Background: Molecular markers of insecticide resistance can provide sensitive indicators of resistance development in malaria vector populations. Monitoring of insecticide resistance in vector populations is an important component of current malaria control programmes. Knockdown resistance (kdr) confers resistance to the pyrethroid class of insecticides with cross-resistance to DDT through single nucleotide polymorphisms (SNPs) in the voltage-gated sodium channel gene.

The aim of this study is to examine the statistics of convective storms and their concomitant changes with thermodynamic variability. The thermodynamic variability is analyzed by performing a cluster analysis on variables derived from radiosonde releases at Brisbane Airport in Australia. Three objectively defined regimes are found: a dry, stable regime with mainly westerly surface winds, a moist northerly regime, and a moist trade wind regime. S-band radar data are analyzed and storms are identified using objective tracking software [Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN)]. Storm statistics are then investigated, stratified by the regime subperiods. Convective storms are found to form and maintain along elevated topography. Probability distributions of convective storm size and rain rate are found to follow lognormal distributions with differing mean and variance among the regimes. There was some evidence of trimodal storm-top heights, located at the trade inversion (1.5-2 km), freezing level (3.6-4 km), and near 6 km, but it was dependent on the presence of the trade inversion. On average, storm volume and height are smallest in the trade regime and rain rate is largest in the westerly regime. However, westerly regime storms occur less frequently and have shorter lifetimes, which were attributed to the enhanced stability and decreased humidity profiles. Furthermore, time series of diurnal rain rate exhibited early morning and midafternoon maxima for the northerly and trade regimes but were absent for the westerly regime. The observations indicate that westerly regime storms are primarily driven by large-scale forcing, whereas northerly and trade wind regime storms are more responsive to surface characteristics.

The Australian Bureau of Meteorology's operational weather radar network comprises a heterogeneous radar collection covering diverse geography and climate. A naïve Bayes classifier has been developed to identify a range of common echo types observed with these radars. The success of the classifier has been evaluated against its training dataset and by routine monitoring. The training data indicate that more than 90% of precipitation may be identified correctly. The echo types most difficult to distinguish from rainfall are smoke, chaff, and anomalous propagation ground and sea clutter. Their impact depends on their climatological frequency. Small quantities of frequently misclassified persistent echo (like permanent ground clutter or insects) can also cause quality control issues. The Bayes classifier is demonstrated to perform better than a simple threshold method, particularly for reducing misclassification of clutter as precipitation. However, the result depends on finding a balance between excluding precipitation and including erroneous echo. Unlike many single-polarization classifiers that are only intended to extract precipitation echo, the Bayes classifier also discriminates types of nonprecipitation echo. Therefore, the classifier provides the means to utilize clear air echo for applications like data assimilation, and the class information will permit separate data handling of different echo types.

An innovative approach to studying the effects of cloud seeding on precipitation is to focus on understanding the natural variability of precipitation and the microphysical responses to aerosol.

During the late afternoon on 16 November 2008 the Brisbane (Queensland, Australia) suburb of ''The Gap'' experienced extensive wind damage caused by an intense local thunderstorm. The CP2 research radar nearby detected near-surface radial velocities exceeding 43 m s 21 above The Gap while hail size reports did not exceed golf ball size, and no tornadoes were reported. The storm environment was characterized by a layer of very moist near-surface air and strong storm-relative low-level flow, whereas the storm-relative winds aloft were weak. While the thermodynamic storm environment contained a range of downdraft-promoting ingredients such as a ;4-km-high melting level above a ;2-km-deep layer with nearly dry-adiabatic lapse rates mostly collocated with dry ambient air, a ;1-km-deep stable layer near the ground would generally lower expectations of destructive surface winds based on the downburst mechanism. Once observed reflectivities exceed 70 dBZ, downdraft cooling due to hail melting and downdraft acceleration based on hail loading are found to likely become nonnegligible forcing mechanisms. The event featured the close proximity of a hydrostatically and dynamically driven mesohigh at the base of the downdraft to a dynamically driven mesolow associated with a low-level circulation. This proximity was instrumental in the anisotropic horizontal acceleration of the nearground outflow and the ultimate strength of the Gap storm surface winds. Weak storm-relative midlevel winds are speculated to have allowed the downdraft to descend close to the low-level circulation, which set up this strong horizontal perturbation pressure gradient.

Observations of aerosol-particle (AP) composition obtained via volatility measurements during the 'Rain In Cumulus over the Ocean' (RICO) experiment are presented. The measurements show that sub-cloud APs with a radius r p ≤ 0.2 µm are composed mainly of ammonium sulphate, while APs with a radius r p ≥ 0.2 µm are composed mainly of sea salt. The AP distribution also contains an indeterminate non-volatile component (r p ≤ 0.4 µm), which could be either elemental carbon or mineral dust.

A naïve Bayes classifier (NBC) was developed to distinguish precipitation echoes from anomalous propagation (anaprop). The NBC is an application of Bayes's theorem, which makes its classification decision based on the class with the maximum a posteriori probability. Several feature fields were input to the Bayes classifier: texture of reflectivity (TDBZ), a measure of the reflectivity fluctuations (SPIN), and vertical profile of reflectivity (VPDBZ). Prior conditional probability distribution functions (PDFs) of the feature fields were constructed from training sets for several meteorological scenarios and for anaprop. A Box-Cox transform was applied to transform these PDFs to approximate Gaussian distributions, which enabled efficient numerical computation as they could be specified completely by their mean and standard deviation. Combinations of the feature fields were tested on the training datasets to evaluate the best combination for discriminating anaprop and precipitation, which was found to be TDBZ and VPDBZ. The NBC was applied to a case of convective rain embedded in anaprop and found to be effective at distinguishing the echoes. Furthermore, despite having been trained with data from a single radar, the NBC was successful at distinguishing precipitation and anaprop from two nearby radars with differing wavelength and beamwidth characteristics. The NBC was extended to implement a strength of classification index that provides a metric to quantify the confidence with which data have been classified as precipitation and, consequently, a method to censor data for assimilation or quantitative precipitation estimation.

Severe hailstorms represent some of Australia's most costly natural disasters. In particular, the densely populated east‐coast cities of Brisbane and Sydney have both been impacted by events that individually produced over AU$1 billion in insured losses. In this study, the frequency of damaging hailstorms in these regions is assessed over an eight‐year period using single‐polarisation radar observations. Reflectivity data from multiple radars are carefully calibrated and merged to produce daily grids of MESH (the maximum expected size of hail) at 1 km spatial resolution. Neighbourhood contingency tables are then used to define an optimal MESH threshold for identifying damaging hail events. This analysis makes use of a unique insurance dataset, comprising building‐scale exposure and claim data over a multi‐year period. Overall, damaging hailstorms occur on an average of 26 and 32 days per year in the Brisbane and Sydney regions, respectively. The actual cities of Brisbane and Sydney both experience about five damaging hailstorms annually. In both regions, the highest hail frequencies occur along the coastal slopes of the Great Dividing Range, with more localised maxima highlighting the potential role of mesoscale circulations in storm initiation and intensification. For Brisbane, the seasonal cycle shows a pronounced peak in December, while in Sydney hail frequencies are high throughout the warm season (November–March). This difference reflects the distinct seasonal cycles of convective instability in the two regions. Consistent with previous studies, the combination of CAPE and deep‐layer shear is found to provide reasonable discrimination of environments conducive to severe hailstorms.