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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]

Understanding bushfire–atmosphere interactions is essential for accurate prediction of fire behaviour, and for the safe and effective strategic management of fires to mitigate risk to people and property. Bushfires with feedbacks to thunderstorms represent the most extreme form of fire–atmosphere interaction, with potential to initiate tornadoes, lightning and hazardous winds causing dangerous fire behaviour and new ignitions many kilometres from the fire front. However, there is very little evidence that links quantitative fire behaviour with observed thunderstorm dynamics. Here we combine stochastic modelling of fire behaviour with satellite and mobile weather radar data of a bushfire thunderstorm in Queensland, Australia. The results show the coupling between fire behaviour and thunderstorm development in a conditionally unstable atmosphere. The process by which the coupling occurs raises questions as to the cause and effect relationship of the bushfire-initiated thunderstorms and associated fire behaviour. Recommendations for future research are made, highlighting the need for understanding links between modelled and observed fire behaviour dynamics and atmospheric thermodynamics. Bushfires can support the evolution of thunderstorms. Here, we combine a variety of observations of a bushfire thunderstorm with ensemble fire spread modelling and fire severity mapping. Results show the coupling between the intensity of the fire and its burning through forest canopy with thunderstorm updrafts. [ABSTRACT FROM AUTHOR]

For many areas, satellite-based precipitation products or reanalysis model data represent the only available precipitation information. Unfortunately, the resolution of these datasets is generally too coarse for many applications. A very promising downscaling approach is to use soil moisture due to its clear physical connection to precipitation. We investigate the feasibility of using soil moisture derived from land surface temperature in this context. These data are more widely available in the required resolution compared to other soil moisture data. Rain gauge-adjusted radar data from Namoi serves as a spatial reference dataset for two objectives: to identify the most suitable globally available precipitation dataset and to explore the precipitation information contained in the soil moisture data. The results show that these soil moisture data cannot be used to downscale satellite-based precipitation data to a high resolution because of cloud cover interference. Therefore, the Integrated Multi-satellitE Retrievals for GPM (IMERG) late data represents the best precipitation dataset for many areas in Australia that require timely precipitation information, according to this study. [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]

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 (GLCM), 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]

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 (GLCM), 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]

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]

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]

Over-the-horizon radars (OTHRs) utilize the refractive properties of the ionosphere to illuminate targets beyond the Earth’s horizon. Consequently, the performance of this type of radar is highly dependent on the ionosphere. Reliable models of the radar ground backscatter are required to accurately assess the ionospheric propagation conditions and, thus, the expected performance of OTHRs. The ground backscatter coefficient characterizes the amount of radiation scattered back from a surface toward a receiver per unit area. While the backscatter coefficient of the sea is well understood and may be calculated from theory if the sea state is known, the backscatter coefficient of land at high frequencies is not well understood. To calculate the land backscatter coefficients over Northern Australia, a methodology that compares observed backscatter ionograms to those synthesized using high-frequency (HF) radio wave ray-tracing techniques through model ionospheres was developed. Maps of the backscatter coefficients across Northern Australia were produced. The effects of surface properties, including topography, soil moisture, and vegetation cover on the backscatter coefficients, were investigated. A weak positive correlation between the backscatter coefficient and the soil moisture and surface roughness was observed; however, it was found that the vegetation had the largest effect on the backscatter coefficient. [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]

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 its 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 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 0.9. Moreover, taking radar measurements as reference data set, Mie-cloudy winds are shown to be more precise than Rayleigh-clear winds with an overall random error of 5.81 m s-1 for Rayleigh-clear winds and 4.14 m s-1 for Mie-cloudy winds. Similar results were also found from triple collocation analysis, with error standard deviations of 5.61 m s-1 and 3.50 m s-1 for Rayleigh-clear winds and Mie-cloudy winds, respectively. In addition, the Mie channel is shown to be better capable of capturing the wind in the planetary boundary layer (< 1,500 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]

Linear precipitation systems are a prominent contributor to rainfall over Melbourne, Australia, and the surrounding region. These systems are often convective in nature, frequently associated with cold fronts, and in some cases can lead to significant rainfall and flash flooding. Various types of linearly organized systems (e.g., squall lines, quasi-linear convective systems) have been the subject of much research in the United States and elsewhere, but thus far relatively little analysis has been done on linear systems in Australia. To begin to understand rainfall extremes and how they may change in this region in the future, it is useful to explore the contribution of these types of systems and the characteristics that define them. To this end, we have examined the recently developed Australian Radar Archive (AURA), identifying objects that meet a specific set of relevant criteria, and used multiple methods to identify heavy and extreme daily rainfall. We found that on average, days with linear systems contribute over half of the total rainfall and 70%–85% of heavy/extreme rainfall in the Melbourne region. The linear systems that occur on heavy rainfall days tend to be larger, slower-moving, and longer-lived, while those on extreme rainfall days also tend to be more intense and have a greater degree of southward propagation than linear systems on other days. [ABSTRACT FROM AUTHOR]

There is a growing need to better understand and quantify risks associated with extreme weather, including severe thunderstorm‐related hazards such as hail and lightning. Hail occurrence based on a long‐term archive of radar observations is presented for the first time in many temperate and subtropical regions of Australia, together with lightning observations from a ground‐based network of sensors. Mean monthly and hourly occurrence frequencies are examined for hail and lightning. Environmental conditions obtained from hourly reanalysis data indicate stronger wind shear on average for hail than lightning. The environmental conditions also indicate higher freezing levels on average for lightning than hail. These environmental differences provide plausible physical reasons for observed differences between hail and lightning climatology through the year. The study results are intended to help inform future planning and preparedness for thunderstorm‐related risks, including for severe weather forecasting and climate risk applications. Plain Language Summary: Natural hazards caused by thunderstorms, such as hail and lightning, can have severe impacts on society. Radar data are used to examine hail events at 10 Australian locations, and comparisons are made with lightning observations. Results are averaged over several years to calculate average annual values, including for the hourly and the monthly number of the hail and the lightning events. We find differences between hail and lightning events in the environmental conditions that they occur in. These environmental differences are found to help explain differences through the year in the average risk of occurrence of hail or lightning. The results are intended to enhance resilience in relation to thunderstorm‐related risks and provide guidance for extreme weather modeling and climate adaptation purposes. Key Points: Occurrence frequencies of hail and lightning are presented based on observations for a wide range of regions around AustraliaEnvironmental conditions are found to provide insight on spatiotemporal variations in hail and lightning activityEnvironments associated with individual hazard types are recommended for use in improved preparedness for thunderstorm‐related damages [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.

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]

Interpolation of ground-based radar measurements is required when mapping data from their native spherical coordinates to a Cartesian grid. For reflectivity the question arises as to whether this processing should be performed in units of Z (mm6 m−3) or dBZ. This study addresses this question using one year of data from three radars, operating in diverse climates across Australia. For each radar, a subset of 800 volume scans is processed to identify "triads"—groups of three consecutive gates with valid data—in each of the three coordinate directions: range, azimuth, and elevation. For every triad, the reflectivity at the central gate is estimated by linearly interpolating between the outer two gates in both Z and dBZ. The resulting values are then compared with the true reflectivity at the central gate to quantify the interpolation errors. For all three sites and in all three coordinate directions, we find that interpolation in Z is more accurate on average, especially in regions of high reflectivity and strong reflectivity gradient (i.e., convective cores). However, interpolation in dBZ is better in regions of low and monotonically increasing/decreasing reflectivity. It is therefore recommended that reflectivities be converted from dBZ to Z prior to interpolation except when identifying echo-top height or other low-reflectivity boundaries. [ABSTRACT FROM AUTHOR]

This paper reviews some existing and potential applications of high frequency (HF) radar surface current, wave and wind data drawing on experiences within Australia and also from elsewhere in the world. These include oil spill transport, search and rescue services, navigational safety, marine renewables, tsunami detection, water quality and coastal development projects, and many different applications in the area of marine ecology. The data are also used for studies of ocean dynamics and for the validation of hydrodynamic and wave models. Some new wind direction and wave measurements from the Australian radar systems are presented. The quality of the data is crucial to all these applications and some examples of data validation are discussed. Learning from the experiences of other more developed networks, and in particular building solid links with the user communities in Australia, is essential to ensure long-term sustainability of the Australian HF radar network. [ABSTRACT FROM AUTHOR]

The charge distributions in a thundercloud play an important role in the initiation and propagation of lightning discharges. To further understand the effects of charge distributions on lightning discharge, the authors conducted a very high-frequency (VHF) lightning observation campaign during the 2006/07 monsoon in Darwin, Australia, using a VHF broadband digital interferometer (DITF). A C-band polarimetric weather radar to estimate the precipitation profiles such as hydrometeor classification was operated by the Bureau of Meteorology (BOM) Research Centre. Cloud-to-ground (CG) and intracloud (IC) flashes were initiated from the outer and the inner parts of the upper side of the graupel regions, respectively. In the cases of CG flashes, the negative leaders travel first about 10 km horizontally through positive charge regions and then begin to bend toward the ground when they reach the edge of the positive charge regions where there is no graupel region underneath. In contrast, in the cases of the IC flashes the negatively charged graupel regions block the downward developments of negative leaders. It is noted that positive charge regions could facilitate the extension of the horizontal negative leader. These results may suggest that lightning flash types are closely dependent on their initiation locations and the surrounding charge distributions. The experimental results are consistent with other previous observation results and charge model simulations. [ABSTRACT FROM AUTHOR]

Australia has conducted research into over-the-horizon radar (OTHR) for almost sixty years. Early programs focused exclusively on military capabilities, such as the detection of aircraft, missiles and ships, but 1974 a team was formed to design a new OTIfR for which a remote sensing mission was proposed. Oceanic wind mapping experiments in 1977- 78 yielded promising results so remote sensing became a reeognised mission assigned to the new radar, known as Jindalee Stage B. This capability was progressively expanded over the period 1982-87 to include mapping of wave height and other oceanographic variables. A real-time data link to the Australian Bureau of Meteorology was set up in 1985, providing daily wind direction maps covering over one million square kilometres of the NW Indian Ocean, the Timor Sea and the Arafura Sea. In concert with user-focused remote sensing programs, investigations were undertaken to gain a detailed understanding of issues relating to propagation, system calibration, radar resource management and scheduling. Many theoretical studies and radar experiments were conducted to assess the radar's ability to measure ever more ambitious geophysical phenomena, ranging from oil spills and tropical rainfall to cyclogenesis and volcanic activity. Inevitably, as the radar evolved into a facility owned and operated by the Royal Australian Air" Force, the remote sensing mission had to compete with surveillance tasks with higher priorities and the quality of the routine service provided to the Bureau of Meteorology deteriorated, eventually being terminated after" a decade of activity. Despite this, the knowledge acquired and the practical lessons learned remain valid today and may be of value to other OTHR programs. [ABSTRACT FROM AUTHOR]

Robust maritime surveillance with radar requires an accurate description of the backscatter from the sea. An estimated probability distribution of the backscatter is commonly used to determine the threshold for separating targets from clutter at a given false alarm rate. Data collected at medium to high grazing angles by the Defence Science Technology Organisation (DSTO) Ingara fully polarimetric X-band radar demonstrate that the commonly used K-distribution is not always adequate for modelling the probability distribution. This is especially the case for the horizontal polarisation and in regions of high backscatter where target detection can be a problem. An alternative proposed as a more accurate model in this region is known as the KK-distribution. The analysis presented in this study describes this model with the addition of multiple looks and a thermal noise component to produce greater accuracy in the mean and underlying shape. The threshold required to achieve a constant false alarm rate is then studied and compared with the K-distribution. [ABSTRACT FROM AUTHOR]

A clustering algorithm was applied to Frequency with Altitude Diagrams (FADs) derived from 4 yr of hourly radar data to objectively define four tropical precipitation regimes that occur during the wet season over Darwin Australia. The precipitation regimes defined are distinguished in terms of convective intensity, presence of stratiform precipitation, and precipitation coverage. Regime 1 consists of patchy convection of medium intensity and low area coverage, and regime 2 contains strong convection with relatively small area coverage. Regime 3 is composed of weak convection with large area coverage and large stratiform regions, and regime 4 contains strong convection with large area coverage and large stratiform regions. Analysis of the seasonal cycle, diurnal cycle, and regime occurrence as a function of monsoon activity all provide insight into the different physical character of the precipitation regimes. Two of the regimes exhibit a diurnal cycle with a peak in the afternoon, while the other two show a peak in their frequency of occurrence in the early morning. The different character of the regimes is also confirmed by the varying contributions that convective and stratiform rainfall make to the overall within-regime precipitation. [ABSTRACT FROM AUTHOR]

The detection and mapping of canopy disturbance caused by natural events such as cyclones in tropical rainforests is important for monitoring and understanding rainforest dynamics and recovery. In February 1999 tropical cyclone Rona crossed the coastline of Far North Queensland, Australia, causing significant damage to the rainforest canopy in some areas. This paper examines the application of multitemporal canopy digital elevation models (DEMs) generated by a NASA-operated airborne radar mapping system called TOPSAR in 1996 and 2000, for detection of canopy disturbance caused by cyclone damage. Canopy damage was mapped here by identifying areas with a significant decrease in canopy height estimated from the difference of the 1996 and 2000 TOPSAR DEMs. Conventional aerial photographs, flown shortly after the cyclone (March 1999), were used to validate the resulting map of cyclone damaged rainforest canopy. The results showed that the DEM-derived canopy damage map performed reasonably well when comparing the spatial distribution and size of damaged areas, while taking into account the time difference between cyclone damage and the second radar acquisition. Some errors were encountered on the steeper slopes which were related to terrain distortions inherent in radar images of steep terrain. The height accuracy of the DEM was close to the depth of some of the gaps being mapped, which also contributed to errors. Even so, the results demonstrate there may be potential for weather-independent, regional-scale mapping of forest canopy change from imaging radar that is not always possible from traditional optical means of measuring canopy elevation, for example, airborne laser data and stereo aerial photography. This may be possible in relatively flat areas, provided the second radar acquisition occurs within months of the damaging event. Further algorithm refinement is required to improve its robustness and the range of topographies where this approach can provide reliable estimates of the extent of canopy disturbance. [ABSTRACT FROM AUTHOR]

Polarimetric radar data have been used to produce microphysical classifications. This kind of analysis is run in a real-time mode from several research radars, including the C-band polarimetric (C-Pol) radar in Darwin, Australia. However, these classifications have had very little systematic evaluation with independent data. Using surface data is often difficult because of sampling issues, particularly for hail. The approach taken here is to use a combination of 50- and 920-MHz wind profiler estimates of rain and hail to provide validation data for the radar pixels over the profiler. The profilers also observe signals associated with lightning, and some comparisons are made between lightning occurrence and the radar measurements of graupel. The retrievals of hail–rain mixtures are remarkably robust; there are some issues regarding other microphysical classes, however, including difficulties in detecting melting snow layers in stratiform rain. These difficulties are largely due to the resampling of the radar volume data onto a grid and to poor separation of the snow classes. [ABSTRACT FROM AUTHOR]

The potential of using synthetic aperture radar (SAR) imagery to study seasonal variations in floodplain inundation and accompanying changes in herbaceous plant communities was assessed for aquatic environments in the Kakadu region of northern Australia. Dual wavelength (C- and L-band), co- and cross-polarized (HH and HV) SAR imagery of the floodplain of Magela Creek, a tributary of the East Alligator River, was acquired in April and October 1994 during the Spaceborne Imaging Radar-C (SIR-C) missions of the Space Shuttle. The results indicate the effectiveness of multifrequency, polarimetric SAR as follows: woody and herbaceous vegetation in flooded and nonflooded states can be mapped. Aquatic, herbaceous macrophyte communities, such as Nelumbo nucifera, Pseudoraphis grassland and Hymenachne-Eleocharis swamp, can be distinguished. Phenologic changes in macrophyte communities between April and October are apparent. The similarity of the Magela floodplain to the extensive floodplains in northern Australia suggests that these results can be extended regionally. [ABSTRACT FROM AUTHOR]

It is broadly acknowledged that the precision of satellite-altimeter-measured instantaneous sea surface heights (SSH) is lower in coastal regions than in open oceans, due partly to contamination of the radar return from the coastal sea-surface state and from land topography. This study investigates the behavior of ERS-2 and POSEIDON altimeter waveform data in coastal regions and estimates a boundary around Australia's coasts in which the altimeter range may be poorly estimated by on-satellite tracking software. Over one million 20 Hz ERS-2 (March to April 1999) and POSEIDON (January 1998 to January 1999) radar altimeter waveform data were used over an area extending 350 km offshore Australia. The DS759.2 (5′resolution) ocean depth model and the GSHHS (0.2 km resolution) shoreline model were used together to define the coastal regions. Using the 50% threshold retracking points as the estimates of expected tracking gate, we determined that the sea surface height is contaminated out to maximum distance of between about 8 km and 22 km from the Australian shoreline for ERS-2, depending partly on coastal topography. Using the standard deviation of the mean waveforms as an indication of the general variability of the altimeter returns in the Australian coastal region shows obvious coastal contamination out to about 4 km for both altimeters, and less obvious contamination out to about 8 km for POSEIDON and 10 km for ERS-2. Therefore, ERS-2 and POSEIDON satellite altimeter data should be treated with some caution for distances less than about 22 km from the Australian coast and probably ignored altogether for distances less than 4 km. [ABSTRACT FROM AUTHOR]

Rain gauges continue to be sources of rainfall data despite progress made in precipitation measurements using radar and satellite technology. There has been some work done on assessing the optimum rain gauge network density required for hydrological modelling, but without consensus. This paper contributes to the identification of the optimum rain gauge network density, using scaling laws and bias-corrected 1 km × 1 km grid radar rainfall records, covering an area of 28,371 km2 that hosts 315 rain gauges in south-east Queensland, Australia. Varying numbers of radar pixels (rain gauges) were repeatedly sampled using a unique stratified sampling technique. For each set of rainfall sampled data, a two-dimensional correlogram was developed from the normal scores obtained through quantile-quantile transformation for ordinary kriging which is a stochastic interpolation. Leave-one-out cross validation was carried out, and the simulated quantiles were evaluated using the performance statistics of root-mean-square-error and mean-absolute-bias, as well as their rates of change. A break in the scaling of the plots of these performance statistics against the number of rain gauges was used to infer the optimum rain gauge network density. The optimum rain gauge network density varied from 14 km2/gauge to 38 km2/gauge, with an average of 25 km2/gauge. [ABSTRACT FROM AUTHOR]

Autonomously-operating radars employing the 'ZLC configuration' have been providing long-term datasets of insect flight activity to heights of about 1 km since the late 1990s. A unit of this type operating in Australia has recently received a major upgrade. The aim of the project was to maximize the utility of the radar to entomologists and aeroecologists by providing larger and more continuous datasets and extending observations to 2.5 km. The upgrade was achieved primarily by incorporating modern digital technology, which provides much improved data-acquisition-and-control performance and data-archiving capacity; by implementing a more comprehensive observing protocol; and by replacing fixed electronic signal-acquisition gates with specially developed software that identifies insect echoes and applies a narrow moving gate that follows them. The upgraded version provides an approximately five-fold increase in hourly sample sizes, a doubling of the duration of observations (from 12 to 24 h per day) and a doubling of the height range over which observations are made. The design considerations (incentives and constraints) that informed the various subsystem implementations are identified, and the necessary compromises are discussed. Observations of the development of a layer echo during a migration by two different insect types are presented as a demonstration of the upgraded unit's capabilities. [ABSTRACT FROM AUTHOR]

The International Telecommunication Union (ITU) Resolution 612, in combination with Report ITU-R M2.234 (11/2011) and Recommendation ITU-R M.1874-1 (02/2013), regulates the use of the radiolocation services between 3 and 50 MHz to support high frequency oceanographic radar (HFR) operations. The operational frame for HFR systems include: band sharing capabilities, such as synchronization of the signal modulation; pulse shaping and multiple levels of filtering, to reduce out-of-band interferences; low radiated power; directional transmission antenna, to reduce emission over land. Resolution 612 also aims at reducing the use of spectral bands, either through the application of existing band-sharing capabilities, the reduction of the spectral leakage to neighboring frequency bands, or the development and implementation of listen-before-talk (LBT) capabilities. While the LBT mode is operational and commonly used at several phased-array HFR installations, the implementation to commercial direction-finding systems does not appear to be available yet. In this paper, a proof-of-concept is provided for the implementation of the LBT mode for commercial SeaSonde HFRs deployed in Australia, with potential for applications in other networks and installations elsewhere. Potential critical aspects for systems operated under this configuration are also pointed out. Both the receiver and the transmitter antennas may lose efficiency if the frequency offset from the resonant frequency or calibration pattern are too large. Radial resolution clearly degrades when a dynamical adaptation of the bandwidth is performed, which results in non-homogeneous spatial resolution and reduction of the quality of the data. A recommendation would be to perform the LBT-adapt scans after a full measurement cycle (1-h or 3-h, depending on the system configuration) is concluded. Mutual cross-interference from clock offsets between two HFR systems may bias the frequency scans when the site computers controlling data acquisitions are not properly time-synchronized. [ABSTRACT FROM AUTHOR]

The article presents the information on the installation of new anti-ship missile defence radar system on the Anzac-class frigate HMAS Perth by the Royal Australian Navy's for initial operational use after success in the trials. It states that the radar identify, track and guide the missiles to multiple targets at the same time. It informs that the system can defend the ship from modern cruise missile attack and is a significant enhancement for the Anzac-class frigate.

The article presents information on 2004 Antennas and Propagation Society's" Distinguished Achievement Award". The Distinguished Achievement Award was established in 1985 to recognize outstanding technical achievement and meritorious service to the Antennas and Propagation Society. The 2004 Award was presented to Allan W. Love for his many contributions in the field of antennas, and particularly for his creative development of the line source feed for the Arecibo spherical reflector antenna. He served as a radar officer in the Royal Air Force from 1941 to 1945 in the Middle East and Australia. He served the Antennas and Propagation Society for many years in a number of capacities, including the Presidency in 1984.