ORCID Profile
0000-0002-6054-4873
Current Organisation
Australian Bureau of Meteorology
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Publisher: Wiley
Date: 04-2010
DOI: 10.1002/QJ.515
Publisher: American Meteorological Society
Date: 09-2014
Abstract: 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 −1 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 dB Z , 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 near-ground 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.
Publisher: American Meteorological Society
Date: 2012
Publisher: Copernicus GmbH
Date: 03-2023
DOI: 10.5194/GMD-2023-7
Abstract: Abstract. The Australian Bureau of Meteorology has developed a national hydrological projections (NHP) service for Australia. With the focus on hydrological change assessment, the NHP service aims at being complementary to climate projections work carried out by many federal and state governments, universities, and other organisations across Australia. The projections comprise an ensemble of application-ready bias-corrected climate model data and derived hydrological projections at daily temporal and 0.05° × 0.05° spatial resolution for the period 1960–2099 and two emission scenarios (RCP 4.5 and RCP 8.5). The spatial resolution of the projections matches that of gridded historical reference data used to perform the bias correction and the Bureau's operational gridded hydrological model. Three bias correction techniques were applied to four CMIP5 global climate models (GCMs) and one to output from a regional climate model forced by the same four GCMs, resulting in a 16-member ensemble of bias-corrected GCM data for each emission scenario. The bias correction was applied to fields of precipitation, minimum and maximum temperature, downwelling shortwave radiation and surface winds. These variables are required inputs to the Bureau's landscape water balance hydrological model (AWRA-L) which was forced using the bias-corrected GCM and RCM data to produce a 16-member ensemble of hydrological output. The hydrological output variables include root-zone soil moisture (moisture in the top 1 m soil layer), potential evapotranspiration and runoff. Here we present an overview of the production of the hydrological projections, including GCM selection, bias correction methods and their evaluation, technical aspects of their implementation and ex les of analysis performed to construct the NHP service. The data are publicly available on the National Computing Infrastructure (0.25914/6130680dc5a51) and a user interface is accessible at awo.bom.gov.au roducts rojection/.
Publisher: American Meteorological Society
Date: 09-2013
DOI: 10.1175/JTECH-D-12-00082.1
Abstract: 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.
Publisher: American Meteorological Society
Date: 07-2015
DOI: 10.1175/JTECH-D-14-00206.1
Abstract: The Australian Bureau of Meteorology’s operational weather radar network comprises a heterogeneous radar collection covering erse 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.
Publisher: Wiley
Date: 04-2008
DOI: 10.1002/QJ.198
Publisher: American Meteorological Society
Date: 10-2015
Abstract: 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.
Publisher: Wiley
Date: 04-2006
DOI: 10.1256/QJ.05.106
Publisher: Elsevier BV
Date: 07-2023
Publisher: Wiley
Date: 2020
DOI: 10.1002/QJ.3693
Location: United Kingdom of Great Britain and Northern Ireland
Location: United States of America
Location: Australia
No related grants have been discovered for Justin Peter.