reflector packages. Geophysical imaging of subtle changes in the shallow geological structure during growth of fault-propagation folds can be conducted using GPR informing the identification of locations for invasive investigations (e.g., trenching). [ABSTRACT FROM AUTHOR]

The precise information on fuel characteristics is essential for wildfire modelling and management. Satellite remote sensing can provide accurate and timely measurements of fuel characteristics. However, current estimates of fuel load changes from optical remote sensing are obstructed by seasonal cloud cover that limits their continuous assessments. This study utilises remotely sensed Synthetic-Aperture Radar (SAR) (Sentinel-1 backscatter) data as an alternative to optical-based imaging (Sentinel-2 scaled surface reflectance). SAR can penetrate clouds and offers high-spatial and medium-temporal resolution datasets and can hence complement the optical dataset. Inspired by the optical-based Vegetation Structural Perpendicular Index (VSPI), an SAR-based index termed RADAR-VSPI (R-VSPI) is introduced in this study. R-VSPI characterises the spatio-temporal changes in fuel load due to wildfire and the subsequent vegetation recovery thereof. The R-VSPI utilises SAR backscatter (σ°) from the co-polarized (VV) and cross-polarized (VH) channels at a centre frequency of 5.4 GHz. The newly developed index is applied over major wildfire events that occurred during the "Black Summer" wildfire season (2019–2020) in southern Australia. The condition of the fuel load was mapped every 5 (any orbit) to 12 (same orbit) days at an aggregated spatial resolution of 110 m. The results show that R-VSPI was able to quantify fuel depletion by wildfire (relative to healthy vegetation) and monitor its subsequent post-fire recovery. The information on fuel condition and heterogeneity improved at high-resolution by adapting the VSPI on a dual-polarization SAR dataset (R-VSPI) compared to the historic forest fuel characterisation methods (that used visible and infrared bands only for fuel estimations). The R-VSPI thus provides a complementary source of information on fuel load changes in a forest landscape compared to the optical-based VSPI, in particular when optical observations are not available due to cloud cover. [ABSTRACT FROM AUTHOR]

This paper evaluates the opportunity provided by global interferometric radar datasets for monitoring deforestation, degradation and forest regrowth in tropical and semi-arid environments. The paper describes an easy to implement method for detecting forest spatial changes and estimating their magnitude. The datasets were acquired within space-borne high spatial resolutions radar missions at near-global scales thus being significant for monitoring systems developed under the United Framework Convention on Climate Change (UNFCCC). The approach presented in this paper was tested in two areas located in Indonesia and Australia. Forest change estimation was based on differences between a reference dataset acquired in February 2000 by the Shuttle Radar Topography Mission (SRTM) and TanDEM-X mission (TDM) datasets acquired in 2011 and 2013. The synergy between SRTM and TDM datasets allowed not only identifying changes in forest extent but also estimating their magnitude with respect to the reference through variations in forest height.

Erosion is a powerful force that has moulded the Earth ever since water has been present on its rocky surface. In its seemingly harmless pursuit, erosion threatens ecosystems, reduces agricultural production, and impacts water quality. When trying to investigate erosion, there is no easy way to identify hotspots, only leaving the possibility of predicting where erosion should be occurring. This study aimed to develop a method to identify erosion using Synthetic Aperture Radar (SAR) images in a process called Coherent Change Detection (CCD). In doing so, it was found that CCD can be used to identify erosion due to rain events; however, false positives were also found due to soil moisture changes. This study used a new method for removing soil moisture effects that utilised the drying out of the soil to map where changes had occurred. This helped limit false positives, but more work is required to ensure soil moisture does not interfere with the results. Field data comprising aerial imagery and soil sampling were collected to improve the SAR processing as well as validate the results. The results of this study indicate the feasibility of developing an erosion analysis system capable of providing near real-time data specifically for arid regions. [ABSTRACT FROM AUTHOR]

The article discusses the Jindalee Operational Radar Network (JORN) technology has beginnings in ionospheric research conducted at Weapons Research Establishment from the 1950s. Topics include the first experimental radar, designed and built by Defence Science and Technology Organisation (DSTO), has constructed at Alice Springs in central Australia; and Commonwealth Cabinet approved a submission from the Minister for Defence.

Knowledge of species' population trends is crucial when planning for conservation and management; however, this information can be difficult to obtain for extremely mobile species such as flying-foxes (Pteropus spp.; Chiroptera, Pteropodidae). In mainland Australia, flying-foxes are of particular management concern due their involvement in human-wildlife conflict, and their role as vectors of zoonotic diseases; and two species, the grey-headed flying-fox (Pteropus poliocephalus) and the spectacled flying-fox (P. conspicillatus), are currently threatened with extinction. Here we demonstrate that archival weather radar data over a period of ten years can be used to monitor a large colony of grey-headed flying-foxes near Melbourne. We show that radar estimates of colony size closely match those derived from traditional counting methods. Moreover, we show that radar data can be used to determine the timing and departure direction of flying-foxes emerging from the roost. Finally, we show that radar observations of flying-foxes can be used to identify signals of important ecological events, such as mass flowering and extreme heat events, and can inform human activities, e.g. the safe operation of airports and windfarms. As such, radar represents an extremely promising tool for the conservation and management of vulnerable flying-fox populations and for managing human interactions with these ecologically-important mammals.

A key part of native forest management in designated wood production areas is identifying locations which must be exempt from logging. Forest laws, government regulations, and codes of practice specify where logging is and is not permitted. Assessing compliance with these regulations is critical but can be expensive and time consuming, especially if it entails field measurements. In some cases, spatial data products may help reduce the costs and increase the transparency of assessing compliance. However, different spatial products can vary in their accuracy and resolution, leading to uncertainty in forest management. We present the results of a detailed case study investigating the compliance of logging operations with laws preventing cutting on slopes exceeding 30°. We focused on two designated water catchments in the Australian State of Victoria which supply water to the city of Melbourne. We compared slopes that had been logged on steep terrain using spatial data based on a Digital Elevation Model (DEM) derived from LiDAR, a 1 arc second DEM derived from the Shuttle Radar Topography Mission, and a Digital Terrain Model (DTM) with a resolution of 10m. While our analyses revealed differences in slope measurements among the different spatial products, all three datasets (and the on-site slope measurements) estimated the occurrence of widespread logging of forests on slopes >30° in both water catchments. We found the lowest resolution Shuttle Radar Topography Mission DEM underestimated the steepness of slopes, whilst the DTM was variable in its estimates. As expected, the LiDAR generated slope calculations provided the best fit with on-site measurements. Our study demonstrates the value of spatial data products in assessing compliance with logging laws and codes of practice. We suggest that LiDAR DEMs, and DTMs also can be useful in proactive forest planning and management by helping better identify which areas should be exempt from cutting before logging operations commence., Competing Interests: The authors have declared that no competing interests exist.